Predictive Maintenance Machine Learning

The Benefits of Using Machine Learning to Predict Maintenance

Condition-based monitoring and predictive maintenance help organizations across all industries, such as transportation, energy, aviation, unmanned aerial vehicles, wind turbines and more, to anticipate maintenance needs by pinpointing how and when equipment or systems will fail before it occurs. This is made possible by using technology like ScoutCam’s AI-based imaging platform to gather data that details the state of equipment and its components. Monitoring, Preventing and predicting failures and being proactive by immediately scheduling maintenance right when it is needed is aided further by integrating machine learning techniques in condition-based monitoring and predictive maintenance.

Machine learning is a type of AI that leverages existing data and conditions into algorithms to “learn” and then make predictions using mathematical or data models about future events based on previous ones. These algorithms or techniques, which include clustering, regression, classification, and anomaly detection to name just a few, have become an important aspect of condition-based monitoring and predictive maintenance.

The extensive and variable datasets used in machine learning techniques is a key element of the data models generated to address maintenance issues.

While the type of predictive maintenance inputs will vary based on the kind of equipment and use case in question and what metrics are desired and can be measured, data that is used can include detection of leaks, different measurements (such as distances between dynamic and static parts, rotation angles, slip line movements, etc.), miniature visual damage, corrosion levels, and more to compare actual performance against expected performance.

This data-driven approach is particularly useful in predictive maintenance for distant field service operations or areas which are beyond the technician reach. Critical data that is gathered from deliberately placed sensors, often in difficult operating environments, can be analyzed remotely to determine when components are expected to fail. This foresight allows organizations to have the remedies in place and on hand before the failure occurs.

Using machine learning techniques with advanced monitoring tools like ScoutCam technology yield several important benefits for predictive maintenance.

Reducing Equipment Downtime

Equipment failure results in unexpected downtime, which can be easily prolonged when there are delays in obtaining necessary parts due to supply issues. Using machine learning techniques helps organizations get the most out of their operating equipment while increasing its lifespan. For example, machine learning algorithms can be used to detect anomalies in performance that indicate pending failure. This allows for the timely replacement of failing components, prior to their actual failure, and prevents those components from possibly damaging other parts of the equipment.

Predictive maintenance tools like ScoutCam’s image-based AI platform provides real-time insight on the condition of equipment. When this data is used in machine learning algorithms, the data models can be used to limit failures, downtime and costs by identifying potential maintenance obstacles and root causes.

Enhanced Operating Equipment’s Reliability

All organizations want their equipment to be readily available and functioning optimally while not adding to costs. Machine learning techniques add more assurance in the predictability of component anomalous behavior, breakdown and malfunction. The machine learning models that are generated from the data collected from monitored equipment helps with creating a more informed maintenance management plan, one that can facilitate the prioritization of the critical components that have a high likelihood of failure.

Reduced Cost Expenses

There are costs associated with unnecessary maintenance, acquiring components, transporting them to a location and setting them up. For example, consider a traditional, reactive maintenance plan for a wind farm in a remote offshore location that requires the automatic replacement of a gearbox.

There are significant costs for purchasing the gearbox itself or its components, transporting the gearbox to the location and paying for the engineers with the expertise to install the components. In cases in which these parts are replaced unnecessarily, it is difficult to justify the expenses when machine learning-based predictive maintenance can be used to pinpoint the most optimal time for replacement.

Machine learning techniques can make inventory forecasting more accurate so that critical components can be on hand when they are needed, having been procured based on data analysis. The data obtained from imaging technology solutions like ScoutCam can help maintenance personnel determine when components are most likely to breakdown or expire, eliminating the need to replace components unnecessarily and at a high cost. It also helps organizations avoid the emergency costs that are inevitable with unexpected equipment failure and irregularities and the resultant breakdowns and downtime.

Reduced Environmental Impact

Equipment failures can have a negative impact on the environment, particularly in industries in which the malfunctions can result in leaks of hazardous materials, like certain gases or particulates like nitrogen oxides, carbon monoxide or carbon dioxide.

To minimize the impact to the environment as much as possible, machine learning algorithms can be used to analyze machine data to determine if there is behavior or trends that indicate a pending failure. Alerts can be generated so that immediate action can be taken at the first indication of operating issues that could result in environmentally unsafe events.

Reduced Risk

One of the significant challenges across all industries is the ability to identify and forecast the risks that can prevent the sustainability and safety of operations and users (such as airplane or train travelers). From a maintenance perspective, risk mitigation is accomplished by using machine learning to become more efficient with maintenance and to optimize the allocation of personnel and other resources to the areas of equipment or systems that require them the most.

Improved Operational Performance

Continuously monitoring and analyzing historical and real-time data has a positive on impact on operational performance because the factors that can obstruct operations are minimized. For example, using machine learning in predictive maintenance not only reduces the amount of time needed for repairs, but also the frequency of repairs of critical failure of equipment. When the occurrences of equipment failures decrease, downtime is also reduced. When equipment and systems operate with minimal disruption and machine learning can inform which areas can be more efficient, operational performance will always improve.

With predictive maintenance machine learning, organizations can conduct deliberate, highly informed maintenance decision-making. They can accurately anticipate their maintenance needs using ScoutCam’s image-based AI platform, which combines real-time data and conditions gathered from sensors with the advanced analytical power of machine learning. To learn how ScoutCam’s condition-based monitoring and predictive maintenance solution can help your organization optimize maintenance resources and avoid the fallout of unexpected downtime, contact us today.

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