It is seen that in traditional manufacturing, machine availability rates on the production floor were fine-tuned by preventive maintenance. Data generated by shop floor assets are continuously monitored and sifted through machine learning models, which can then closely predict what’s coming down the belt. In fact, today’s lean manufacturing runs on a new brand of Predictive Maintenance which takes past asset performance data and predicts what the future will bring. Such kind of valuable intelligence helps the manufacturing companies to schedule labour only when needed and maximizes asset availability.

The global predictive maintenance market is expected to reach around $23.5 billion by 2024 with an annual growth rate of nearly 40 per cent between 2018 and 2024. “Predictive Maintenance is one step ahead than its preventive counterpart, the processes for both, which involve human labour and factory downtime, are expensive and can significantly undercut a company’s bottom line” says Deepak Pohekar, Executive Director, ZF Wind Power Coimbatore. The suppliers and vendors would prescribe a fixed service schedule for entire fleets of assets and parts would be replaced accordingly whether they needed to be or not.  

Industrial Internet of Things (IIoT)

With the help of Predictive Maintenance, organisations can monitor and test various indicators such as slow bearing speed, lubrication or temperature. Using condition based monitoring and Industrial Internet of Things (IIoT) technology, these tools can detect abnormalities during normal operations and send real time alerts to the machine owner that indicates a potential future failure.  Through the expansion of smart devices across the globe, a large number of customers have found the benefits of Predictive Maintenance to improve the efficiency of their production, the safety of their personnel and the quality of their products.

Temperature Technology

The temperature technology approach to predictive maintenance involves checking the temperature of equipment frequently, which enables the easy tracking of operating conditions. This method may include identifying hot spots in the electronic equipment, identifying fuses that are nearing capacity, locating faulty terminations in electrical circuits and more. Temperature sensors are commonly used in industrial condition monitoring.

Vibration Analysis

The vibration analysis method of condition monitoring looks for and monitors significant changes from a machine’s standard vibration. To increase accuracy, typical vibrations should be recorded multiple times so the deviations are more easily noticed. Sensors can be placed near important element s of the manufacturing process, such as near valves or motors, to easily detect a potential malfunction.

Motor Circuit Analysis

Motor Circuit Analysis is used across a variety of industries, from automotive to the marine industry which as the name implies, measures a motor’s stator and rotor. This analysis is also useful in detecting contamination and ground faults. Motor circuit analysis can test new motor inventory before installation of equipment as well as existing motors for system health.

Advantages of Predictive Maintenance

Reduces Downtime Risk

The aspects of machine cleaning, replacement of risk prone parts, all fall under the broader umbrella of planned downtime. Integrating these practices with the intuitive and data driven model of predictive maintenance allows for greater optimization and reduces the risk of unplanned downtime. Because predictive maintenance relies on the data collected from machine operations, it becomes substantially more convenient to schedule maintenance operations. Subsequently, this plays a pivotal role in extending the lifetime of ageing assets, ensuring that the production witnesses no significant breakdowns.

Nothing plays as much of a role in causing a manufacturing unit’s ruin as that of unplanned downtime. One of the best ways of accomplishing this objective is using predictive maintenance to have proactive maintenance schedules. Predictive Maintenance takes into historical account data to detect patterns that help identify machines that stand the risk of experiencing an outrage shortly.

Equipment Optimization

By effectively eliminating the practices of scheduled maintenance and condition based maintenance, predictive maintenance facilitates the optimization of equipment lifetime by monitoring their output, efficiency and quality at all times. Depending on their life and their frequency of usage, the model allocated different schedules to all the unit’s various operational components.

Greater Work Productivity

There is no need to disrupt worker productivity for an unexpected malfunction or breakdown. Predictive Maintenance plans around workers’ schedules and enables up to 83 per cent faster service time-to-resolution. Predictive Maintenance maximizes uptime, prevents productivity lags as well as increases asset utilization.

By anticipating machine maintenance, service departments can generate major cost savings and increased ROI through reduction of costly service truck rolls. It can also save cost through increased first time fix rates, streamlined maintenance costs through reduced labour, equipment and inventory costs.

Conclusion

By adopting a predictive maintenance strategy, users can mine their critical asset data and identify anomalies or deviations from their standard performance. Such insights can help the users to discover and proactively fix issues days, weeks or even months before they lead to failures. This can help them avoid unplanned downtime, reduce industrial maintenance overspend and mitigate safety and environmental risks.