Abstract

This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D) texture features and a multiclass support vector machine (MCSVM). The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns), and extracts these texture features by generating the dominant neighborhood structure (DNS) map. The principal component analysis (PCA) is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA) multiclass support vector machines (MCSVMs) to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

Highlights

  • Induction motors are widely used in rotary machinery systems, including both heavy-and light-duty machinery [1], and play an important role in the industry due to their hardiness, low cost, and low maintenance requirements

  • This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D) texture features and a multiclass support vector machine (MCSVM)

  • The performance of the proposed model is evaluated in terms of true positive (TP) and false positive (FP) classification accuracy [27], where TP represents the number of faults in class i that are correctly classified into class i, and FP is the number of faults in other classes that are incorrectly classified into class i

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Summary

Introduction

Induction motors are widely used in rotary machinery systems, including both heavy-and light-duty machinery [1], and play an important role in the industry due to their hardiness, low cost, and low maintenance requirements. Vibration analysis has been the most frequently employed methodology for identifying induction motor faults due to its ability to represent intrinsic information of them [5]. These vibration signals are analyzed in time domain, frequency domain, and time-frequency domain [6, 7]. A multifault detection and classification approach is proposed that first converts vibration signals to two-dimensional (2D) gray images by transforming the amplitude of the signals into the intensity of the pixels in an image.

Related Works
Proposed Model
Implementation of the Proposed Model
Experimental Results
Conclusions
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