Category: Predictive Maintenance

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Plant Reliability Predictive Maintenance

Predictive Maintenance: Is it the best approach for Greenfield or Brownfield projects?

Predictive-Maintenance-Is-it-the-best-approach-for-Greenfield-or-Brownfield

Predictive Maintenance: Is it the best approach for Greenfield or Brownfield projects?

Predictive-Maintenance-Is-it-the-best-approach-for-Greenfield-or-Brownfield

Digital transformation in the industry entails the creation of connected enterprises wherein machines and people can seamlessly communicate and access relevant information in real time. Intelligently digitized factories are able to achieve plant reliability objectives while maximizing production uptime and maintaining optimal machine health. Where the application of smart automation, robotics, the internet of things (IoT), machine learning (ML) and artificial intelligence (AI) can make this strategically possible in new manufacturing sites, legacy sites often struggle to digitalize key operations.

This evokes the question for many plant and maintenance leaders – is IoT-enabled Predictive Maintenance (PdM) suitable for greenfield (new) projects only or can brownfield (old) projects also successfully adopt it? This article will address this question and deliberate on the suitability of predictive maintenance in both greenfield and brownfield projects.

Predictive Maintenance in Greenfield Projects

To begin with, greenfield projects often have the advantage of factoring in scalable automation and digitalising processes from the off-set. The project plan accounts for such technologies and shop-floor teams are sensitized from the very beginning about the standard digitalized practices. Furthermore, there are no legacy applications to upgrade or distributed assets to integrate, calling for extensive change management. 

This makes it easy for plant and maintenance heads to pursue and achieve plant reliability objectives in brand-new manufacturing sites. A digitally transformed production unit can have:

  • All machines equipped with sensors or integrated chips to monitor ongoing operations
  • Real-time information about asset availability and reliability, accessible through intelligent dashboards
  • Sophisticated data storage and big data analytics to generate strategic insights
  • Established standard operating procedures (SOPs) to manage digitalized equipment with minimal manual intervention
  • Blanket adoption of industrial IoT, AI and machine learning (ML) enabled technologies to support production and maintenance functions
  • Safer work environments with minimal equipment breakdown and unplanned downtime

Greenfield projects also need to prioritise asset maintenance and reliability to ensure that such ideal manufacturing conditions are maintained as the production site matures. A predictive maintenance approach becomes a natural fit and can be easily adopted by: 

  • Outfitting all mission-critical assets for real-time condition monitoring
  • Creating digital twins for all equipment; covering as-designed, as-built, and as-maintained states of the assets
  • Using machine models and simulations to test out OEM investigation and maintenance strategies 
  • Utilizing quality data inputs to generate analytical insights about machine health and performance
  • Access prescriptive maintenance actions formulated by ML across the connected enterprise  
  • Continuously support plant-wide asset improvement, while maintaining high safety standards and minimal human intervention

While all these adaptations and benefits are possible in greenfield projects with the right investment and strategy, brownfield projects can go through a completely different process before transformative digitalization can happen.

Predictive Maintenance in Brownfield Projects

Brownfield projects are often decades old and are typically part of process manufacturing. This means that they run 24×7 and even an hour of downtime can mean thousands of dollars lost in revenue. Halting production in such sites to retrofit legacy equipment with IoT-enabled predictive maintenance and digital reliability solutions poses a big challenge. 

Moreover, the need for digitization is often realized in critical applications before it’s scaled up to all assets. Such phased digitalization creates an ecosystem of software and hardware that is not necessarily synchronized or can be integrated to generate data-backed intelligence. Manual data collection about machine conditions remains a prevalent practice and is conducted by a third-party condition monitoring partner or an in-house CBM team. These teams are also resistant to the adoption of new technology, fearing the obsolescence of their roles. 

All these challenges are compounded by the high cost of retrofitting legacy equipment with the latest technologies and discourage plant heads from actively pursuing more advanced monitoring, diagnostic, maintenance, and asset management solutions. But all is not lost! Brownfield projects can derive greenfield benefits from intelligent automation and a predictive maintenance approach by:

  • Adopting holistic and scalable digital reliability solutions that can be integrated with the existing CRM and OT/IT infrastructure
  • Digitizing critical applications with advanced diagnostic and monitoring sensors to receive real-time information about machine condition
  • Automating information flow and generating analytical intelligence to support maintenance activities, thus minimizing production downtime
  • Training operations and maintenance teams to utilize predictive analytics and prescribed actions before performing OEM investigations and/or replacement of components
  • Pivoting from a preventive maintenance approach towards a predictive maintenance approach to extend the remaining useful life (RUL) of assets and optimize production uptime
  • Determining ROI of digitalization not only in terms of costs saved or downtime avoided but also safer and more sustainable production environment created.

Final Thoughts

In sum, predictive maintenance, digital reliability, and other industry 4.0 technologies are not just limited to greenfield projects. They could be equally, if not more, relevant for brownfield projects operating with older equipment and outdated asset management processes. With the right management outlook, a digitalisation strategy can be devised for old manufacturing sites and systematic automation can be done to optimize production and maintenance processes.

Infinite Uptime’s digital reliability solutions are designed to understand the very specific maintenance needs of process manufacturing plants with crucial assets running uninterrupted for many decades. Tailored monitoring insights and predictive analytics are shared with plant leaders in industries such as Cement, Steel, Mining, Automotive, Tyre, Chemicals, Paper, FMCG, Pharmaceuticals, Glass, Oil & Gas, etc. Our patented vibration analysis technology and syndicated reliability reports allow maintenance leaders in brownfield projects to derive greenfield projects and maximize their plant reliability.

Get in touch with our experts or book a demo now to understand how our solutions fit your plant.

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Predictive Maintenance

Why Predictive Maintenance is gaining adoption in the Paper Industry

Predictive Maintenance is gaining adoption in the Paper Industry

Why Predictive Maintenance is gaining adoption in the Paper Industry

Predictive Maintenance is gaining adoption in the Paper Industry

The pulp and paper industry has been steadily experiencing a decline in demand for graphic papers used for newsprint, printing, and writing. In a digital-first economy, paper can very well be seen as an obsolete commodity and the growth numbers of past years are nothing but strong evidence of the same. However, as some paper applications decline, others are growing. 

Paper packaging is replacing plastics at a fast pace, while specialty paper is gaining popularity in niche markets. Resultantly, thousands of paper production units around the world are adapting their production environments to cater to this transforming demand. There’s an increased focus on optimal utilization of resources, re-integrating waste products into processes, and finding sustainable ways to ensure asset reliability. 

Predictive maintenance can play a pivotal role in these changing industry-wide objectives, and here’s how:

Predictive maintenance in Paper Industry Several rotating equipments are utilized in industrial paper production that requires continuous upkeep and availability. Any undiagnosed machine faults or failures can surmount equipment breakdown and unplanned process downtime. Not only can this mean increased production costs and an unsafe work environment, but it could also mean a disrupted supply chain and unhappy customers.  To manage such contingencies, most paper producers rely on preventive or scheduled maintenance, organizing planned production downtimes to carry out maintenance activities and machine part replacements. While this approach reduces the likelihood of factory floor mishaps or sudden machine breakdowns, it does nothing to reduce production downtime. The preventive maintenance approach also leads to a decrease in the remaining useful life (RUL) of assets under deployment and increases the total maintenance cost.
Predictive-maintenance-in-Paper-Industry-image-infinite-uptime

This is why the paper manufacturing plants of the future are slowly but surely adopting a predictive maintenance strategy and shifting away from preventive maintenance. Predictive maintenance (PdM) is a maintenance approach that uses cloud-enabled technologies to monitor diverse assets involved in paper production in real time. 

With predictive maintenance, maintenance managers and plant heads can proactively track machine health, estimate the maintenance needs of all deployed assets, and support maintenance decision-making.

Benefits of Predictive Maintenance and Digital Reliability Solutions.

Predictive maintenance relies on continual monitoring of equipment conditions through sophisticated technologies that are IoT-enabled. Technologies such as infrared thermography, vibration analysis, oil analysis, and acoustic monitoring are deployed to collect equipment condition data. The data thus collected is analyzed to detect any deviation in asset performance or irregularities that are otherwise impossible to detect without sophisticated equipment.

Benefits-of-Predictive-Maintenance-and-Digital-Reliability-Solutions-image-infinite-uptime

The adoption of PdM in paper production contributes toward digital reliability objectives and delivers the following benefits to maintenance and operation teams:

  • Maintenance activity is highly targeted in predictive maintenance and only performed when a fault or anomaly is detected. Routinely organized production downtimes and pre-emptive part replacements are not required.
  • PdM is increasingly efficient over time and reduces the labor-intensive nature of maintenance activities. Predictive maintenance is also more cost-efficient, reducing the inventory carrying burden for spare parts.
  • PdM dashboards also offer machine health insights with digital twin technology, supporting intelligent decision-making for maintenance planning and procurement of machine parts. Consequently, the net cost of assets is effectively reduced as the complete useful life of every machine is utilized before any replacement is done.
  • The likelihood of unplanned reactive maintenance is reduced by 70-90% with a predictive approach. Since equipment faults are diagnosed well before the actual functional failure, organized maintenance events can help avoid process disruption and reactive efforts.
  • Critical equipment problems and underlying causes of diminishing productivity can be accurately identified with predictive maintenance. As a result, a higher network system reliability can be achieved in a cost-efficient manner.

Critical PdM Applications in Paper Industry

Given the numerous benefits of predictive maintenance, its adoption across the paper and pulp industry seems only natural. But with cost optimization as an important driving force for all strategic planning within the sector, it’s important to identify the critical applications of predictive maintenance and plan a systematic adoption.

The following equipment and machine groups that play a significant role in the paper production process are the most important applications of predictive maintenance solutions in the paper industry:

Critical Applications of predictive maintenance in Paper Industry

Several machine groups utilized in paper production have a complex design, making manual monitoring and intervention difficult. Therefore, for each equipment application, the digitization points need to be carefully identified and validated for condition monitoring. With real-time vibration monitoring and predictive analytics, commonly occurring faults can be proactively diagnosed, such as:

  • Misalignment
  • Unbalance
  • Bearing faults
  • Structural Looseness
  • Gear Defects
  • Friction (lack of lubrication)

The maintenance teams can then organize OEM inspections on focused locations and fix or replace machine components that are suffering from a particular fault. The resultant reduction in unplanned downtime and improved machine health can translate into a stronger bottom line and help paper manufacturers develop a true competitive advantage.

Final Thoughts

In conclusion, for the paper industry, automation and digitization initiatives have to be driven by the ultimate objective of achieving better cost efficiencies and operational excellence. Predictive maintenance can significantly reduce production and maintenance costs and improve process hygiene across any paper production unit. 

With real-time data analytics and access to intelligent insights about machine conditions, PdM can empower maintenance and operation teams more than ever. This is precisely why; global paper manufacturers are adopting predictive maintenance solutions for making their plants more reliable and efficient. 

Infinite Uptime’s digital reliability solutions suit specific industry needs and provide advanced analytics to support maintenance and minimize unplanned downtime. 

Get in touch with our experts or book a demo now to understand how our solutions fit your plant.

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Predictive Maintenance

Top Manufacturing Trends of 2022

Top_Manufacturing_Trends_of_2022

Top Manufacturing Trends of 2022

Top_Manufacturing_Trends_of_2022
The last two years have been nothing less than a vigorous shake-up to manufacturing. From kickstarting an industry-wide awakening to digital transformation and remote operations for a decade, manufacturing has seen nothing less than a paradigm shift. It has also brought the spotlight on the criticality of this sector for the global economy & those who work tirelessly day & night in factories.

In 2022, we expect all of these trends to rise. In the past two years, the challenges experienced in industrial operations have led to a complete introspection of the entire manufacturing operations cycle, including supply chain management & asset maintenance. The results gradually create a need for a fundamental change to protect the bottom line while staying agile & resilient to any future challenges.

Here are some top trends for Manufacturing in 2022:

1. The Remote Monitoring shift: It’s not that remote monitoring has not existed for Manufacturing, but it was the choice of a few digitally aware pioneers. But intermittent surges of the pandemic, with its restrictions of social distancing, workforce shortages, and government regulations, have made remote monitoring mandatory for Manufacturing. The hybrid or remote working setup is not a temporary shift anymore.

With remote work becoming the new normal, investing in technologies like IoT that make remote monitoring happen is now essential for digital reliability & empowering your maintenance team to stay prepared for all circumstances. A platform like Infinite Uptime’s Industrial Data Enabler (IDE), a patented edge-computing Vibration monitoring system, can point out any anomalies in the machines long before they become critical. Accessing the correct data to the relevant people in real-time is no less than a game-changer. Here is how remote monitoring can help your facilities.
2. Connected Supply Chains: Supply chains were hit in the worst way due to the globe-wide nature of the pandemic. Unpredictable & sudden shortages globally, coupled with lockdowns, made inventory management very difficult. While businesses are trying to make their supply chains agile & localized, digital technologies like AI, ML & IoT are helping solve this via connected supply chains. Here are two ways how they can help:

  • By solutions that can predict the inventory needs in advance and enable real-time tracking of shipments, optimizing delivery timings
  • An intelligent Predictive Maintenance solution that ensures a consistent & reliable asset performance can predict the flow of raw materials products from supplier & the end product to the customers due to fewer breakdowns or disruptions.
3. Skill gap & rise of Prescriptive Analytics: With increasing competition & economic growth, shortage of skilled workforce is one of the biggest challenges for manufacturers globally. In addition to this, the retirement of baby boomers with years of information & tribal knowledge without a proper knowledge transfer can lead to huge errors on the shop floor. That is why today, your digital reliability system needs to be prescriptive, not just predictive. Not only does the anomaly in the machine need to be predicted, but a recommendation based on past data also needs to be suggested to the plant operator. Only then can manufacturing analytics become truly actionable in time.
4. Sustainability beyond compliance: With the rise in global initiatives around climate change and sustainability, every industry has been experiencing the impact. Manufacturing has not been any different. Sustainability for Manufacturing comes in various forms- for operations, energy footprint, water and packaging to reduce waste and carbon emissions. For the first two, reliable assets are the key. A well-functioning asset that is always available & performing well will not result in higher energy overheads. On top of this, sudden malfunctions or stoppages without an established protocol in the case of industries like chemicals, oil & gas, etc., can result in harmful emissions in the atmosphere.

Conclusion

The past two years have reiterated the importance of a data-driven approach coupled with automation and the importance of a healthy energy footprint, workers and machines. 2022 will see manufacturers globally investing in these trends and using innovative technologies to bridge the gap between today’s setup and industry 4.0.

About Infinite Uptime

Infinite Uptime is transforming the industrial health diagnostics space with a Digital First approach. We provide comprehensive solutions around Machine Diagnostics, Predictive Maintenance and Condition Monitoring to the top engineering and process industries globally. We promise to deliver maximum Machine Uptime, minimize Factory Disruption and elevate Equipment Reliability for a stellar factory performance.

Infinite Uptime leverages IoT, machine learning, artificial intelligence, smart communications, cloud computing, analytics and data science techniques to accelerate digital adoption and turn Industry 4.0 into a business reality. To know more about us and our customer success stories, please visit www.infinite-uptime.com or write to contact@infinite-uptime.com.

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Predictive Maintenance

Understanding Predictive Maintenance As a Service

Understanding-Predictive-Maintenance-as-a-Service

Understanding Predictive Maintenance As a Service

Understanding-Predictive-Maintenance-as-a-Service

Asset maintenance in discreet and process manufacturing is a key priority – one that can have a multiplying effect on plant reliability and bottom line. For industries where even a single hour of production downtime due to asset unavailability can translate into millions of dollars of losses, strategic investments are made to make maintenance more effective. Maintenance management has thus, evolved into a complex field with application of varied digital and analytics solutions to improve efficacy while reducing costs.

Predictive maintenance is a result of this pragmatic evolution and has helped manufacturers save billions of dollars through increased Remaining Useful Life (RUL) of assets and minimized production downtime. By 2025, the predictive maintenance market is expected to grow to USD 25 billion, creating unparalleled value across global industrial value-chains. This article will cover what predictive maintenance is, how it has evolved to suit industry needs, and how it applies in various manufacturing setups.

 

What is Predictive Maintenance?

Predictive maintenance (PdM) is a maintenance approach which entails use of cloud-enabled technologies to monitor diverse assets involved in production and estimate the maintenance needs on the basis of asset condition and detected anomalies. Condition Based Monitoring (CBM) is the underlying concept, which is used to monitor industrial assets in real time. Technologies such as infrared thermography, vibration analysis, oil analysis, and acoustic monitoring are deployed to collect equipment condition data.

The data thus collected is analysed to detect any deviation in asset performance or irregularities which are otherwise impossible to detect without sophisticated equipment. But predictive maintenance doesn’t just stop at detection of performance issues. With the help of edge diagnostics and predictive analytics, the underlying causes behind poor asset performance are determined. Predictive maintenance solutions can also forecast the time when a monitored equipment will breakdown, if corrective maintenance measures are not taken.

Historically, industrial maintenance has evolved from being reactive to predictive.

What-is-Predictive-Maintenance-banner-infinite-uptime

This evolution has been driven by an increased focus on Overall Equipment Effectiveness (OEE) and the need to reduce unplanned production downtime. While manufacturing plants no longer rely on a reactive maintenance approach, preventive or scheduled maintenance is the most popular among undigitized setups. Since a preventive approach can deliver 50%-75% OEE on an average, maintenance leaders hesitate to shift away from it towards a predictive maintenance approach. However, understanding the key differences between both practices can help.

How is Predictive Maintenance Different from Preventive Maintenance?
Predictive-Maintenance-Different-from-Preventive-Maintenance

Preventive Maintenance

  • Preventive maintenance is essentially a periodic and schedule-based maintenance approach that uses asset performance history and routine equipment inspections to plan maintenance events.
  • Irrespective of the machine condition and the remaining useful life of the asset, maintenance and replacement activity is performed. 
  • Preventive maintenance is usually labour-intensive and not very cost-efficient. Maintenance teams have to maintain inventory of equipment spare parts and schedule planned downtimes to execute maintenance.
  • The net cost of asset is increased as over the period of its usage, several parts are replaced and consumables are used to keep the asset running optimally.
  • Latent problems that may develop in-between inspection schedules could go un-noticed and remain un-addressed. As a result, the dependence on reactive maintenance increases.

Predictive Maintenance

  • Predictive maintenance relies on continual monitoring of equipment condition through sophisticated technologies that are IoT-enabled.
  • Maintenance activity is highly targeted in predictive maintenance and only performed when a fault or anomaly is detected. Routinely organized production downtimes and pre-emptive part replacements are not required.
  • PdM is increasingly efficient over time and reduces the labour-intensive nature of maintenance activities. Predictive maintenance is also more cost-efficient, reducing the inventory carrying burden for spare parts.
  • Net cost of assets is effectively reduced as the complete useful life of every machine is utilized before any replacement is done.
  • The likelihood of unplanned reactive maintenance is reduced by 70-90% with a predictive approach. Since equipment faults are diagnosed well before the actual functional failure, organized maintenance events can help avoid process disruption and reactive efforts.
  • Critical equipment problems and underlying causes of diminishing productivity can be accurately identified with predictive maintenance. As a result, a higher network system reliability can be achieved in a cost-efficient manner.

To better understand how both the approaches can have a distinctive impact on the maintenance organization, here’s an example. A single high-pressure roller press can support the production of a 2MTPA cement grinding plant. If it breaks down due to undetected anomalies or fault in its planetary gearbox, it can result in a potential downtime of 24-48 hours. Now, preventive maintenance measures allow plant maintenance staff to schedule regular asset maintenance events, irrespective of the machine condition and abnormalities which may be affecting productivity.

On the other hand, with a predictive maintenance approach, not only can such anomalies be detected in advance, but such critical events can also be avoided with prescriptive measures. (Read the full case here.)

How does Predictive Maintenance Work? While predictive maintenance is applicable for both discreet and process manufacturing setups, its adoption must follow a strategic workflow. The following process or workflow can help manufacturing maintenance teams effectively harness the advantages of predictive maintenance:

To begin with, all assets available in a production environment must be evaluated to determine how mission critical they are and whether they should be proactively maintained. Once the appropriate machine groups are identified, they can then be outfitted with the cloud-enabled predictive maintenance solutions. These solutions could offer edge-diagnostics with the help of vibration analysis and condition monitoring, but before fault diagnosis can happen, it is important to define performance parameters for assets being monitored.

These performance parameters can include baseline metrics which would be considered as the norm, and continual monitoring would not lead to unnecessary false positive notifications. The assets can then be allowed to operate as per their pre-defined schedule and requirement. Meanwhile, real-time performance data and machine health indicators can be collected and stored on cloud. Advanced predictive analytics can diagnose data anomalies and generate insights regarding potential faults in the monitored assets.

These insights can empower maintenance teams to schedule maintenance activities with minimal production downtime and costs.

 Conclusion:

In sum, predictive maintenance as a service has evolved with the core objective of making industrial asset management more efficient and less costly. Data accessibility and intelligent analytics have shaped the service into a powerful enabler that allows maintenance and operations teams drive plant reliability objectives. While predictive maintenance adoption has to be strategic and supported by change management, its benefits are quickly felt and undeniable for even the most complex production units.

Infinite Uptime offers responsively designed 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, explore the digital reliability [PS1] and asset management solutions of Infinite Uptime.