Induction Machines Bearing Failures Detection And Diagnosis Using Variable Neighborhood Search

This paper deals with induction machines bearing failures detection and diagnosis using vibration and temperature signals. The failure detection is managed by a clustering graphical representation creating transition classes. Motivated by the computational complexity of the problem, a Variable Neighborhood Search (VNS) metaheuristic is developed including well-designed local search algorithms for data clustering to the system diagnosis. Computational experiments carried out on the PRONOSTIA experimental platform data show that the proposed algorithm seems to be efficient and effective.