How Predictive Maintenance is changing Art of Maintenance

Updated: Jan 22

Predictive maintenance, a key aspect of Industry 4.0


Maintenance is a challenging process: Ensure that the machine remains available and reduce repair resource consumption while ensuring that the quality of the product is maintained. It was previously difficult to address all these aspects. The advent of Industry 4.0, on the other hand, has provided new possibilities for predictive maintenance.


A key part of Industry 4.0 includes sensors and cyber-physical systems that allow physical processes to be digitized and integrated with IT-based data-driven automation and control operations. Digital twins, flexible automation, and predictive maintenance are a few aspects of Industry 4.0's industrial applications.


As part of Industry 4.0, huge quantities of digital data are collected regarding the health of machinery and equipment incorporate in maintenance. Data collection is enabled by deploying a variety of sensors, including vibration sensors, sound sensors, temperature sensors, energy usage sensors, and thermal cameras.


What Is Predictive Maintenance?


Every day, we use various machines, but without proper maintenance, they will eventually break down. You can forecast when machines will break down if you use predictive maintenance. By planning ahead, you can prevent unplanned downtime, manage inventories better, manage inventories more effectively, and extend the life of your equipment. By using predictive maintenance, you can anticipate future failures as well as pinpoint problems in complex machinery and determine what needs to be replaced.


In predictive maintenance, system data triggers repair notices. Teams conduct predictive maintenance when sensors, PLCs, and other Internet of Things (IoT) applications indicate that a part or component is failing or is at or near the point of failure. The distinction between predictive maintenance and time-and-usage-based preventive maintenance is that predictive maintenance relies on the real-time component condition, whereas preventive maintenance depends on predetermined standards for component life.


Maintaining your equipment with predictive maintenance gives you more control and relies on less speculation. In order to maximize equipment performance, longevity, and repeatability, factories should combine preventive and predictive maintenance practices.


PdM refers to the practice of conducting maintenance to prevent problems from occurring instead of conducting maintenance on a fixed schedule or when a problem occurs.

Why predictive maintenance is Important?


Industry such as manufacturing, which has costly machinery and depreciation is a significant expense, needs to effectively manage assets. Saving money through predictive maintenance is possible.


Effective asset management, in essence, comes down to predictive maintenance. Despite being older strategies aimed at increasing efficiency, six sigma and lean management offer limited results for businesses today after being used for more than a decade.


Understanding how and why assets fail is essential to the development of a Predictive Maintenance approach, along with recognizing the warning signs of impending problems. Manufacturing environments can also benefit from predictive maintenance by improving their productivity, quality, and efficiency.


With predictive maintenance, all three can be achieved: safety compliance, proactive corrective action, and longer asset life. The asset can be prevented from failing by looking ahead and recognizing when it is likely to fail and undertaking preventive maintenance, schedule adjustments, and repairs.


Predictive Maintenance has the following advantages:


A reduction or elimination of unscheduled equipment downtime due to equipment or system failure; an increase in labour efficiency; an increase in production capacity; a reduction in maintenance costs; and an increase in equipment lifespan.


How does Predictive Maintenance Works?



Predictive Maintenance Workflow
Predictive Maintenance Workflow - source google

Although predictive maintenance is a simple concept, it relies on a variety of sensor data to maintain machine health. Predictive maintenance consists of scheduling work based on the current conditions of an asset. The process of knowing the exact condition of complex assets, on the other hand, does not come easily.


It tracks the condition of assets and alerts technicians to upcoming equipment breakdowns by using three primary components


A condition-monitoring system sends real-time performance data and machine health information


The Internet of Things enables machines, software solutions, and cloud technology to communicate. This allows huge amounts of data to be collected and analyzed

With all that processed data, predictive data models can create failure predictions.

Some of those sensors measure:


  • Temperature

  • Pressure

  • Vibration

  • Rotation speeds

  • Current

  • Chemical properties oil


These sensors can provide an indication of future issues, which creates maintenance work orders for machines whose values are above or below normal. Based on what you plan to track and which assets will be on your PdM, you can choose the sensors you want.


Utilizing condition-monitoring sensors has another benefit in that you can get a very accurate understanding of what is going on inside an asset without affecting productivity. In other words, you do not have to take a physical inspection of an asset apart and stop it.


Condition monitoring and predictive maintenance can be used in a variety of ways depending on which sensors you use and what tests you plan to run.


The ability to monitor these factors in real-time allows for a prompt intervention to correct problems before they develop.

Additionally, time series analysis is useful for detecting outliers. While these variations may appear to be okay when seen in isolation, time series analysis can reveal abnormal trends and anticipate future issues. Through the Internet of Things (IoT), sensors such as those previously mentioned can collect and distribute data. A large part of PdM involves connecting assets to a central system that stores data using these sensors. W-LAN or LAN-based connectivity, as well as cloud technology, are used to power these central hubs.


Depending on how the system is set up, the assets can then communicate, collaborate, analyse data, offer remedial action, or take direct action.


As a result, an automated system has been developed that:


  • Installed sensors to monitor operating conditions.

  • Creates patterns from anomalous data by understanding and predicting it.

  • Monitor deviations from thresholds and create alerts.


What is The ROI of predictive maintenance?


Predictive maintenance can almost eliminate catastrophic equipment failures. Compared to preventive maintenance, savings are 8 to 12 per cent higher; and compared to reactive maintenance, savings are 30 to 40%.


Downtime due to equipment failures is reduced by 50%: Asset breakdowns are costly and stressful. An hour of downtime can cost a corporation millions of dollars in revenue. Downtime can be reduced because difficulties can be predicted ahead of time. For manufacturing or logistics organisations with interconnected machines, increasing uptime is a key challenge.


Increased machine-usable life by 3-5 per cent: Because predictive maintenance avoids machine malfunctions and maintains optimal operation, it can extend the life of machines and robots.


Reduced environmental impact. Companies will waste fewer natural resources as machines last longer and become more efficient as a result of improved analytics. Predictive maintenance is one of the rare programmes that benefit both a company's financial line and its CSR objectives.


Maintenance expenditures will be reduced by 0-40 per cent since planned maintenance is based on a timetable. There will be times when maintenance chores are performed when they are not required. Such inefficiencies can be avoided with predictive maintenance. Furthermore, based on symptoms, predictive maintenance systems alert professionals to the adjustments that need to be made to the system.


10–25% reduction in worker injuries. By combining sensor data with analytic tools, industries will be able to discover new strategies to avoid injuries. Factory conditions can be greatly improved and worker injuries reduced with fewer breakdowns and accident avoidance devices that can alert or even stop machinery when a worker is in danger.


Reduced waste by 10% to 20%. Unidentified sub-optimal operations can result in inefficient production. In such cases, raw materials, energy, labour expenditures, and machine time are all squandered. Predictive maintenance systems can detect faults that could lead to waste before they become a problem.


Advanced analytics. Predictive maintenance requires the collection of sensor data from a variety of machines. Analysts will have a great deal of data to analyse once that data begins to be collected automatically. This information can be utilised to find chances for parameter and process optimization.


Increased client satisfaction and improved product quality: A virtuous cycle of testing and learning is created by detailed sensor data and the capacity to observe the outcomes of interventions. Teams discover ways to increase quality when they modify machine parameters and improve results.


Employee morale has improved. Downtimes and operations with sub-optimal parameters have a negative influence on not only output but also employee morale. Rushing to solve problems as they arise is stressful. Predictive maintenance reduces the likelihood of such occurrences.


Predictive maintenance systems are learning systems, thus their performance improves with time. Based on feedback from technicians or sensors on the shop floor, they construct an implicit knowledge base of issues and comprehend their core causes.

Let’s wrap up


Almost any industry in which large amounts of data are generated and routine maintenance or fine-tuning is required can benefit from predictive maintenance. It has the potential to transform discrete sectors like consumer packaged goods, automotive, electronics, textiles, and aerospace, as well as process industries like food & beverage, chemicals, oil & gas, and pharmaceuticals. The nature of their work relies on data interpretation and can be detached from the intricacies of production machinery, which is why many suppliers are associated with almost any industry.


Companies that haven't yet implemented planned maintenance may not be a good fit for predictive maintenance.


By implementing a pilot project ahead of time, a PdM system implementation will be more rapid. As part of the pilot project, employees will be taught about the system, the security of the system will be tested, and the algorithms will be improved.


Maintenance techniques are shifting from reactive to proactive in many sectors. With advancements in IoT technology and IoT-enabled applications, as well as continued cost reductions, this trend is being accelerated.


It takes time for the PdM strategy to be fully implemented. By analyzing the plant requirements, planning financial and material resources, and training maintenance personnel properly, the implementation challenges can be considerably reduced.


You can achieve Predictive or Preventative Maintenance with program software. These best practices will help you improve your revenue while improving your logistics, lowering your costs, and boosting your profits. And with a solution from DXSolution, it's easier than ever. Please connect with us and get a free consultation. You can follow our LinkedIn page and our hashtag - #dxsolutionadvisor, on LinkedIn.


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