Abstract

Wavelet transform has become popular because of satisfied result for identifying short circuit in stator of induction motor. This paper presents prototype of discrete wavelet transform combined with quadratic discriminant analysis for monitoring of induction motor condition. Mode of motor operation is introduced in this paper as normal operation, starting of fault, and ending of fault. For this task, Discrete wavelet transform with Quadratic discriminant analysis is used to classify motor current into this three state of motor operation. Motor current is processed by discrete wavelete transform with Haar filter to extract high frequency of signal. Then, energy level calculated from high frequency signal is evaluated with quadratic discriminant analysis to identifying four states of motor current.

Highlights

  • Induction motors are a critical component of most of industrial processes and are always integrated with production equipment

  • This paper describes capability of third level Haar wavelet transform combined with quadratic discriminant analysis to identification temporary fault in stator winding of induction motor

  • Energy level is obtained from high frequency signal of third level Haar wavelet transform

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Summary

Introduction

Induction motors are a critical component of most of industrial processes and are always integrated with production equipment. These characteristics are often the most important part of the signal It makes MCSA based FFT digital signal processing to be not suitable enough to analysis current signal if we would like to know time information. If we use big size for width of windowing, STFT will result good resolution of frequency, but poor for time resolution. It means that some frequencies of signal will be able to captured, but we do not know exactly when it is happen. If small size of windowing applied in STFT, we will get good resolution in time domain, but poor in frequency resolution For this problem, generation of digital signal processing after STFT can be used.

Wavelet Transform
Experiment Setup
Experiment Result
Fault Classification based on Quadratic discriminant Analysis
Findings
Conclusions
Full Text
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