Category: Uncategorized

Categories
Uncategorized

Optimizing machine health with Condition Monitoring

What is Machine Health Monitoring? Optimize Your Machine Health with Online 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?

What is Machine Health Monitoring?

Machine Health Monitoring refers to the use of advanced technologies, particularly the Industrial Internet of Things (IIoT), to continuously monitor the condition and performance of machinery in real-time. This process involves collecting and analyzing data from various sensors and equipment to identify potential issues, predict failures, and optimize maintenance strategies. By leveraging a machine health monitoring system, predictive maintenance strategies can be applied to schedule maintenance activities just before a failure is likely to occur. This not only minimizes unplanned downtime but also reduces maintenance costs and extends the lifespan of equipment. Real-time machine health monitoring thus plays a crucial role in maintaining operational efficiency and reliability.

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.

What is Online Condition Monitoring ?

Online Condition Monitoring, in the context of predictive maintenance, refers to the continuous observation of equipment health using real-time data collected through sensors. This approach enables organizations to track performance metrics like vibration, temperature, and sound, allowing for the early detection of anomalies that may indicate potential failures. By analyzing this data, predictive maintenance strategies can be employed to schedule maintenance activities just before a failure is likely to occur, thereby minimizing unplanned downtime, reducing maintenance costs, and extending the lifespan of equipment.

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.

How Online Condition Monitoring Works

  • Data Collection: Sensors are strategically placed on equipment to measure key performance indicators. These sensors continuously gather data, which is then transmitted to a central monitoring system.

  • Real-Time Analysis: The collected data is analyzed in real-time using sophisticated algorithms and machine learning models. This analysis helps in identifying patterns and detecting anomalies that could indicate potential failures.

  • Alert System: When the system detects an anomaly or a deviation from normal conditions, it generates alerts or notifications. These alerts enable maintenance teams to address issues before they escalate into costly failures.

  • Predictive Maintenance: By utilizing predictive analytics, Online Condition Monitoring allows for the scheduling of maintenance activities just before a failure is likely to occur. This approach reduces unplanned downtime and helps in optimizing maintenance resources.

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.

At Infinite Uptime, we have pledged to transform the industrial health diagnostics space. Thus, we offer IIoT-based machine health monitoring solutions for process plants like yours. Our intelligent solutions easily integrate into the plant using AI and machine learning and help tap your plant’s peak efficiency.

FAQs
Machine health monitoring using IIoT is vital in Industry 4.0 because it helps minimize unplanned downtime, reduces maintenance costs, and enhances overall plant efficiency. By providing real-time insights and predictive analytics, it allows for proactive maintenance, thus optimizing operations and increasing profitability.
IIoT-based machine health monitoring improves manufacturing efficiency by reducing unplanned downtime (which can lead to 40-50% efficiency loss), extending equipment life, and optimizing maintenance schedules. It also enhances workplace safety by preemptively addressing potential machine failures.
IIoT machine monitoring minimizes waste by predicting and preventing machine malfunctions that can lead to defective products or sub-par output quality. It optimizes maintenance practices, reduces unnecessary lubrication and spare parts usage, and improves overall resource utilization.
Real-time data collection through IIoT sensors allows for continuous monitoring of machine performance metrics such as vibration, oil quality, and temperature. Analysis of this data helps identify anomalies and potential failures early, triggering alerts for proactive maintenance actions.
IIoT-based machine health monitoring systems facilitate improved communication by providing comprehensive visibility into manufacturing operations. This connectivity ensures timely information flow between operators, managers, and maintenance teams, enhancing operational efficiency and reducing response times.
IIoT-driven systems offer significant advantages over traditional reactive and preventive maintenance approaches by enabling predictive maintenance. They optimize maintenance strategies, reduce costs associated with downtime and repairs, and support data-driven decision-making for better operational outcomes.
Categories
Uncategorized

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 game changer. 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 through predictive maintenance in mining and IoT mining?

Infinite Uptime offers responsively designed machine diagnostics, remote condition monitoring, and predictive maintenance solutions in diverse industries such as Cement, Steel, Mining and Metals, Tire, Paper, Automotive, Chemicals, FMCG, Oil and Gas, and more. To understand how predictive maintenance applies to your process plant and can help in achieving plant reliability, explore the comprehensive solutions of Infinite Uptime.

Click here to schedule a demo with our team of experts.
FAQs
The mining industry grapples with the high costs of equipment failure, spending up to 50% of operational budgets on maintenance. Remote locations exacerbate downtime as getting personnel and parts to sites is time-consuming and costly. Safety concerns due to equipment health also pose significant risks in hazardous environments.
Predictive Maintenance uses IoT and AI to monitor equipment in real-time, predicting failures before they occur. This proactive approach reduces downtime, extends equipment life, and lowers maintenance costs compared to reactive (fixing after failure) and preventive (scheduled maintenance) strategies.
Predictive Maintenance reduces downtime by 15-20%, enhances productivity by minimizing interruptions, and optimizes production capacity. It lowers maintenance and spare part costs by monitoring equipment continuously and prevents costly breakdowns, thus improving overall operational efficiency.
Mining operations employ Reactive Maintenance (fixing after failure), Preventive Maintenance (scheduled check-ups), Condition-based Maintenance (monitoring performance for signs of wear), and Predictive Maintenance (AI-driven real-time monitoring) to ensure equipment operates efficiently and safely.
IoT enables real-time data collection from mining equipment, allowing for predictive analytics and condition monitoring. This data-driven approach facilitates proactive decision-making, improves operational efficiency, and enhances safety by identifying potential hazards before they escalate.

It’s crucial to select a solution that integrates seamlessly with diverse equipment types and can operate in remote, off-grid locations with limited connectivity. Deployment speed and scalability are also critical to ensure minimal disruption and rapid ROI across large-scale mining operations.

By preemptively identifying equipment issues, Predictive Maintenance helps create safer working conditions in mines, reducing the risk of accidents and environmental hazards. It also supports sustainable practices by optimizing resource use and minimizing operational disruptions.
Categories
Uncategorized

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

How to choose a maintenance solution for your plant ?

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.
FAQs
There are three main types: Reactive Maintenance, which fixes equipment after it fails; Preventative Maintenance, which follows scheduled check-ups to prevent failures; and Predictive Maintenance, which uses AI and IoT to monitor equipment in real-time and predict failures before they occur, reducing costs and downtime.
Predictive Maintenance allows for proactive equipment monitoring, predicting failures based on real-time data. This approach minimizes unplanned downtime, extends equipment life, and optimizes maintenance schedules, resulting in significant cost savings and improved operational efficiency compared to reactive and preventative strategies.
Predictive Maintenance enhances plant productivity by ensuring machines operate at peak efficiency with minimal downtime. It also promotes a safer working environment by preemptively addressing equipment issues, thereby reducing risks to plant workers and assets.
Choose a solution that is easy to use and intuitive for all plant personnel, integrates seamlessly with diverse equipment types and manufacturers, and provides actionable insights rather than just alerts. Ensure the solution offers measurable outcomes in terms of reduced downtime and increased equipment longevity.
IoT enables real-time data collection from sensors embedded in equipment, facilitating predictive analytics and condition monitoring. This data-driven approach allows plant managers to make informed decisions quickly, improving overall plant efficiency and reliability.
Look for a solution provider that offers quick deployment and scalability options. The time taken to implement the solution should be minimal to maintain momentum and ensure rapid ROI. Scalability should also be seamless, allowing for expansion from pilot phases to full plant integration without significant disruptions.