FAULT DETECTION AND PREDICTIVE MAINTENANCE OF INDUSTRIAL MACHINES USING MACHINE LEARNING

Abstract

The Industrial Internet of Things (IIoT) is the use of Internet of Things (IoT) technologies in manufacturing which harnesses machine data generated by various sensors and applies various analytics on it to gain useful information. The data captured by the machines is usually accompanied by a date time component, which is vital for predictive modelling. The machinery faults predictions are expensive both in terms of repair and loss output in production. These losses or faults may cause the entire machinery or plant to break down. The main objective of this study is to apply advanced machine learning techniques to avoid these losses and faults and replace them with predictive maintenance. The Objective of this study is to identify and predict the faults in industrial machinery using Machine Learning (ML) and Deep Learning (DL) approaches.

Predictive maintenance is a proactive approach to maintaining equipment and systems in industrial settings. It involves using data and machine learning algorithms to predict when equipment is likely to fail, so that maintenance can be scheduled in advance to minimize the likelihood of unplanned downtime. In the context of the Industrial Internet of Things (IIoT), predictive maintenance can be implemented by collecting data from sensors and other IoT devices, and using that data to train machine learning models that can predict when equipment is likely to fail. These models can be used to create alerts or notifications, which can be used to trigger maintenance activities before equipment failure occurs. This can help to reduce the costs and disruptions associated with unplanned downtime, and improve the overall efficiency of the industrial operation.