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Appl. Sci. 2018, 8(1), 25; https://doi.org/10.3390/app8010025

Broken Rotor Bar Fault Detection and Classification Using Wavelet Packet Signature Analysis Based on Fourier Transform and Multi-Layer Perceptron Neural Network

1
Department of Electrical and Electronic Engineering, University Putra Malaysia, Serdang 43400, Malaysia
2
Centre of Advanced Power and Energy Research (CAPER), University Putra Malaysia, Serdang 43400, Malaysia
*
Author to whom correspondence should be addressed.
Received: 30 August 2017 / Revised: 17 November 2017 / Accepted: 5 December 2017 / Published: 25 December 2017
(This article belongs to the Section Computer Science and Electrical Engineering)
View Full-Text   |   Download PDF [4354 KB, uploaded 25 December 2017]   |  

Abstract

As a result of increasing machines capabilities in modern manufacturing, machines run continuously for hours. Therefore, early fault detection is required to reduce the maintenance expenses and obviate high cost and unscheduled downtimes. Fault diagnosis systems that provide features extraction and patterns classification of the fault are able to detect and classify the failures in machines. The majority of the related works that reported a procedure for detection of rotor bar breakage so far have applied motor current signal analysis using discrete wavelet transform. In this paper, the most appropriate features are extracted from the coefficients of a wavelet packet transform after fast Fourier transform of current signal. The aim of this study is to develop an effective and sensitive method for fault detection under low load conditions. Through combining the strength of both time-scale and frequency domain analysis techniques, a unified wavelet packet signature analysis pinpoints the fault signature in the special fault-oriented frequency bands. The wavelet analysis combined with a feed-forward neural network classifier provides an intelligent methodology for the automatic diagnosis of the fault severity during runtime of the motor. The faults severity is considered as one, two, and three broken rotor bars. The results have confirmed that the proposed method is effective for diagnosing rotor bar breakage fault in an induction motor and classification of fault severity. View Full-Text
Keywords: induction motor; broken rotor bar; wavelet packet signature analysis; fast Fourier transform; multi-layer perceptron neural network induction motor; broken rotor bar; wavelet packet signature analysis; fast Fourier transform; multi-layer perceptron neural network
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Zolfaghari, S.; Noor, S.B.M.; Rezazadeh Mehrjou, M.; Marhaban, M.H.; Mariun, N. Broken Rotor Bar Fault Detection and Classification Using Wavelet Packet Signature Analysis Based on Fourier Transform and Multi-Layer Perceptron Neural Network. Appl. Sci. 2018, 8, 25.

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