Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems

Abstract

Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine’s condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.

Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines

“…Using (28), (29), (36) and (37), the final resistance and leakage inductance matrices that are used in the electromechanical equations of the healthy squirrel cage IM are…”

“…The resistance (38) and leakage inductance (39) matrices of the healthy machine are assembled using (28), (29), (36) and (37), using the values of the resistance and the leakage inductance of a stator phase, a rotor bar and end ring segment given in Appendix A.…”

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

“…To extend MCSA to the fault diagnosis of IM working in transient regime, advanced time-frequency (TF) transforms of the current are needed, so that the transient fault signatures can be identified in a joint TF domain. These transforms can be linear, such as the short-time Fourier transform (STFT) [28][29][30][31], the short-frequency Fourier transform (SFFT) [31,32] and the wavelet transform (WT) [33], or quadratic, such as the Wigner-Ville distribution (WVD) [34] or the ambiguity function [8]. Quadratic TF transforms can achieve optimal resolution for mono-component chirp signals but, in case of multi-component ones, they produce cross-terms artifacts that pollute the TF representation of the current, making it difficult the correct identification of the fault harmonics.…”

Applications of Power Electronics

“…Open circuit fault diagnosis and the fault tolerance control of three-phase active rectifiers as an inherent stage of many power electronics applications have been addressed in [14]. For induction motors, an automatic fault diagnosis system under a transient situation is developed in [15] and a fault-tolerant control strategy for five-phase induction motors under four and three-phase operation is addressed in [16]. Lastly, in [17], a review is provided on a health management system for lithium-ion batteries with a specific focus on electric vehicle applications.…”