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Research Papers

Multifault Diagnosis of Induction Motor at Intermediate Operating Conditions Using Wavelet Packet Transform and Support Vector Machine

[+] Author and Article Information
Purushottam Gangsar

Department of Mechanical Engineering,
Indian Institute of Technology Guwahati,
Guwahati 781 039, Assam, India

Rajiv Tiwari

Department of Mechanical Engineering,
Indian Institute of Technology Guwahati,
Guwahati 781 039, Assam, India
e-mail: rtiwari@iitg.ernet.in

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received September 18, 2017; final manuscript received January 11, 2018; published online March 13, 2018. Assoc. Editor: Shankar Coimbatore Subramanian.

J. Dyn. Sys., Meas., Control 140(8), 081014 (Mar 13, 2018) (11 pages) Paper No: DS-17-1472; doi: 10.1115/1.4039204 History: Received September 18, 2017; Revised January 11, 2018

This paper proposes advancement in the fault diagnosis of induction motors (IMs) based on the wavelet packet transform (WPT) and the support vector machine (SVM). The aim of this work is to develop and perform the fault diagnosis of IMs at intermediate operating conditions (i.e., the speed and the load) to take care of situations where the data are limited or difficult to obtain at required speeds and loads. In order to check the capability of proposed fault diagnosis, ten different IM fault (mechanical and electrical) conditions are considered simultaneously. In order to obtain the useful information from raw time series data that can characterize each of the fault classes at various operating conditions, the wavelet packet is applied to decompose the data of vibration and current signals from the experimental test rig. Fault features are then obtained using the decomposed data and further used for the diagnosis. In this work, five different wavelet functions (i.e., the Haar, Daubechies, Symlet, Coiflet, and Discrete Meyer) are considered in order to analyze the impact of different wavelets on the IM fault diagnosis. The proposed fault diagnosis has been initially attempted for the same speed and load cases and then extended innovatively to the intermediate speed and load cases. In order to check the robustness of the proposed methodology, the diagnosis is performed for a wide range of motor operating conditions. The results show the feasibility of the proposed fault diagnosis for the successful detection and isolation of various faults of IM, even with limited data or information at some motor operating conditions.

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Figures

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Fig. 1

Binary class SVM for nonperfectly separable case

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Fig. 2

(a) Experimental test rig used for data generation and (b) IM with various seeded faults

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Fig. 4

Feature distribution (μ1, σ) of discrete Meyer wavelet coefficient of vibration signal for all IM faults at 40 Hz and T1 load

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Fig. 5

Feature distribution (μ1, σ) of discrete Meyer wavelet coefficient of the current signal for all IM faults at 40 Hz and T1 load

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Fig. 6

Feature distribution (μ1, σ) of the discrete Meyer wavelet coefficient of vibration signals for all IM faults at 30, 35, 40 Hz and T1 load

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Fig. 7

Feature distribution (μ1, σ) of the discrete Meyer wavelet coefficient of current signals for all IM faults at 30, 35, 40 Hz and T1 load

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Fig. 8

Feature distribution (μ1, σ) of the discrete Meyer wavelet coefficient of vibration signals for all IM faults at 30, 35, 40 Hz and T3 load

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Fig. 9

Feature distribution (μ1, σ) of the discrete Meyer wavelet coefficient of current signals for all IM faults at 30, 35, 40 Hz and T3 load

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Fig. 3

The CV accuracy for training with μ1, 40 Hz, T3, (a) Haar wavelet and (b) Symlet wavelet

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Fig. 10

Feature distribution (μ1, σ) of the discrete Meyer wavelet coefficient of vibration signals for all IM faults at 10 Hz and T1, T2 and T3

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Fig. 11

Feature distribution (μ1, σ) of the discrete Meyer wavelet coefficient of current signals for all IM faults at 10 Hz and T1, T2, and T3

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