Classical Mechanics-Inspired Optimization Metaheuristic For Induction Machines Bearing Failures Detection And Diagnosis

This paper deals with induction machines bearing failures detection and diagnosis using vibration and temperature signals. It proposes the use of a new Classical Mechanics-inspired Optimization (CMO) metaheuristic for data clustering. To ensure failure detection, transitions from a state to another is analyzed in order to form a transitional model between system states generated by the clustering. The performances of the proposed new metaheuristic are evaluated on the PRONOSTIA experimental platform data.