14Real-Time Monitoring and Predictive Maintenance
R. Patel1*, S. Shah1, S. Lella2 and A. Gajbahar1
1Illinois Institute of Technology, Chicago, Illinois, United States
2Lewis University, Chicago, Illinois, United States
Abstract
The pattern of shifting towards predictive maintenance, bolstered by real-time monitoring and analysis techniques, is revolutionizing modern manufacturing with an objective of minimize downtime and maximize operational efficiency. Technological advancements in smart manufacturing coupled with data analytics and machine learning algorithms have helped in achieving this goal. Predictive maintenance requires sophisticated networks of sensors and monitoring devices for monitoring environmental indicators such as temperature, vibration, and pressure, alongside detecting abnormal conditions like excessive wear, consumable levels, and other relevant parameters continuously as a continuous stream of input for machine learning to recommend intervention based on identified trends, anomalies, intermittent issues, and deviations from normal operating conditions. Such recommendations are proactive maintenance or part replacement activities which can be further automated. Beyond manufacturing, these techniques can be used in many industries that require minimizing downtime and maximizing operational efficiency by avoiding equipment failure related operational risks. Such an approach will help energy facilities, aviation, healthcare, and critical infrastructure. Predictive ...
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