Predictive maintenance is a strategy that tries to predict when a machine is at high risk of failure and proactive maintenance activities can be performed just before it is predicted to happen. These predictions are based on machine learning algorithms utilizing data from condition based monitoring of machines and their critical components, made possible through inter-connected systems and sensors on these machines that form a part of Industry 4.0
Imagine a factory floor supervisor who is working on a normal day – having all machines running efficiently. The sensor data is regularly monitoring the running parameters & conditions of each critical component. If a machine part or the machine as a whole is about to develop a snag, one or more sensors monitoring the running conditions send out an alert - saving the manufacturer from unplanned downtime and saving time & lost revenue. This was possible as the manufacturer had deployed a machine learning based predictive maintenance solution that was able to predict a failure before it happened.
Maintenance Strategies for Asset Management
Maintenance has evolved from corrective interventions to condition based monitoring and prediction of failures before they happen:
Algorithms for Predictive Maintenance
The physical assets, including sensors, connected devices to collect real-time data etc. is one part of achieving predictive maintenance capabilities.
The second equally critical component is to train and develop the right machine learning algorithm to predict these failures. Our team at Xtage Labs is equipped to train and deploy machine learning algorithm for predictive maintenance. We follow the following steps for a machine learning based predictive maintenance algorithm development:
Remaining Useful Life (RUL) estimation models
We classify different remaining useful life (RUL) prediction approaches based on the following three business possibilities:
a. Similarity Model Algorithm: If we have the complete history of the running conditions of a machine, including normal working and failure data available
b. Survival Model Algorithm: In this case, we only have the failure information available and normal working conditions data is not captured
c. Degradation Model Algorithm: Here, we do not have failure data available, as the failure cases are very rare and critical and the machines are not allowed to fail (e.g. Aircraft Engine Failure). In this case, we collect data for the safety thresholds which should not be exceeded, and we use it to predict the RUL of a machine and when maintenance is required
Benefits of Predictive Maintenance are wide and far reaching. This includes
a) Reduce Potential Downtime
b) Improve inventory and maintenance schedules
c) Estimate (and minimize) losses due to inefficient machine parts
d) Improve component life
We build and deploy predictive maintenance solution through interactive web based dashboards, having inbuilt alert system - to collect, process and predict RUL based on real-time data inputs from sensors and critical machine components. The real time dashboards is where the sensor data can be streamed and regularly monitored
Beyond Predictive Maintenance?
Prescriptive maintenance looks to build upon predictive maintenance. However, it can only be achieved once predictive maintenance solution has been developed, deployed and refined. Prescriptive maintenance would suggest possible resolutions to a predicted failure.
The usage of prescriptive maintenance is linked to the progress of machine learning & AI technologies, and hopefully we need not wait for long before prescriptive maintenance becomes the solution of choice in the maintenance industry
Xtage Labs is an advanced analytics and machine learning based decision insights company. We work with businesses to derive insights from data, and improve the decision making processes.
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