Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors

• Published in: ISA transactions Vol. 80; pp. 427 - 438
• Main Authors: Martin-Diaz, Ignacio, Morinigo-Sotelo, Daniel, Duque-Perez, Oscar, Osornio-Rios, Roque A., Romero-Troncoso, Rene J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2018
• Subjects: Artificial intelligence; Fault diagnosis; Induction motor; Oblique random forests; Simulated annealing algorithm; MCSA; ANN; ORF; OOB; Oblique random forests; IM; ROC; FPR; KNN; SA; CBM; PCA; AUC; Induction motor; Fault diagnosis; DT; BRB; RF; FFT; TPR; Simulated annealing algorithm; OVA; Artificial intelligence; PM
• ISSN: 0019-0578; 1879-2022; 1879-2022
• DOI: 10.1016/j.isatra.2018.07.033

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. [Display omitted] •A rotor fault diagnosis with all indicators proposed recently in literature is developed.•SA algorithm identifies the features with greater discriminant capacity in different experiments.•Random Forest-based classifiers outperform CART and k-NN for incipient rotor conditions.•Careful performance evaluation through recent state-of-the-art techniques.