Suman

Anomaly Detection in Bearing Vibration Measurments

Bearing finds its applications in various industries such as automobile, wind turbines, construction and mining machinery, agricultural equipment, machine tools etc. hence their defects if not anticipated earlier can lead to expensive intervention. Anomaly detection is a proactive approach that allows to detect potential issues before they cause significant damage. It is the identification of unexpected events, observations, or items that differ significantly from the norm. Electro-Mechanical machine are widely employed in a variety of industry applications. Mechanical machines require maintenance based on built quality and condition in which they operate. The field of maintenance in industry has evolved significantly, due primarily to improvements in control and measurement technology such as Conditional Based Monitoring (CBM) systems. Traditional approach of CBM involved based on scientific approach alone was ineffective due to several factors impact the failure of mechanical device which are hard to take account in the field. With advancement of Machine Learning and Deep Learning technologies it is highly desirable to keep track of health of system and provide insight on root cause of such failures for further improvement in the mechanical devices.

This project gives us an opportunity to work on an anomaly detection ML model that can identify whether bearings

need to be replaced. Bearings play an important role in rotational machines as their failure directly leads to breakdown

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