Predictive Maintenance: A Game Changer in Asset Management
Maintenance is an essential aspect of the manufacturing industry that ensures the smooth functioning of the equipment and reduces downtime. In the past, the industry relied on two primary equipment maintenance strategies: reactive and preventive. Reactive maintenance is simply fixing things when they break down; however, that concept is nearly inconceivable with today’s complex equipment. The next strategy is preventive maintenance, which involved following a scheduled maintenance program. While preventive maintenance strategy kept the downtimes in check, the cost kept sprawling as maintenance activities were performed whether or not it is actually needed.
What the industry needs now is moving beyond the ideas of reactive and preventive maintenance to predictive and proactive maintenance strategies. Predictive maintenance is a forward-thinking approach to equipment upkeep that combines data analytics and machine learning approaches to foresee when equipment is probable to break down. Predictive maintenance uses sample data to allow for coordinating maintenance programs to predict and respond to equipment failures before they occur. The benefits of this approach include minimizing equipment downtime while also reducing maintenance costs by eliminating unnecessary scheduled maintenance. The key is performing the right tests on the right equipment at the right time - predicting when maintenance is needed.
What makes predictive maintenance so important?
Predictive Maintenance allows for safety compliance, pre-emptive corrective actions, and increased asset life. By looking ahead, and knowing what failure is likely to occur when, pre-emptive investigations, maintenance schedule adjustments, and repairs can be performed before the asset fails.
Combining machine data with digital twin models having powerful 3D visualization and multi-physics capabilities, the predictive actions can be refined further for greater accuracy and reliability.
Benefits of Predictive Maintenance include:
● Reduction or near elimination of unscheduled equipment downtime caused by equipment or system failure.
● Increased labor utilization.
● Increased production capacity.
● Reduced maintenance costs.
● Increased equipment lifespan.
● Improved Safety and Environmental Compliance.
Now that we've learnt about what predictive maintenance is and its benefits, what are the various monitoring and analysis techniques that can power your predictive maintenance ?
Predictive Maintenance Technologies
Here are some of the techniques that can enable predictivity in maintenance:
Vibration Analysis:
Sensors are used to gather information about the frequency of machine and equipment vibration. The data can be analyzed to find any unusual vibration patterns that might point to a problem or failure.
Condition Monitoring:
To gather an overall information about the functionality and condition of equipment, sensors, and monitoring tools are used. The gathered information is examined to look for any alterations or patterns across various critical parameters that might point to a problem or failure.
Infrared Thermography:
Equipment temperature changes are monitored by infrared cameras. Any unusual temperature patterns that might point to a problem or failure are found by analyzing the data that has been collected.
Oil Analysis:
Analyzing oil samples from equipment to look for contaminants, metal fragments, or other signs of potential problems or failures.
Ultrasonic Testing:
To detect any potential issues or equipment failures, ultrasonic sensors are used. The data is analyzed after it has been gathered to look for any alterations or anomalies that could indicate a potential issue.
Predictive Modeling:
An equipment failure prediction model is created using historical data and machine learning algorithms. As more data is gathered, the model is continually updated.
Keys to implementing a successful predictive maintenance strategy
Successful Predictive Maintenance implementation requires organizations to have a clear understanding of their objectives, understanding of the equipment and their parameters. It also needs investment in the appropriate technology, a solid data infrastructure, and last but not the least, a data-driven culture.
Is predictive maintenance strategy easy to implement? Might not be. Is it doable? Most certainly. Read our next post in this series to know how predictive maintenance works, the challenges we face, and how we can tackle them for a success in predictive maintenance.
In the meanwhile, if you have any questions or if you are looking to start your predictive maintenance journey, our experts are here to help.
