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Induction machines are employed as prime movers in several applications in the present day industries. The major contributors of faults in an Induction machine are the bearings. The Incipient Bearing Failure defines the fault at an early stage and is a potential danger to the catastrophic failure of the electrical machine. One of the common faults found in bearings is the Outer Race Fault (ORF). The aim of this paper is to identify ORF in the induction machine at different load conditions. The vibration data is acquired and analysis is done by computing the statistical parameters, probability density function, and the cumulative distribution function. Finally, the chi-square test is used to validate the data and findings.

Condition-based maintenance is always an important strategy of maintenance to prolong the effective life of rotating machines as they run on high speeds with a variety of loads in some cases under severe conditions. If the monitoring of the current condition is not done accurately then rotating machines such as turbines, engine, bearing, shafts, gearbox, motors, and compressors leads to catastrophic failures with some serious consequences on the rate of production, safety, loss of manpower and sudden increase in repairing cost. Condition-based maintenance is also be called predictive type maintenance is a far superior technique as compared to preventive maintenance and run-to-break maintenance. In predictive maintenance current status of the machine while in operation, being monitored carefully and based on a brief analysis of the future condition of the machine predicted. This paper going to review the various intelligent condition monitoring techniques developed or used by various researchers to monitor the health of rotating machines and able to predict the faults at the earliest time.

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