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Asset Reliability

Decoding plant reliability in manufacturing and a process to reach there.

Decoding plant reliability in manufacturing and a process to reach there.

Process manufacturers typically operate in data-rich environments and know their plants inside out. While they know their assets and how their resources are deployed, they are often unaware of factors contributing to optimal asset performance. Even if this information exists within the manufacturing ecosystem, the plant maintenance and operational heads don’t really know how to use it to achieve optimum plant productivity. 

Studies reveal that frequent downtimes at process manufacturing plants can result in nearly $15000 per hour of lost revenue. Digitalization of the maintenance process and proactive asset performance management directly contribute to saving this cost. Prioritizing plant reliability becomes the only way to improve overall operations and mitigate unplanned downtimes. 

But what is plant reliability, and what processes need to be institutionalized to achieve it? In this article, we will discuss what reliability means for manufacturing and lay out a six-step process to devise a plant reliability strategy for a manufacturing plant effectively. 

What is plant reliability, and how can you measure it?

Any reliable system accounts for its safety and trustworthiness while ensuring minimal maintenance costs. For plant assets, reliability can depend on performance, condition, maintenance needs, and availability. An asset reliability check can be done based on factors like frequency of maintenance or repair costs, number of malfunctions, unexpected downtimes, and more.

Keeping plant’s maintenance up to the mark ensures that assets run 24/7 with fewer interruptions or unexpected delays due to frequent maintenance incidents. This leads to faster go-to-market, better output quality, employee productivity, and significantly lower operational costs or costs per unit.
Plant reliability in production can be quantitatively measured using the Overall Equipment Effectiveness (OEE). A popular metric for measuring manufacturing productivity, OEE factors in a product of availability (number of downtimes/uptime), performance (speed or run time of your processes), and quality (number of defects).
An OEE score of 100% indicates a completely reliable, dependable, and high-quality plant with maximum productivity. Therefore, calculating OEE and securing a top score should be part of your asset management’s best practices.
Six steps to creating an effective reliability plan at your plant.
Every successful plan consists of a set of clear and executable steps. And the same principle applies to achieving top-notch plant reliability as well. Without a clear, planned route, it can be hard for you to envision your end goal – optimum plant and asset reliability management. Here are the six actionable steps that are essential to executing your plan successfully:
1. Building the right team.

The right team can make or break reliability goals- from top to bottom.

Effective leadership, skilled personnel, and onsite-plant operations team must be aligned with accomplishing plant reliability goals.

Achieving reliability is a team effort and a continuous improvement process. Designated team champions have to be distributed within Operations, Maintenance, and Engineering along with sufficient alignment around their common goals & individual targets. This way, every individual is well aware of their role in constantly improving the plant and understands their dependencies on the other teams. There has to be also a Reliability Leader who helps drive this initiative

2. Creating the right mindset for reliability

For a successful plan, having the right mindset for asset reliability is as important as relevant skills, processes & technical understanding.

Since achieving reliability requires continuous effort, you can try to define your target numerically & align every department’s target accordingly. This target & its deadline needs to be agreed upon by each department-operations, maintenance, engineering, and the subsequent KPIs that befall them individually.

It is critical that all teams uphold this goal as their guiding principle and implement it through individual responsibilities every day.

 3. Adapting Predictive Maintenance (PdM) approach

Plant reliability is also heavily dependent on asset health & reliability. The approach towards asset reliability is centered around the plant maintenance methodology chosen.

An advanced framework like predictive maintenance alongside numerous assets and operations can speed up the process of obtaining plant reliability. By proactively anticipating flaws or anomalies within the plant and addressing them, reliability objectives can be progressively achieved.

And when you proactively work towards fixing them, you can see your maintenance costs and the dreaded plant downtimes plummet instantly. Also, by understanding what caused these failures, your teams can work towards optimizing their maintenance strategy in the future.

 4. Having a best practices checklist for assured equipment reliability

It is not enough to be proactive at one time; it has to become a process for excellence in reliability. For this, rigorous observation of what worked needs to be executed..

Best practices for different plant segments and units can be documented for standardized records and accessibility. Mainly for equipment reliability which requires defined steps, having a list of executables becomes highly essential. These practices are initially set for a particular type of machine in a plant -like a gearbox which can be applied for gearboxes across multiple plants for the same organization to benefit from learnings.

This can help your team focus on improving reliability at the plant, bit by bit, and avoid recurring mistakes alongside employing PdM.

 5. Prioritizing critical assets first

Your plant might have critical equipment that causes the most impact – both financially and operationally when down, like a kiln in a cement plant.

So to reduce this unpredictable impact, you can prioritize these assets and implement a model like Predictive Maintenance to fix issues on priority beforehand. Predictive Maintenance can also improve equipment reliability as it works toward assuring asset performance and health around-the-clock through continuous evaluation.

 6. Assessing your Reliability plan’s progress

Using your goals & best practices checklist, perform regular audits to check for any shortcomings. Organize audits as frequently as monthly or quarterly, based on your process durations. Check if your charted reliability program progress is aligned with your monthly/quarterly goals.

Continuous improvement requires continuous learning too. Make a detailed implementation plan with clear-cut steps for each task for every department involved and skill improvement and tool usage training at periodic intervals. Skills like vibration monitoring.

This practice keeps your plant and teams’ performance in constant check. Also, assessing highlights hidden improvement areas that may be hindering your plant’s reliability.

 Conclusion:

A well-articulated plant reliability plan and set targets can be the driving force towards rapidly fulfilling your reliability goals. Since consistency is key to realizing this goal, it requires a combined proactive effort from leadership, stakeholders, and staff. 

At Infinite Uptime, we provide cutting-edge solutions to implement predictive maintenance programs, seamlessly improving your plant’s reliability. With plant reliability, we help manufacturers across the globe see faster results by significantly improving plant efficiency, fewer downtimes, and better quality using predictive maintenance.

Want to achieve faster plant reliability? Get in touch with us today to schedule a free demo!

Categories
Cement Industry

Why the move from condition monitoring to predictive maintenance is the next big thing in the cement industry

Why the move from condition monitoring to predictive maintenance is the next big thing in the cement industry

The increasing urbanization in the world has consistently put demand pressure on the cement industry. Consequently, the industry has streamlined its operations from time to time and focused on high-quality throughput. Fortune Insights report says the global cement market will grow from $326.80 billion in 2021 to $458.64 billion in 2028, a steep 5.1% globally. Keeping pace with the rising demand and changing market scenario, digital transformation in the cement industry for efficient operation and maintenance is an immediate requirement. 

While condition-based monitoring has seen wide adoption to support digital transformation initiatives in cement manufacturing, predictive maintenance is shaping to be the next big thing. With plant reliability objectives and operational excellence goals on the line, this shift must happen. In this article, we will compare both technologies and deliberate on why this evolution is necessary for cement manufacturers.

Condition-based Monitoring (CBM) in the Cement Industry.
In the cement industry, machinery works under challenging conditions- with fume, gases, dust, and high temperatures. The continuous nature of the cement manufacturing process also ensures that halts in production cannot be without a substantial reason. Thus, routine manual check-ups are sometimes impossible

Asset Maintenance in cement plants is today being practiced using condition monitoring technology. Condition monitoring gives real-time machine working conditions via alerts and allows the maintenance team to take action when the problem is detected. In the cement industry, CBM performs vibration analysis of rotating equipment, oil, grease analysis, thickness measurement of kiln shell and chimney ducting, etc., to examine the assets’ health.

Predictive Maintenance (PdM) in the Cement Industry.
The highly competitive & quality-focused requirement of cement plants today means that condition monitoring falls short in many aspects. This gives rise to Predictive Maintenance: a proactive approach to maintenance that uses IoT and machine learning to predict impending machine failure.

Predictive Maintenance solutions consist of hundreds of strategically placed sensors that record data and send it to a central IoT platform. The IoT platform monitors and analyses any anomalies and notifies the plant manager of the equipment’s life.

The Need to Move from CBM to Predictive Maintenance in the Cement Industry.
1. Condition-based Monitoring technology monitors the real-time condition of the machine and shows warnings when an anomaly happens. While this means it is better than the time-based & reactive maintenance approaches, it still can cause downtimes & in some cases, need repair & spare part costs. Predictive Maintenance technology, on the other hand, predicts the imminent machine failure before it takes place and saves from unplanned downtimes.

2. Condition Monitoring provides on-site engineers with data parameters that are often difficult to interpret in isolation. This means they need their subject matter experts to analyse these first before taking the right actions. By comparison, Predictive Maintenance gives insights behind the data around a machine anomaly, with the why of a particular machine behaviour & recommended actions for mitigation. This means faster decision-making by the on-site team without bothering SMEs for every minor glitch.

3.  Also, CBM technology warns of trivial anomalies that lead to excessive maintenance in cement plants, which leads to unnecessary maintenance and a loss in productivity & efficiency.

Predictive maintenance monitors the real-time condition of the equipment. It predicts faults with potential repercussions, ensuring maintenance activities are performed precisely where they are needed & only when they are required.

Thus, using PdM over CBM makes maintenance in cement plants more efficient and hassle-free.

Significance of Predictive Maintenance in the Cement Industry


First-generation machinery that is decades old is still being used in cement manufacturing. Due to rough operating conditions & continuous running, machines are more susceptible to breakdowns resulting in downtimes. These unplanned downtimes hamper the production quality, reduce profits and create unsafe working environments in the plant.

Predictive maintenance in cement manufacturing resolves these frequent maintenance issues by foretelling the machine failures with least or no human inspection. It enhances the visibility of machine health throughout the plant, enhancing proactive decision-making. 

Predictive maintenance is essential in the cement industry because

 

  • It helps lengthen the life & performance of older machines.
  • It reduces repair & spare part costs due to proactive maintenance.
  • It reduces the frequent planned & unplanned downtimes, which results in a better quality of cement and consistent production.
  • It reduces the chances of any safety hazards caused due to machine malfunction.
  • It saves a lot of time and costs, which otherwise would go into maintenance.
  • It leads to better worker productivity & overall plant efficiency.

Why Predictive Maintenance is the Future of Cement Manufacturing?


A sustainable future of high-quality output, a productive workforce & reliable machinery can be achieved by the digital transformation in the cement industry. As the cement industry is getting ready for a global inflection, environmental & regulatory compliances are expected. Green cement manufacturing will soon become necessary to save the environment and resources.

The rise in the requirement for green cement will necessitate a lean and highly efficient operating style and long-term bottom-line growth. Amidst all developments, predictive maintenance solutions will remain a significant value driver in the shifting roadmaps for obtaining a competitive advantage in this market, enabling better results for workers, customers, management & overall ecosystem.

Conclusion:

Machines in the cement industry work in harsh conditions and, thus, are more prone to breakdowns. A proactive approach to maintenance would be beneficial for plant productivity. Predictive maintenance is preferred over condition-based monitoring systems as it can predict the problem before it happens. In contrast, CBM can only monitor real-time equipment conditions and can’t predict future anomalies.

Also, CBM is less accurate, while PdM is proved more accurate over time by learning through machine learning technology using historical and real-time data. Predictive maintenance technologies will surely lead the future of maintenance in cement plants.

At Infinite Uptime, we strive to transform the industrial health diagnostics space, particularly for process-driven industries like the Cement industry. We offer predictive maintenance solutions enabled with machine learning and IIoT technology that companies combat downtime, lapses in quality, productivity & OEE.

 

Want to know how we helped the largest cement manufacturer in India? – click here.

Categories
Condition Monitoring

Optimizing machine health with Condition Monitoring

Optimizing machine health with Condition Monitoring

Industry 4.0 aims to bring operational excellence by introducing the Industrial Internet of Things (IIoT) to the industries and factories. It allows machine health monitoring using IIoT, streamlining the process and increasing efficiency. In this blog, we’ll share why IIoT machine monitoring is useful and how it can help you?

 

Importance of Machine Health Monitoring in the Industrial 4.0 Era

Despite applying the best reactive and preventative maintenance strategies, industries lose a lot of money & time because of unplanned downtime, machine failures, and wasted maintenance cycles. Unplanned downtime decreases plant productivity and hinders the supply chain. To overcome this, plants need to adopt Condition Monitoring technologies.

IIoT machine monitoring offers real-time insights to assist maintenance teams in making better decisions, enhancing the machine’s efficiency and extending its lifetime. IIoT plays a vital role in enabling plant reliability by:

  1. Providing robust connectivity across the plant
  2. Catering to the growing shortage of plant workers.
  3. Helping in planning and scheduling maintenance strategies.
  4. Bringing more profit against its initial implementation cost.

It also creates a safer working environment for plant workers by reducing the chances of machine failures. 

 

Benefits of Machine Health Monitoring using IIoT

Improvement in the overall efficiency of manufacturing

IIoT machine monitoring machinery considerably increases the overall plant efficiency. It increases the cost efficiency by cutting unnecessary maintenance and decreasing unplanned downtime. Unplanned downtime constitutes a 40-50% loss in efficiency. Condition monitoring predicts the impending failures and helps in curing them beforehand.

Real-time monitoring and required maintenance of all the plant assets enhance the plant’s productivity and sustain it. It also extends the plant equipment’s lifetime, saving many costs that otherwise would go in vain.

Considerable Reduction in Waste

IIoT machine monitoring can help industries in waste management. Defective items are the most significant manufacturing waste from plants. Trivial machine malfunctions often get ignored, which causes the production of defective items or sub-par output quality. It costs money, resources, and man-hours. Also, starting up after unplanned downtimes produces unprocessed/semi-processed goods, further increasing the overall plant waste. IIoT machine monitoring can eliminate this waste by foretelling the possible threats.

Intelligent adoption of IIoT-enabled solutions also reduces the burden of excessive maintenance, lubrication, and spare parts waste. Assessing and predicting machine failures saves time and resources, which would otherwise go to waste.

Improved communication & decision making

Machine health monitoring using IIoT improves communication by providing 360-degree visibility of manufacturing operations to all the right people. Advanced solutions connect plant equipment to a manager, manager to the operator, and operator to operator effectively, reducing the chances of delayed communication.

Providing the right & timely information across the plant boosts the plant productivity multiple folds. Usually, a lot of time gets wasted in planning and scheduling maintenance strategies. IoT-based machine health monitoring systems capture the fault and track down the root cause to advise you on the best approach to tackle it.

Real-time Data Collection, Analysis, and Alerts

IIoT-based condition monitoring systems collect real-time data from all the machinery and analyse and assess them according to the recommended performance levels. If the machine health fails to meet the set parameters, the platform immediately alerts the maintenance manager and conveys the problems with the recommendations to take care of it.

The sensors record data from various equipment to perform vibration analysis, oil analysis, temperature analysis, and other relevant analyses. After analyzing, if it detects any issue, it quickly notifies the possible reasons. For example, the reports may suggest engine erosion if higher than usual iron content is found in the oil analysis. On the other hand, if a higher range of a combination of iron, Aluminium, and chrome is found, it may signal the upper cylinder wear. The maintenance manager can then take immediate action on this.

Conclusion

Even the best reactive and preventative maintenance strategies couldn’t do justice to the cost and productivity. So, industrial revolution 4.0 brings advanced machine health monitoring using IIoT technology to address production and maintenance issues. The technology gained its importance by tackling day-to-day problems in various industries. It benefits industries by reducing plant wastes, collecting and analyzing real-time data, streamlining communication, and increasing overall plant reliability.

Categories
IIoT

Predictive Maintenance & IoT Impact on Mining

Predictive Maintenance & IoT Impact on Mining

The mining industry is one of the oldest & most hazardous commercial sectors where the use and implementation of modern technology are very gradual. Mining companies utilize a plethora of expensive equipment in a high stakes & cost environment. In these cases, asset health is critical to the safety & profitability of the mine.

This is where IoT-driven Predictive maintenance can be a gamechanger. It has the potential to collect and analyze environmental and equipment data instantaneously and conduct real-time risk and area evaluation. It reduces the risk of downtime & loss due to machine failure and reduces overall maintenance & spare part costs of high capital-intensive machinery. The application of IoT in the mining industry is quintessential because of its advantages for large-scale operations in mining, where the operating environment is constantly changing & workforce operates in a compact, adapting, and potentially hazardous environment.

 

Let’s first try to understand the what makes maintenance for the mining industry difficult:

Challenges in the mining industry

Disruptive & exorbitant impact of equipment failure in mines Equipment failure is the worst nightmare for mines. A standard mining operation spends 35-50 percent of its yearly operations budget on just asset maintenance & repairs. Unpredictable equipment failure can disrupt production & a considerable dent in the bottom line.
Remote monitoring of equipment at far-off locations Mines are typically located far away from civilization. So in case of unplanned downtime, it takes time to get expert maintenance personnel and spare parts to reach, diagnose and repair the equipment. These transportation delays & costs impact the budget as well as profitability.
Workforce safety depending on asset health Worker health & safety remains a big concern in the mining industry due to the difficult working conditions. Furthermore, as mines get deeper, the likelihood of a collapse & danger increases. While safety in mines has improved dramatically over the years, the fatalities caused by asset malfunction are a big reason for on-site hazards.
Unreliable connectivity options Additionally, because more mines are constructed in off-grid locations, providing stable electrical infrastructure to power mining operations and appropriate water supply becomes increasingly tricky. Connectivity is limited or unreliable, particularly in underground mines, and the 3G/4G signals may be difficult to pick up in remote regions.

Types of machine maintenance in mining

The different types of machine maintenance are:
  1. Reactive Maintenance/ Run-to-Failure Maintenance: This refers to repairs performed after a machine has already failed and it is unexpected and thus leads to emergency rushed repairs.
  2. Preventive Maintenance: This refers to any planned or scheduled machine maintenance that aims to identify and repair problems before they cause failure. It can be annual/bi-annual. But it cannot prevent asset failure between two schedules or unnecessary downtime.
  3. Condition-based Maintenance: It focuses on monitoring the current status of assets to undertake maintenance when evidence of decreasing performance or approaching breakdown is detected.
  4. Predictive Maintenance: It expands on condition-based maintenance by utilizing instruments and sensors to continuously evaluate machinery performance & flagging off any anomaly and its root cause before it results in a full-blown asset failure.

Predictive Maintenance in mining can cause many benefits – direct & indirect.

Some of the benefits of Predictive Maintenance are:

  • Reduced Downtime: Utilizing predictive maintenance, you can anticipate troubles ahead of time, decrease machine downtime, increase uptime by 15-20%, schedule maintenance as needed, and thus extend the life of an old machine by up to 20%.
  • Increasing Productivity: It ensures that both planned and unplanned downtime is kept to a minimum, resulting in fewer interruptions to production and a significant increase in overall productivity.
  • Higher Production Capacity: Asset availability of high performing & critical assets in mines helps plan and optimize production capacity, which is crucial for effective management & production planning and staying on schedule.
  • Lowered Maintenance & Spare Part Costs: Maintenance and spare part costs are significantly lower for preventative maintenance since all machines in the manufacturing process are continuously monitored and repaired before a problem becomes severe.
  • Enhancing Workplace Safety: Predictive maintenance can reduce the risk of work-related accidents by identifying any discrepancies that could lead to an accident on-site. Predictive maintenance ensures a sanitary and healthy environment in the plant while reducing safety risks by up to 14%.
  • Proactive Decision Making: The implementation of IoT enables mining maintenance managers to detect when there is a breakdown or a drop in performance, enabling them to react quickly and effectively. In addition, monitoring, obtaining, and analyzing data from particular mining equipment over a period may help them understand how the overall efficiency of the process itself can be improved.

Conclusion

The mining industry has been a critical sector globally for centuries. With the right Predictive Maintenance solution, mine maintenance managers can ensure that the production continues without impacting commercial efficiency while ensuring worker safety. A sound & functioning asset also ensures a greener footprint and fewer hazards, proving to be less dangerous for the environment.

Want to know more about how a competent Predictive Maintenance solution by Infinite Uptime is helping some of the largest mining companies improve asset & operational efficiencies?
Categories
Metals & Mining

Future of maintenance in the metals sector

Future of maintenance in the metals sector

Metals and Mining industries are one of the oldest industries and have always helped in the economic development across the globe, right from the first industrial revolution. Today, the metals sector faces numerous challenges from lack of competitive advantage, power shortages, tight budget, regulatory compliances, etc. But one of the significant challenges is still unplanned downtime and maintenance in metalworking.

Here, in this article, we’ll break down the major challenges and discuss the future of maintenance through the predictive maintenance solution for the metal industry.

Challenges faced in the maintenance of the metal industry

Challenges from growing competitors, increasing energy costs, government regulations, etc., make it difficult for metal companies to maintain profits. To add to this, unplanned machine failures and downtimes increase the production cost, reducing the revenue.
Anomalies with old and legacy machinery
Machinery in the metalworking plant is generally old (~30/40 years old) and demands extra care and services. Though equipment in the metal sector was designed to work in the harshest conditions, it does break down with age and poor maintenance strategies. And thus, continuous monitoring & maintenance of assets is essential.

Unplanned downtimes due to machine failures

Downtimes are the worst nightmares for the metal plant heads as they come with no warning alarms and cost time, money, and manpower. Unplanned downtimes decrease plant productivity and delay production, leading to loss of customers and a decline in profits. Thus, maintenance in metalworking plants is required to prevent unplanned downtimes.

Low Maintenance Budget

Cost pressures on metal manufacturing are increasing due to the soaring prices of coking coal. Manufacturers end up trimming the maintenance budget to meet tight margins, retain customers and stay profitable. Even when the rest of the plant scales, maintenance teams are typically still small & lean. What needs to be understood is that plant maintenance activities are critical for business profitability, and investment in tools that can help your maintenance team be on top of all machine parameters is mandatory.

Lower Productivity of Workforce

Inefficient maintenance strategies and improper structure and scheduling often hamper productivity. Even when not entirely down, faulty machines result in sub-par output quality, increased time for plant operators, and lower productivity. Maintenance teams have to spend a hefty amount of time diagnosing issues from scratch, which hampers the plant’s overall productivity. Predictive Maintenance helps avoid this by pointing out anomalies and also suggesting resolutions.

High chances of a safety hazard due to machine failures

Machine failures cost money and lives if spiraling out of control. There are always chances of safety hazards around old machines as they frequently break down. Predictive maintenance for the metal industry can resolve this issue by cautioning about the imminent machine failures before they reach a severe stage.

Scope of Predictive Maintenance for the metal industry in the future

The maintenance of a metal industry or plant is a cumbersome process. It demands diligence and precision, yet machines break down. Machine failures and downtimes are very catastrophic for the plant. One significant machine failure takes away all the past maintenance hard work, plant productivity, and future profits.

Fortunately, advanced technologies that empower industry 4.0 have got our backs. The rise of IoT, 5G connectivity, automation tools, AI, and Machine learning simplifies the maintenance process. An IoT-led Predictive Maintenance solution can foretell the potential failures and prescribe the requisite corrective measures, protecting not just machines but your bottom line too. Here is how:

Benefits of Predictive Maintenance in the metal industry

Predictive maintenance tools can be beneficial in the metal industry as they can save a lot of money and time. It thoroughly resolves some significant problems in the maintenance of the metal industry.

Predictive Maintenance analytics offers substantial time and cost savings.

Prevent downtime & asset health degradation

Foster real-time decisions

Better quality output

Predictive Maintenance is highly cost-effective, saving roughly 8% to 12% over Preventive Maintenance and up to 40% over Reactive Maintenance (according to the U.S. Department of Energy). It gives you a competitive edge against the other players in the market, which you can leverage to produce and supply higher quantities of goods.

Low chances of safety hazards

Well-functioning machines promise a safe on-site environment. Real-time assessment and beforehand cure of any anomaly can sustain the plant equipment and extend its life. Non-faulty machines reap great results and ensure a safer working environment for plant workers.

Conclusion

Future maintenance in the metal industry necessitates futuristic technology to cut costs and increase production efficiency. A Predictive Maintenance solution answers the most questions on plant maintenance, and it provides a competitive edge that you can leverage to increase your profits.

At Infinite Uptime, we strive to transform the industrial health diagnostics space by enabling process manufacturers to use their assets to their full potential. With our advanced predictive maintenance platform, we are helping unveil the untapped efficiency and productivity of the metal industry and creating a profitable future.

Want to know how we can enable better asset health & performance for your metal plant? Reach out to our team of experts today.
Categories
Condition Monitoring

How to choose a maintenance solution for your plant in 2022?

How to choose a maintenance solution for your plant in 2022?

Revolutions are synonymous with disruptions. Industry 4.0 is nothing different. It demands new and advanced technologies for manufacturing plant maintenance and discarding obsolete plant maintenance processes at a much faster pace. It sometimes becomes overwhelming to understand and adopt new technologies as a plant head. So, here, we have an article to help you choose the right maintenance solution for your plant in 2022.

In this article, we’ll majorly talk about how you can choose the right Predictive Maintenance solution for your plant. But let’s start with the 3 basic types of industrial plant maintenance solutions available in the market.

Types of industrial plant maintenance

Reactive Maintenance


Maintenance that is out of reaction rather than duty and is performed only after the equipment is finally broken. This obsolete maintenance strategy can save you money in the short term but eventually increases your losses by increasing machine downtime, inefficiency, and frequent failures

Preventative Maintenance

Preventative plant maintenance requires scheduled check-ups routined on industry standards, and it involves timely maintenance and carry-out tasks like a belt and filter changes regularly. This maintenance strategy for plants and equipment increases equipment life but requires regular labor for check-ups and maintenance.

Predictive Maintenance

Predictive plant maintenance leverages artificial intelligence, cloud storage, and IoT to provide real-time data on plant equipment. It diagnoses the real-time condition of in-service equipment, and then the required maintenance schedule is followed. It also reduces the operating cost by 12-18% and provides a safer working environment.

Objectives of a Predictive Plant Maintenance Solution


The objective of opting for a plant maintenance solution is to elongate the life of plant equipment and operate them in an optimum condition at minimum cost. Here are all the significant objectives below-

 

  1. To maintain the peak productivity of the manufacturing plant.
  2. To obtain the optimum working capacity of equipment at the lowest possible cost.
  3. To minimize the losses from unwanted breakdowns and downtimes.
  4. To provide a safe working environment for plant workers.
  5. To protect the equipment from frequent breakdowns and efficiency loss.
7 most important questions to consider before choosing a Predictive Plant Maintenance solution


Predictive Plant Maintenance Solution comprises equipment and sensors, gateway, cloud service, and management to sense, record, and provide actionable insights on the machine’s condition. Artificial intelligence, machine learning, and IoT always try to yield accurate results.

But before you buy a predictive plant maintenance solution, consider these 7 critical aspects of it to decide which predictive maintenance solution is right for you.

Easy-to-Use and intuitive for everybody


The ideal Predictive Maintenance solution must be easy to use for all, from onsite plant operators and technicians to the plant manager & plant head. It should be intuitive and user-friendly to be mainly accessible to everyone required. If you need a data scientist every time to decode the insights provided by this software, then you are set up for sudden asset failures due to delayed responses.

The right predictive plant maintenance solution can empower the onsite condition monitoring/ maintenance teams with the correct machine data at the right time for successful plant maintenance assessments with actionable insights.

Finding the root cause, not just alerts

Some Predictive Maintenance solutions indicate only alerts of anomalies, while the others yield insightful data alerts with what might be causing them. Those insights can be used to get a 360º condition of working equipment, and plant engineers can trace the root cause of the problems and plan a more effective solution. It also helps to distinguish the false alerts from the true ones.

For example: Just pointing out an issue with an exhaust fan of a kiln in a cement plant may lead to 1000 causes, but a solution that analyzes this further and points to a loose bearing that may be the cause can lead to a different level of agility for your maintenance teams.

Are the outcomes measurable or just hopeful?

Ensure that the maintenance technology brings you the results in some way or the other. And the results must be measurable and not hypothetical, which means you should be able to calculate the profits that the technology is bringing against its cost.

The average cost per hour of equipment downtime is $260,000. Look for a predictive maintenance solution that can save you the downtime cost and increase profits. Predictive maintenance can reduce machine downtime by 30%-50% and increase machine life by 20%-40%. (McKinsey)

Usable across assets and manufacturers

A plant usually has various types of equipment from multiple manufacturers and suppliers, depending upon the quality and cost. The Predictive Maintenance solution you are planning to install must easily integrate and comply with every piece of equipment in the plant- regardless of its age, type, and manufacturer.

Having different data collection mechanisms for different equipment is costly and leads to entropy & silos that obstruct the whole picture. Technology, along with human intelligence, functions to streamline complex processes and increase efficiency, and not the opposite. 

Experience around process plants

Process manufacturing plants differ from other industries in various aspects. Predictive maintenance solutions request historical data to function reliably, but process plants have limited historical machine data, making it difficult for the predictive solution to function properly. Make sure your vendor has experience working with process plants to tackle the situation constructively.

 

Deployment & scaling time 

One of the most popular hesitation in IoT-driven Plant maintenance deployments is the time taken to deploy the solution. If the deployment takes months, the internal enthusiasm built around the deployment dies down, and so does the ROI.

It is also essential that the deployment velocity is maintained when the solution is scaled up-whether from some machines to the entire plant or across plants.

Look for a predictive maintenance vendor that can integrate the solution in your plant and enable working within a few weeks and not months.

Predictive Maintenance in mining can cause many benefits – direct & indirect.

Conclusion

Predictive plant maintenance solutions save millions of dollars for manufacturing companies by predicting equipment health and indicating impending failures beforehand. Various predictive maintenance solution providers come with multiple packages, and hence choosing the right fit for your plant is important. Look for an easy-to-use and intuitive product that can comply with mixed assets from diverse manufacturers.

At Infinite Uptime, we strive to transform the industrial & machine health diagnostics space. Our Predictive Maintenance solutions are used by hundreds of process plants globally, saving millions of hours of downtime, and improving the efficiency, scale & output of plants, one insight at a time.

Want to know more about how you can safeguard your machine’s health & reliability with Predictive Maintenance?

Click here to schedule a demo with our team of experts.
Want to know more about how a competent Predictive Maintenance solution by Infinite Uptime is helping some of the largest mining companies improve asset & operational efficiencies?

Categories
Cement Industry

Why are cement plants the perfect candidates for Predictive Maintenance?

Why are cement plants the perfect candidates for Predictive Maintenance?

There are three facts about cement plants that are universally true: 

  • The average machine age in a cement plant is at least 30-40 years. 
  • Finding the right expertise to maintain them consistently is challenging.
  • Every machine – big or small – has the power to bring the whole process to a complete standstill. 

 

These three facts establish that proactive machine maintenance in cement plants is critical to remain profitable and scale efficiently. As demand for cement grows hand-in-hand with blooming infrastructure, GDP growth & exports, the pressure on cement plants to produce continuous, high-quality output also increases proportionately.

 

This article discusses Predictive Maintenance, a new age approach for plant maintenance, and why an IoT-led Predictive Maintenance approach can solve most of your maintenance worries for your cement plants.

Introduction to Predictive Maintenance

Predictive Maintenance in process manufacturing plants such as the cement industry can identify deviations in machine health at the nascent stage before they escalate into full-blown problems that may result in unplanned downtime.

But that is putting it very mildly. If you look at the daunting results of a neglected cement plant, violent accidents and sky-high repair and replacement costs, while the downtime continues indefinitely, are two of many consequences of a system that is not armed with the intel that Predictive Maintenance can provide. 

Here’s a simple example that explains the difference between the health of a machine that uses Predictive Maintenance and one that doesn’t – exam preparation.

An intelligent student looks at exam preparation as a daily occurrence, checking in regularly to maintain good grades and maximize performance at the end of the year. A weaker one only thinks about the exam preparation as a reaction to the possibility of failing and only begins to take action when things have started to go south. 

Condition Monitoring & Predictive Maintenance operate how a good student goes about exam prep. While Condition Monitoring checks in with the machine’s health periodically, Predictive Maintenance sees that the machine is continuously monitored and will keep functioning like it is supposed to for as long as possible. 

Why is Predictive Maintenance critical for the cement industry?


Let’s dive into the specifics of what makes Predictive Maintenance critical for cement plants:

Diverse assets and asset categories make finding the right workforce difficult.

The cement manufacturing process involves multiple ingredients & processes, with various machinery used at every stage of every process, meaning many types of assets need to be covered. The sheer number of diverse machines makes it difficult to find the same variety of expertise and strength in numbers to manage them. Add to this the fact that many employees don’t have the specialized knowledge to evaluate the machines and act in time, and you have a classic problem.

With Predictive Maintenance, employees need to act upon prescribed causes & mitigation steps to restore machine status. So, even when their domain knowledge is limited, automated Condition Monitoring and Predictive Maintenance nudge things along the way.

 

 

Remote locations make reactive action expensive and delayed   The remote locations of cement plants make unplanned downtime a lengthy affair. Finding the root cause of machine failure, sourcing & transporting the spare parts takes a long time. For uncommon causes of machine failure, having a Subject Matter Expert (SME) or an experienced plant engineer on-site 24*7 is next to impossible today, and escorting them to the premises whenever required turns out to be very expensive. Predictive Maintenance can solve this by providing concise instructions to fix problems, reducing the need to fly in experts frequently. On the other hand, the Subject Matter Experts (SMEs) can also diagnose the root cause of machine failure remotely with all the relevant data at their disposal.

Digitize the entire plant, not parts of it. Every business has assets they value more than others, which is the case in cement plants too. Assets considered to be more income-generating than others and acquired at a higher cost are taken care of more meticulously.

As a result, according to statistics, only 10% of equipment at cement plants is digitized, leaving the others to be monitored manually & open for risks of sudden failure.

This can escalate into unexpected downtimes with dire consequences at a process manufacturing plant. Regardless of the size of output or functionality of a machine, a system failure for one machine spells unexpected downtime for the whole plant. IoT-based Predictive Maintenance makes it easy to digitize all the machinery in a plant, making it easy to monitor the entire process regardless of location.
Lack of number & skilled workforce adds risks. Workforce planning in manufacturing is more expensive than ever, and it is challenging to scale labor at the same rate as capital. The traditional mindset toward plant maintenance perceives it as a quality function rather than a revenue generation function. This means that although the total number of workers across the plant may grow 10X, the Condition Monitoring team size still stays X.

On top of this, experienced plant SMEs who retire or change their jobs also take the native knowledge of the machine operations with them. Lesser skilled personnel might find it challenging to understand the finer details about all the machines.In this scenario, Predictive Maintenance can help make the process seamless, making it easier for less-qualified or inexperienced plant managers to follow specific instructions and fulfill their duties.
Reducing repeated capital expenditure with prolonged asset life. Going back to the beginning of this article– most of the machinery we are talking about here is several decades old, and it may have been there since the very beginning of the industry in the country. The aging equipment will require replacement in the coming decades.

Replacing plant machinery requires a colossal capital influx and is not a feasible option. According to Entrepreneurship magazine, setting up a cement plant today producing 5000 MT/day would require an investment of at least USD 13.77 million to start with, only for the plant & machinery. That is why Predictive Maintenance is the best way to take care of these machines and prolong their Remaining Useful Life (RUL) as long as possible by detecting every minor fault that has the potential to turn into a catastrophe.
Save costs & time by narrowing fault down to a specific machine part. Predictive Maintenance can identify the problem areas of your plants very closely, making it easier and cheaper to fix problems. For example, A kiln is integral to the functioning of a cement plant, but there are smaller fixtures inside this massive furnace that are just as important. Nuts, bolts, and exhaust fans are small but essential kiln components.

If one of these shows anomalies, Predictive Maintenance can indicate that the problem is occurring due to an issue with the exhaust and not the kiln as a whole. Quickly replace the fan, and your system is as good as new.
Integrated machine analytics help in proactive decision-making. Integrated machine analytics allow organizations to understand the plant operations better and make proactive decisions about machine maintenance, product output, and efficiency. By collecting data from various machines across plants and presenting it on a dashboard that can be accessed remotely from anywhere, it becomes easy for the concerned authorities to identify patterns and trends and take insightful actions in time.

Predictive Maintenance ensures optimum operation and performance of machines, thereby ensuring consistent output. This consistency eventually makes for better quality, helping you stand out as a company that has the potential to be a market leader.
Ensure consistent quality of output, sustainability & Environment Safety. Sustainability & Predictive Maintenance don’t seem to be connected at first, but they are deeply interlinked. A poorly maintained machine doesn’t just result in bad performance or output but can be a sink for energy consumption and a catalyst for an explosion or an on-site accident. These accidents can result in a catastrophe both from a sustainability and a worker safety point of view.

Why are IoT-driven Predictive Maintenance solutions better than conventional factory automation systems?

Before IoT-driven Predictive Maintenance solutions, manufacturers used factory automation solutions like Allen Bradley & Siemens for plant maintenance. Here is how IoT-driven Predictive Maintenance solutions are a better choice for cement plants:

1. Predictive Maintenance is a proactive solution, not a reactive one.

  • A conventional factory automation system will shut down operations in response to a crisis to avoid further damage.
  • An IoT-based solution will see that crisis coming from a distance, initiate a likely fix, and alert superiors of the occurrence. 
2. Factory automation systems are prohibitively expensive compared to IoT-driven predictive solutions.
  • The higher costs meant that manufacturers could only cover their most expensive assets, leaving risks for unexpected downtime.
  • IoT-enabled Predictive Maintenance covers the entire plant at a reasonable cost, ensuring all the machines receive equal coverage..
3. Factory automation systems were designed decades back, and a lot has changed since then.

The main action taken by these archaic systems is to shut things down and minimize damage, sealing its fate as a glorified fire extinguisher.

On the other hand, IoT-driven solutions for the cement industry aim to:

  • Maximize the productivity of your plant, not just to avoid calamities.
  • It gives you the power of foresight, which is valuable in an industry as competitive as this one.
  • Older systems do not even look into parameters that IoT scrutinizes, e.g., measuring the vibrations of a machine is a brand-new feature overlooked before.

Conclusion

With the right solution & team of domain experts, Predictive Maintenance can create an unbeatable competitive advantage for your cement plant, fostering efficiency across the workforce, resources, and processes. By identifying and addressing minor issues with critical assets before they become big problems, Predictive Maintenance helps keep machines running smoothly and efficiently, leading to higher quality products and lower costs. It not only optimizes maintenance costs but also increases improves operational efficiency by reducing unscheduled downtimes.
Categories
IIoT

Decoding IT/OT Convergence: A Guide on Understanding IT and OT

Decoding IT/OT Convergence: A Guide on Understanding IT and OT

As IoT grows synonymous with digital transformation & advancements in manufacturing, it has also led to a wave of change on the shop floor. This is a significant result of IT/OT Convergence, which led to faster decision-making, better collaboration, and a single source of truth across the organization.

But what does the IT/OT Convergence do with IoT, though? How are IoT, Information Technology (IT), and Operational Technology (OT) connected? For starters, they have the same three letters appearing in some sequence in all three abbreviations, but what more do these three have in common?

It’s essential to understand these terms before analyzing the IT/OT convergence.

 

What is Information Technology (IT) and Operational Technology (OT)?

Until IoT became a thing, there were two distinct worlds – traditional OT systems, which have machines, devices, and other industrial equipment, and more digital IT systems that handle everything related to computers, servers, storage, networking, and others. It’s been a while since the two worlds crossed over into one – IoT. To put this in simpler terms using an application of IoT, the smart devices in our homes today that are automated are a perfect example. These devices are part of a network that combines the prowess of both IT and OT systems to automate seemingly mundane human tasks like switching on and off lights. Now that we understand how IT/OT convergence happens, let’s look at IT/OT definitions with some jargon. As the name suggests, Information Technology (IT) includes computers, servers, and networking devices to create, process, store, and exchange all forms of electronic data in a secure manner. For a manufacturing environment, it can be hardware like laptops and servers and software for ERPs, inventory management, and other business-related tools.On the other hand, Operational technology focuses on managing and controlling physical devices operating globally. For manufacturing, it can include systems like MES, SCADA, PLCs, and CNCs that monitor & control the processes on the shop floor.

How does IT/OT Convergence help in Manufacturing? Converging various aspects of technology is as old as technology itself. Integrating and interoperating different technologies can increase efficiency, cut down costs, and improve the workflows of multiple applications.

Earlier, the OT teams would handle everything that came under the purview of operations, keeping the plant running smoothly. On the other hand, the IT team runs business applications smoothly from the head office. They would only collaborate for one-off tasks like unplanned downtime, an untoward security incident etc., without any real collaboration.

The data for both teams was available in silos with no single source of truth-giving birth to communications issues, blind spots in processes and delayed decision-making. The OT machines, in particular, were only communicating with the world via niche M2M protocols, with data stored at disparate locations, available only in silos. This is where IT/OT convergence came in.

The IT/ OT convergence aimed to bring physical equipment (OT) into the digital world of IT. This was made possible, thanks to many advances in the tech industry, starting from Machine-to-Machine (M2M) communication, not to mention the increasing sophistication of IoT sensors and actuators that can be incorporated into OT equipment. Wireless communication over standard networking protocols allowed the data from each OT system to be communicated to a central server. The IT OT convergence allows for increased autonomy, maintenance, uptime, and accuracy of all the physical systems involved, with instant machine data access to the relevant stakeholders.

This convergence is focused primarily on automatic processes, using connected devices equipped with sensors to gather, send, and receive data. The data then is stored in a central platform, where it can be analyzed, monitored and actioned upon in real-time. This opens up a new realm of possibilities, where anyone with the know-how can develop APIs to analyze different devices and monitor, analyze & control their functioning.
Manufacturers Boon – The IoT Convergence With IoT, IT/OT convergence in manufacturing has become a success story.

The convergence allows businesses and manufacturing entities to be more cost-efficient (or, more precisely, resource-efficient – be it costs, time or supply involved). With the sales and inventory data to optimize manufacturing operations, equipment and energy consumption is more efficient, while maintenance and the quantity of unsold inventory are reduced.

Here are some notable key benefits of switching to an IoT-enabled manufacturing environment.

  1. Real-time decision making:IIoT (Industrial Internet of Things) allows manufacturers to collect all the data they would ever need and analyze it in real-time. Sensitive data can be analyzed directly at the source, which significantly reduces the bandwidth required, not to mention the increased levels of security.
  2. Predictive Maintenance: One of the most significant benefits of IIoT is the revolution of predictive maintenance. Unplanned downtime can cause manufacturing entities to lose a substantial amount of money, while the traditional preventive maintenance method proves to be highly costly. The IT/OT convergence makes it possible for manufacturers to predict when the machines need maintenance and plan accordingly without unnecessary downtime or repair costs.
  3. Increased Efficiency: Whether your manufacturing entity is looking to decrease annual energy costs, increase inventory turns, reduce the time to introduce a new PLC, decrease defect rates, or improve the overall effectiveness of the physical machinery involved – IT/OT convergence can help your business do it all.

Phases of IT/OT Convergence

There are three primary phases of IT/OT Convergence.
  1. Process convergence – Deals with the intersection of workflows, ensuring that important projects and data are communicated to relevant stakeholders.
  2. Software and Data Convergence – Deals with procuring the necessary software and data from the front office for the IT/OT needs. This is a technology-based convergence that deals with the network architecture of the enterprise.
  3. Physical Convergence – Deals with the hardware – old hardware is either replaced or retrofitted with new sensors and actuators to accommodate the incorporation of IT into traditional OT.

Final Word

IT/OT convergence has been a significant milestone in the IoT journey and a win-win for both OT & IT Teams.

The OT teams can now access the machine data whenever they need it for proactive decision making to create value in their machines, processes & workforce. They can align better with overall business systems like ERP etc., creating unparalleled insights.

The IT teams can fulfil their smart factory vision with a healthy understanding of the ground reality and collaborate with the operations team to evolve together.

We’ve covered many of such stories in detail in our Case Studies section – where we showcase just how much businesses in your industry can gain through process digitization and using the Internet of Things.

Want to know how IT/OT convergence can revolutionize your manufacturing processes? Please get in touch with us – and our domain experts would be happy to explain over a quick call.
Categories
Diagnostic Service

Predictive Maintenance as a Service for the Steel Industry.

Predictive Maintenance as a Service for the Steel Industry.

Introduction:

The steel industry plays an essential role in developing the economic standing of any country. Since steel is a necessary component for every primary sector, to keep the country’s economy in full bloom, steel factories need to ensure that all production processes are running smoothly, which can be hindered by unplanned downtime.

Unexpected downtime can cost not just a lot of money and time for any steel plant but can also affect the production and growth of downstream industries dependent on steel production. Machine availability and reliability being the top concern in steel production, the cost of secondary damages of such breakdowns can be astronomical. This can significantly affect the quality, operational efficiency, loss of productivity, and increased risk of accidents on site. With such high stakes, using predictive maintenance to avoid unexpected downtime can be a gamechanger for steel plants.

What are the Challenges in Steel Manufacturing Industry?

PROCESS LEVEL CHALLENGES: Steelmaking involves many manufacturing techniques which are time-consuming and complex. Apart from primary production processes, there are many sub-processes where the intermediate products are reheated, solidified or pressed into various forms, like pipes, sheets, bars, rods, and different structural shapes based on the requirement.

The primary steel manufacturing process is continuous and process-based, whereas the secondary manufacturing phases are discrete. What complicates it further is the fact that:

  • Production process parameters from the upstream steel manufacturing processes strongly influence the downstream ones.
  • The intermediate products in the process undergo both chemical and mechanical changes, making monitoring quality and output more difficult.
PLANT LEVEL CHALLENGES: While steel manufacturing is already a complex process, steel plants may also face many on-ground challenges in maintaining efficiency, such as:
  • Older & legacy machinery
  • Frequent halts in production due to machine failure & downtime
  • Expensive coal & raw materials
  • Avoiding unexpected accidents on site
  • Various external factors like lockouts, strikes, inefficient administration, and shortage of raw materials

Due to these challenges, steel plants face constant pressure to produce high-quality products in less time without any unexpected production halts. Unplanned downtimes can put the whole process on a stop, affect quality and production, and endanger the health and safety of workers on site.
What is Predictive Maintenance? Predictive maintenance is the next level of condition-based maintenance that regularly monitors the operating condition and health of machines through edge computing. It helps predict asset issues before they occur, thus not disrupting the manufacturing workflow, reducing accidents, and improving the machine’s overall availability & reliability.

The data from the edge computing systems continuously provide results in real-time to alert you of machine performances and machine breakdowns. It also alerts you of maintenance based on what machine data indicates, which helps to avoid any unexpected repair costs.

Advantages of Predictive Maintenance for Steel plants

Reducing downtime and Ensuring asset longevity & RUL: Failure of machines can be pretty stressful and is an added expense. Using predictive maintenance, you can predict issues ahead of time, reduce downtime of machines, increase uptime by 15-20%, schedule maintenance as and when required, and thus improve the lifeline of the old machine by up to 20%.
Reducing maintenance costs Since all the machines in the steel manufacturing process are constantly monitored and fixed before the problem gets severe, maintenance and spare part costs are way lower than what they would be for reactive maintenance or preventative maintenance. There is also no need for unnecessary planned downtime.
Improving Workplace safety Predictive maintenance can lower the risk of workplace accidents by flagging off any anomalies that can trigger off an accident on site. Predictive maintenance ensures a hygienic and healthy environment in the plant and reduces safety risks by up to 14%.
Enhancing productivity By ensuring that both planned downtime & unplanned downtime are at their minimal, predictive maintenance ensures that there are fewer disruptions to production, improving the overall productivity drastically.

How Does Infinite Uptime’s Predictive Maintenance as a Service Solution Work for Steel Industry?

Infinite Uptime’s Predictive Maintenance as a Service uses real-time data to find out the status of the machine and the health of every rotating asset. The edge computing system is deployed to monitor all critical assets in every process and monitors parameters like vibration, temperature, etc. A machine health score is provided in real-time for every monitoring location. Anytime there is a dip between the prescribed machine score, an alert goes to the plant supervisor, along with a recommended remedial action suggested by our Predictive Maintenance as a Service solution. The machine status is further analyzed to ensure that the mitigated solution has improved the status quo.

Customized dashboards for different levels like plant operator, manager, plant head, or manufacturing head (multi-plant) are created & made accessible for the team to ensure agile & proactive decision making to ensure the production continues smoothly.

Conclusion

The steel industry is the linchpin of global economic development. Any unplanned downtime or production stoppage can jeopardize the steel manufacturers and manufacturers of all the industries that rely on it.

Predictive maintenance can be a value-added service for steel manufacturers. With the right Predictive Maintenance solution on your side, you can avoid extra costs, reduce downtime, increase productivity and focus your time and efforts on your business instead of worrying about unexpected downtimes or machine failures.
Categories
Condition Monitoring

Predictive Maintenance as a Service: A game-changer for Manufacturing.

Predictive Maintenance as a Service: A game-changer for Manufacturing.

Manufacturing encompasses a diverse and wide array of processes, industries, and raw materials. Yet all manufacturers everywhere share a common enemy: Unplanned Downtime, which harms productivity, asset health, brand reputation & dotted line.

Every year, top fortune 500 manufacturing enterprises lose almost 1 trillion to unplanned downtime, nearly 8% of their annual revenue. Here is how the introduction of Predictive Maintenance as a Service can be a gamechanger in the world of manufacturing & asset maintenance to curb unplanned downtime.

But to understand why Predictive Maintenance as a service is so revolutionary, let’s understand how asset maintenance was performed until a few years ago:

What are the types of Asset Maintenance practices in Manufacturing?

Reactive Maintenance: Reactive Maintenance means letting your machines run unchecked till they fail. Maintenance here is post-failure, as a reactive approach after the anomaly. While it saves you unnecessary downtime & maintenance costs for parts that don’t require servicing, it also means you risk machine failure anytime by being blind about your machine health.
Planned Maintenance or Preventive Maintenance: After the reactive maintenance approach resulted in constant fire-fighting for the plant manager, maintenance became a time-based activity, i.e., annual, bi-annual, based on their own and peer’s experiences. But often, it was noted that a planned downtime, although revealing nothing wrong with the asset, would still result in loss of productivity & profits. And sometimes, the machines would fail even before the planned period, so the problem persisted.

With the failure of both of these approaches to curb machine failure and thereby unplanned downtime in time, the industry looked forward to solutions like IoT & AI to power up maintenance with real-time insights. And that is what predictive maintenance as a service is all about.
Predictive Maintenance as a Service: Predictive Maintenance (PdM) relies on real-time monitoring of machine health using smart technologies like edge-computing, IIoT, data science, and analytics. Once an anomaly (w.r.t vibration, temperature, or acoustics) is detected, it is flagged off to the relevant plant supervisor for the next immediate action. A maintenance activity can thus be scheduled if something goes wrong while the maintenance expert can also decode the exact ‘something’. PdM enables the maintenance teams with necessary controls to extend equipment lifecycle, optimize the cost of maintenance, maximize machine uptime and amplify factory performance.

Types of machine maintenance in mining What Industries can Predictive Maintenance as a Service make the most impact on?

While any manufacturing plant- whether discrete or process-based can deliver a clear impact with Predictive Maintenance as a Service measures, the process-based manufacturing plants can truly thrive because of their unique workflow of interconnected processes.

Since the output of the process manufacturing plant depends on the previous steps completed in tandem, the stoppage of even a single machine can halt the entire production process. This is where predictive maintenance as a service can help by ensuring that the machine health issues are taken care of before they become serious.

Here are some examples of plants where Predictive Maintenance as a Service can save the day:

  • Cement plants
  • Steel plants
  • Metals & Mining
  • Oil & Gas Refineries
  • Power plants
  • Chemical plants
  • Pharmaceutical plants
  • Petrochemical plants

How can Predictive Maintenance as a Service be a gamechanger for Manufacturing?

Predictive Maintenance as a Service brings all the benefits of cutting-edge technology without the financial downside of capital intensiveness and sustainability. Here is how it can do magic for manufacturing plants:

Asset health & performance: In an asset-intensive industry like manufacturing, where the equipment is costly and used to the extreme, equipment & component replacement costs are prohibitively high. You can boost asset life, RUL (Remaining Useful Life), and Machine Uptime & Reliability by tackling asset issues before they get serious.

EHS & Compliance benefits:
Manufacturing plants consist of the most demanding working environments with toxic gases, material, and dangerous machines working furiously. It is no wonder that the regulatory guidelines get stringent now and then. It is also a risky working environment for the operators and other employees. Predictive maintenance as a service policy ensures no untoward accidents or compliance issues. Read more on our ATEX Certification.

OEE: Standing for Overall Equipment Effectiveness OEE is a globally prevalent metric that measures the productivity of a manufacturing asset. Calculated as a product of equipment availability, performance & quality of output produced, OEE is a benchmark for comparing the productivity of plants. The availability & performance of the machine depends on the maintenance & servicing when it is needed. According to a Deloitte study, a regular PM results in high OEE, Uptime & Reliability, compared to all types of maintenance.
Quality & Brand reputation: Regular Asset maintenance and machine health analysis can ensure that the machine performs at the top of its capacity. This will provide a high quality of the overall output. A fully functional plant producing quality output with minimal disruptions will also ensure a good brand image & reputation in the ecosystem.
Increased Employee Productivity: A well-functioning asset means that employees don’t have to fight fires caused due to last-minute machine failures. It also means quality and timely output, allowing them to be productive at what they do.

What Infinite Uptime’s Predictive Maintenance as a Service brings to the table?

The end-to-end Predictive Maintenance as a service by Infinite Uptime involves collecting data & computing the triaxial vibrations, temperature, and noise of the mechanical equipment in real-time via its patented edge computing system. The data is then monitored & analysed in real-time, and a machine health score is assigned. A machine with a lower health score is flagged to the plant supervisor or plant engineer with a diagnostic assessment score and the probable cause for the anomaly and a recommendation on improving the machine. This helps the maintenance teams to plan better and save critical downtime of machines which positively impacts the overall factory performance and productivity.

Conclusion

With real-time insights from interconnected assets being monitored and analysed instantly, predictive maintenance as a service provides massive power to the manufacturers without any drawbacks of a conventional maintenance solution. And that is when the true digital transformation will happen when data and insights combine to provide value.