This paper investigates the current monitoring for effective fault diagnosis in induction motor (IM) by using random forest (RF) algorithms. A rotor bar breakage of IM does not derive in a catastrophic fault but its timely detection can avoid catastrophic consequences in the stator or prevent malfunctioning of those applications in which this sort of fault is the primary concern. Current-based fault signatures depend enormously on the IM power source and in the load connected to the motor. Hence, homogeneous sets of current signals were acquired through multiple experiments at particular loading torques and IM feedings from an experimental test bench in which incipient rotor severities were considered. Understanding the importance of each fault signature in relation to its diagnosis performance is an interesting matter. To this end, we propose a hybrid approach based on Simulated Annealing algorithm to conduct a global search over the computed feature set for feature selection purposes, which reduce the computational requirements of the diagnosis tool. Then, a novel Oblique RF classifier is used to build multivariate trees, which explicitly learn optimal split directions at internal nodes through penalized Ridge regression. This algorithm has been compared with other state-of-the-art classifiers through careful evaluation of performance measures not encountered in this field.
Keywords: Artificial intelligence; Fault diagnosis; Induction motor; Oblique random forests; Simulated annealing algorithm.
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