Abstract
Induction motors are key equipments widely used in modern industries. Fault diagnosis of broken rotor bar (BRB) timely and accurately is very important to ensure the reliable operation of induction motors (IMs). In this study, a multivariate relevance vector machine with multiple Gaussian kernels (MKMRVM) and principal component analysis (PCA) are developed to construct a classification model. Then an improved bacterial foraging optimization combining with Levy fight mechanism, named LBFO, is employed to tune the kernel parameters of MKMRVM to obtain the optimal fault diagnosis model. Finally, The LBFO-based MKMRVM classification model is used to identify the diagnosis of BRB of IMs. The performance is assessed based on a comprehensive experiment of fault diagnosis of BRB. The experimental results demonstrate that the proposed method may achieve high diagnosis accuracy for different noise levels of diagnosis signals and is superior to other related fault diagnosis techniques.
Highlights
Nowadays motors are extensively applied to a range of industries, due to their high reliability, simple construction and robust design [1]
The MKMRVM classifier constructed by a multiple kernel Gaussian function has d kernel parameters, which equals the dimension of input samples
EXPERIMENTAL SETUP To evaluate the method of fault diagnosis of broken rotor bar (BRB) proposed in this paper, the experimental data are required
Summary
Nowadays motors are extensively applied to a range of industries, due to their high reliability, simple construction and robust design [1]. Thayananthan [23] proposed a multivariate relevance vector machine (MRVM) extending from RVM which was originally used to resolve the pose estimation problem In the last few decades, many optimization methods have emerged, including genetic algorithm (GA) [24], particle swarm optimization (PSO) [25], ant colony optimization (ACO), differential evolution (DE) [26], simulated annealing (SA) [27], and so on These classic optimization techniques are frequently used to tune parameters of various fault diagnosis models or feature selection of different signals [28]–[32]. A two-stage method to construct a classifier model with the high accuracy for fault diagnosis of BRB of IMs is proposed.
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