A PCA and Two-Stage Bayesian Sensor Fusion Approach for Diagnosing Electrical and Mechanical Faults in Induction Motors

Abstract

Induction motors are widely used in industrial plants for critical operations. Stator faults, bearing faults or rotor faults can lead to unplanned downtime with associated cost and safety implications. Different sensors may be used to monitor the health state of induction motors with each sensor typically being better suited to diagnosing different faults. Condition monitoring approaches which fuse data from multiple sensors have the potential to diagnose a greater number of faults. A sensor fusion approach based on the combination of a two-stage Bayesian method and Principal Component Analysis is proposed for diagnosing both electrical and mechanical faults in induction motors. Acoustic, electric and vibration signals are gathered from motors operating under different loading conditions and health states. The inclusion of the PCA step ensures robustness to varying loading conditions. The obtained results highlight that the proposed method performs better than equivalent single stage or feature-based Bayesian methods.

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

“…It was observed that this proposed combined approach effectively and adaptively identified inner ball faults. Stief et al [33] proposed a sensor fusion approach to diagnose both electrical and mechanical faults in induction motors based on the combination of a two-stage Bayesian method and PCA. Caggiano [34] also proposed an advanced feature extraction methodology based on PCA.…”

Introduction to Special Issue on Symmetry in Mechanical Engineering

“…The presented solutions, methods, and approaches can be improved and used in the future. Moreover, mechanical engineering is essential for fault diagnosis of machines [10][11][12][13][14][15][16][17][18][19][20][21][22] and the analysis of temperature [23][24][25]. The mechanical properties of materials are also investigated in the literature [26][27][28].…”

Inter-turn Short Circuit Diagnosis Using New D-Q Synchronous Min–Max Coordinate System and Linear Discriminant Analysis

“…Although the wired method involves direct coupling, recent developments in wireless communication technology have enabled diagnosis without direct coupling. Furthermore, many research studies have been conducted based on the aforementioned developments [8][9][10]. Typically, amplitudes of frequencies ratio 50 s frequency coefficient (MSAF-RATIO-50-SFC), MSAF-RATIO-50-SFC-EXPANDED, MSAF-RATIO-24-MULTIEXPANDED-FILTER-8, and the shortened method of frequencies selection (SMoFS-15) are normally used, but the analysis is somewhat complicated [11,12].…”