Category: Predictive Maintenance

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Predictive Maintenance

Condition-Based Maintenance vs. Predictive Maintenance: A Comprehensive Comparison

Condition-Based Maintenance vs Predictive Maintenance: A Comprehensive Comparison

Condition-Based Maintenance vs Predictive Maintenance: A Comprehensive Comparison

Condition-Based Maintenance vs Predictive Maintenance: A Comprehensive Comparison
In the ever-evolving world of industrial operations, maintenance strategies play a crucial role in ensuring equipment reliability and operational efficiency. Among the various approaches, Condition-Based Maintenance (CBM) and Predictive Maintenance (PdM) are two prominent strategies that are often discussed. Understanding the differences and applications of each can help organizations choose the right strategy to optimize their maintenance efforts. This article explores the key aspects of Condition-Based Maintenance and Predictive Maintenance, highlighting their differences, benefits, and best-use scenarios.

Condition-Based Maintenance (CBM)

Definition : Condition-Based Maintenance (CBM)

Condition-Based Maintenance (CBM) is a maintenance strategy where actions are taken based on the actual condition of equipment rather than on a fixed schedule. CBM involves monitoring the performance and health of equipment in real-time to determine the appropriate time for maintenance interventions.

Key Characteristics

Real-Time Monitoring : CBM relies on real-time data collected from various sensors and monitoring tools to assess the condition of machinery.
Reactive Approach : Maintenance is performed when certain parameters, such as vibration, temperature, or pressure, indicate that equipment is not operating within its normal range.
Threshold-Based : CBM involves setting thresholds or limits for specific parameters. Maintenance actions are triggered when these thresholds are breached.

Benefits

Reduced Downtime : By addressing issues only when they arise, CBM helps in minimizing unnecessary maintenance activities and reducing overall downtime.
Cost Efficiency : Maintenance costs can be optimized by performing interventions only when necessary, avoiding the expense of routine maintenance.
Extended Equipment Life : Timely maintenance based on equipment condition can help in preventing severe damage and extending the life of machinery.

Limitations

Reactive Nature : CBM may still lead to unexpected failures if the condition parameters are not effectively monitored or if sudden changes occur.
Limited Insight : CBM provides information on the current state of equipment but may not offer insights into future potential issues.

Predictive Maintenance (PdM)

Definition : Predictive Maintenance (PdM)

Predictive Maintenance (PdM) is a proactive maintenance strategy that uses data analytics and advanced algorithms to predict when equipment is likely to fail. By analyzing historical and real-time data, PdM aims to identify potential issues before they lead to equipment breakdowns.

Key Characteristics

Data-Driven : PdM relies on sophisticated data analytics, machine learning, and historical data to forecast equipment failures and schedule maintenance.
Proactive Approach : Maintenance is performed based on predictions of potential failures, allowing for planned interventions before issues become critical.
Trend Analysis : PdM involves analyzing trends and patterns in equipment data to predict future performance and potential problems.

Benefits

Minimized Downtime : By predicting failures before they occur, PdM helps in scheduling maintenance activities at the most convenient times, reducing unplanned downtime.
Enhanced Reliability : PdM provides deeper insights into equipment health, enabling more accurate and effective maintenance strategies.
Optimized Maintenance Scheduling : Maintenance activities can be scheduled based on predicted needs, reducing unnecessary maintenance and improving operational efficiency.

Limitations

High Initial Investment : Implementing PdM requires investment in advanced technologies, data analytics tools, and sensor systems.
Complexity : PdM systems can be complex to set up and require ongoing management and analysis to ensure accuracy and effectiveness.

Comparison and Best Use Cases

Maintenance Strategy : CBM is best suited for environments where monitoring equipment condition in real-time is feasible and where maintenance needs are relatively straightforward. PdM, on the other hand, is ideal for complex systems where predicting potential failures can significantly enhance reliability and reduce costs.
Cost Considerations : CBM typically involves lower upfront costs but may result in higher maintenance costs over time. PdM requires a larger initial investment but can lead to greater cost savings and efficiency improvements in the long run.
Complexity and Implementation : CBM is generally easier to implement and manage, while PdM involves more sophisticated technology and data analysis, requiring specialized expertise.

Here's a comparative table outlining the differences between Condition-Based Maintenance (CBM) and Predictive Maintenance (PdM):

Aspect Condition-Based Maintenance (CBM) Predictive Maintenance (PdM)
Definition Maintenance based on the actual condition of equipment. Maintenance based on predictions of future equipment failures.
Approach Reactive; maintenance is performed when equipment condition exceeds predefined thresholds. Proactive; maintenance is scheduled based on predicted future failures.
Data Collection Real-time monitoring through sensors and data collection tools. Advanced data analytics using historical and real-time data.
Maintenance Triggers Based on threshold breaches or deviations in real-time data. Based on predictive algorithms and trends in data.
Technology Used Basic sensors and monitoring systems. Advanced analytics, machine learning, and IoT sensors.
Cost Lower initial investment; ongoing costs based on maintenance activities. Higher initial investment; potential for greater long-term savings.
Complexity Generally simpler to implement and manage. More complex, requiring sophisticated setup and ongoing analysis.
Downtime Potential for unplanned downtime if condition thresholds are not timely detected. Minimizes unplanned downtime by predicting and addressing issues before they occur.
Insight Provides information on current equipment condition. Offers insights into future performance and potential issues.
Maintenance Schedule Reactive; maintenance is performed as needed based on equipment condition. Proactive; maintenance is planned and scheduled based on predictions.
Error Detection Based on real-time condition data and threshold breaches. Based on trend analysis and predictive models.
Implementation Time Quicker to implement due to less complexity. Longer setup time due to advanced technology and analysis.
Impact on Equipment Life Extends equipment life by addressing issues as they arise. Potentially extends equipment life by preventing severe issues before they occur.
Workforce Training Less intensive; focused on monitoring and responding to condition data. More intensive; requires understanding of predictive analytics and data interpretation.
This table provides a clear comparison of the two maintenance strategies, helping organizations understand the key differences and make informed decisions based on their specific needs and operational contexts.
Conclusion
Both Condition-Based Maintenance and Predictive Maintenance offer valuable benefits and can be effective strategies for improving equipment reliability and operational efficiency. The choice between CBM and PdM depends on various factors, including the complexity of the equipment, budget constraints, and the specific needs of the organization. By understanding the differences and applications of each strategy, businesses can make informed decisions to optimize their maintenance practices and achieve better operational outcomes.

At Infinite Uptime, we specialize in advanced Predictive Maintenance solutions that integrate Condition-Based Maintenance strategies, along with Fault Diagnostics and Machine Health Monitoring. Our state-of-the-art diagnostics and analytics tools enhance equipment reliability, minimize downtime, and drive operational excellence. With a presence in Georgia, USA; Dubai, UAE; and Pune, India, we are well-positioned to support your global maintenance needs. To learn more about how we can assist with your maintenance requirements, visit www.infinite-uptime.com or contact us at contact@infinite-uptime.com.

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

Understanding Types of Vibration and Measurement in Predictive Maintenance

Understanding Types of Vibration and Measurement in Predictive Maintenance By Infinite Uptime

Understanding Types of Vibration and Measurement in Predictive Maintenance

Understanding Types of Vibration and Measurement in Predictive Maintenance By Infinite Uptime
In the realm of industrial operations, understanding vibrations is crucial for effective predictive maintenance (PdM). At Infinite Uptime, we leverage cutting-edge technology to monitor vibrations, ensuring that your equipment operates at peak efficiency. This article delves into the various types of vibrations, their measurement, and how they relate to our services.

Types of Vibration

Vibrations can be categorized into several types, each providing insights into the condition of machinery:
01 01. Free Vibration :
Occurs when a system oscillates without external forces after an initial disturbance. The natural frequency determines how long the system continues to vibrate.
02 02. Forced Vibration :
Happens when an external force continuously acts on a system, such as an imbalance in rotating machinery. Understanding forced vibrations is crucial for diagnosing equipment issues.
03 03. Damped Vibration :
Involves energy dissipation over time, reducing amplitude. Damped vibrations are common in systems designed to minimize oscillations, such as vehicle suspensions.
04 04. Transient Vibration :
A short burst of vibrations caused by sudden changes, such as equipment start-up or impact forces. Monitoring transient vibrations can help in early fault detection.
Understanding these vibration types is essential for effective predictive maintenance, as each can signal different underlying issues within machinery.

Vibration Measurement Units

Vibrations are quantified using several measurement units, typically focusing on displacement, velocity, and acceleration:
Displacement (mm) : Refers to the distance a vibrating object moves from its rest position. It's often measured in millimeters (mm) and provides insight into the severity of vibrations.
Velocity (mm/s) : Indicates how fast the displacement is occurring over time. Velocity measurements can help identify issues related to imbalances or misalignments in machinery.
Acceleration (m/s²) : Reflects the rate of change of velocity, highlighting sudden changes in vibration that may indicate faults. This measurement is vital for capturing transient events.

Vibration Measurement Techniques

Effective vibration monitoring involves advanced measurement techniques, such as:
Piezoelectric Sensors : These sensors convert mechanical vibrations into electrical signals, allowing continuous monitoring of equipment health. At Infinite Uptime, our piezoelectric sensing technology operates 24/7, capturing data every six seconds to ensure prompt diagnosis.
Accelerometers : Used to measure the acceleration of vibrations, providing valuable data for analyzing the dynamic behavior of machinery.
Data Analysis Algorithms : Our proprietary algorithms analyze vibration data to deliver actionable insights, ensuring that any potential issues are addressed before they lead to downtime.

The Role of Vibration Analysis in Predictive Maintenance

Vibration analysis is integral to our predictive maintenance approach. By continuously monitoring vibrations, Infinite Uptime can:
Diagnose Equipment Health : Our AI-driven diagnostics accurately identify faults and their severity, allowing for timely interventions.
Reduce Downtime : With advanced analytics, we prevent over 48,000 hours of potential downtime, enhancing operational reliability across industries such as steel, automotive, and pharmaceuticals.
Optimize Maintenance Costs : Our predictive maintenance model, requiring zero capital investment, helps reduce maintenance costs by up to 27%, extending asset life and improving overall productivity by 22%.
Conclusion

Understanding vibration types and measurement is critical for effective predictive maintenance. At Infinite Uptime, a leading predictive maintenance company, with operations in India and the USA, we harness this knowledge through advanced monitoring technologies and machine diagnostics, ensuring that your equipment operates reliably and efficiently. By choosing us, you gain access to unparalleled expertise and innovative solutions tailored to your needs. Our approach supports your digital transformation journey, driving your operations toward greater efficiency and profitability.

Get in touch with our experts or book a demo now to understand how our solutions fit your cement plant.
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Plant Reliability Predictive Maintenance

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

Three men in hard hats looking at a tablet in an industrial factory

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

An industrial facility featuring numerous machines and equipment, emphasizing the integration of automation 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. 

Aerial view of power plant and river. Predictive Maintenance in Brownfield Projects.

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.

FAQs
Greenfield projects benefit from Predictive Maintenance due to their ability to incorporate IoT-enabled technologies and digital solutions from the outset. This allows for seamless integration of sensors, real-time monitoring, and data analytics, ensuring optimal machine health and minimal downtime right from the start. These projects set a foundation for efficient operations with minimal manual intervention and high reliability.
Brownfield projects, often characterized by older infrastructure and legacy equipment, face challenges in retrofitting IoT and predictive maintenance technologies. The phased approach to digitalization, high retrofitting costs, and resistance to change among existing maintenance teams hinder the adoption of advanced predictive maintenance strategies initially. However, with strategic planning and investment, brownfield sites can gradually transition to benefit from predictive maintenance.
Despite initial challenges, brownfield projects can leverage Predictive Maintenance by focusing on digitizing critical applications first. By integrating advanced sensors for real-time monitoring and adopting predictive analytics, these projects can optimize maintenance schedules, reduce downtime, and extend asset lifespan. Training existing teams to utilize predictive insights and gradually shifting from reactive to proactive maintenance strategies are essential steps in this process.
Digital twin technology plays a crucial role in both greenfield and brownfield projects by creating virtual replicas of physical assets. For greenfield projects, digital twins facilitate optimal design and maintenance planning from the outset. In brownfield projects, digital twins help simulate operational scenarios, enabling predictive analytics to enhance decision-making and optimize maintenance activities, ultimately improving overall reliability.
Predictive Maintenance supports sustainability in manufacturing by minimizing unplanned downtime, reducing energy consumption through optimized operations, and enhancing resource efficiency. By preventing unnecessary maintenance and improving asset performance, Predictive Maintenance helps manufacturers achieve environmental sustainability targets while maintaining production efficiency.
Long-term benefits include improved operational efficiency, reduced maintenance costs, extended asset lifespan, and enhanced safety. In greenfield projects, these benefits start early and are integrated into operational norms, while brownfield projects achieve these benefits gradually as they overcome initial barriers and integrate digital reliability solutions into existing infrastructure.
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Predictive Maintenance

Why Predictive Maintenance is gaining adoption in the Paper Industry

A paper industry factory with numerous machines and machinery

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.

A man in an orange hard hat and safety glasses operates a machine, emphasizing the importance of safety in industrial maintenance

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.

A person in gloves working on a roll of paper. Image relates to Benefits of Predictive Maintenance and Digital Reliability Solutions

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:

Predictive maintenance applications in paper industry: optimizing costs, enhancing efficiency, and ensuring systematic adoption

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.

FAQs
The paper industry is shifting to Predictive Maintenance because it allows for real-time monitoring of equipment using IoT-enabled technologies like vibration analysis and thermal imaging. This proactive approach helps detect potential failures before they occur, minimizing downtime and reducing maintenance costs compared to reactive or preventive maintenance.
Predictive Maintenance optimizes maintenance activities by focusing on actual equipment health rather than scheduled interventions. This leads to reduced downtime, lower operational costs, extended asset lifespan, and improved overall productivity. Additionally, it enhances safety by preventing unexpected equipment failures.
By minimizing unplanned downtime and optimizing resource utilization, Predictive Maintenance supports sustainable practices in paper manufacturing. It helps in reducing energy consumption, optimizing material usage, and ensuring efficient production processes, which are critical for meeting environmental goals.
Digital twin technology creates virtual models of physical assets and processes. In Predictive Maintenance, these digital twins simulate real-time equipment conditions and performance. By analyzing data from these models, maintenance decisions become more accurate and timely, optimizing operational efficiency and reliability.
Critical equipment such as rollers, turbines, pumps, and conveyor systems in paper production benefit significantly from Predictive Maintenance. These machines often operate under harsh conditions and require continuous monitoring to detect faults like misalignment, bearing defects, or structural issues before they escalate.
Predictive Maintenance enables paper manufacturers to improve their production efficiency, reduce costs, and enhance product quality consistently. By minimizing downtime and maximizing equipment uptime, manufacturers can meet customer demands more effectively and maintain a competitive edge in the market.
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Predictive Maintenance

Top Manufacturing Trends of 2022

Image of steel workers in a factory, showcasing 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.

FAQs
Machine diagnostics in manufacturing refers to the process of monitoring and analyzing machine conditions to detect faults or potential breakdowns. It involves collecting data through techniques like vibration analysis or spectral analysis to pinpoint the root cause of issues affecting machine health.
Machine diagnostics helps manufacturing industries by minimizing downtime through early fault detection and predictive maintenance. By identifying issues before they escalate, maintenance teams can plan proactive repairs, thereby optimizing asset performance and reducing operational costs.
Remote monitoring involves continuously monitoring machine conditions in real time using IoT-enabled technologies. It allows maintenance teams to access data from anywhere, facilitating proactive maintenance decisions based on real-time insights. Remote monitoring enhances the effectiveness of machine diagnostics by ensuring comprehensive and continuous visibility of asset health.
Common techniques include vibration analysis, oil analysis, electrical analysis, ultrasonic analysis, and infrared thermography. These methods help in detecting anomalies such as vibrations, contamination levels, electrical irregularities, and temperature changes, which are indicative of potential machine faults.
Remote condition monitoring offers benefits such as enhanced safety for maintenance teams, as potential hazards can be identified remotely. It also supports cost-efficient operations by enabling predictive maintenance strategies based on real-time data, thereby improving overall plant reliability and reducing unplanned downtime.
By continuously monitoring machine health and diagnosing faults early, these technologies help in maintaining optimal asset performance. Predictive maintenance driven by machine diagnostics and remote monitoring ensures that maintenance activities are proactive rather than reactive, leading to improved operational efficiency and extended equipment lifespan.
Industries such as Cement, Steel, Mining and Metals, Tire, Paper, Automotive, Chemicals, FMCG, Oil and Gas, among others, can benefit significantly from these solutions. The ability to monitor critical equipment remotely and predictively address maintenance needs is crucial for enhancing productivity and reliability across diverse industrial sectors.

Key features include real-time data accessibility, predictive analytical insights, and responsive design tailored to meet the specific needs of process plants. Infinite Uptime’s solutions enable seamless integration of machine diagnostics and remote monitoring into existing operational frameworks, ensuring continuous improvement in asset performance and reliability.

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Predictive Maintenance

Understanding Predictive Maintenance As a Service

Two workers in blue overalls standing by massive turbine engines

What is Predictive Maintenance (PdM) and how does it differ from Preventive Maintenance? Discover how Predictive Maintenance works!

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(PdM)?

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.

Steel workers in a factory - IIoT-based predictive maintenance

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?

A man working on a machine in a factory

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.

FAQs
Predictive maintenance utilizes IoT-enabled technologies to monitor equipment condition in real-time, allowing maintenance teams to detect potential issues before they cause failures. This proactive approach aims to maximize asset lifespan and minimize unplanned downtime, improving overall operational efficiency and reducing maintenance costs.
Predictive maintenance has evolved from reactive and preventive maintenance approaches to a data-driven strategy that uses advanced analytics and real-time monitoring. It enables maintenance teams to predict equipment failures, optimize maintenance schedules, and enhance plant reliability in manufacturing environments.
Adopting predictive maintenance reduces unplanned downtime by 70-90%, lowers maintenance costs, extends equipment lifespan by maximizing its useful life, and improves overall production efficiency. It also enables more informed decision-making and reduces reliance on reactive maintenance practices.
Predictive maintenance differs from preventive maintenance by monitoring equipment condition in real-time and performing maintenance only when necessary based on data-driven insights. Unlike preventive maintenance, which relies on scheduled intervals, predictive maintenance minimizes unnecessary maintenance activities and optimizes resource utilization.

Predictive maintenance enhances plant reliability by detecting equipment faults early, diagnosing underlying issues accurately, and enabling proactive maintenance actions. This approach reduces the risk of unplanned downtime, improves equipment uptime, and ensures smoother operations in manufacturing plants.