Feature Extraction using Wavelet Transform for Multi-class Fault Detection of Induction Motor

Chattopadhyay, Paramita; Konar, Pratyay

doi:10.1007/s40031-014-0076-1

Cited by 12 publications

(8 citation statements)

References 23 publications

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“…The signals are decomposed into some separate bands without redundant signal and omissions and the decomposed bands all have a certain amount of energy, which reflects the running state of skin electrical signal; therefore, it's reasonable to use the method of wavelet packet energy detection to detect the change of the proportion of energy in corresponding frequency band [4][5] .…”

Section: Wavelet Packet Transformmentioning

confidence: 99%

Skin Electrical Signal Feature Extraction Based on Wavelet Packet and Establishment of Human Thermal Comfort Model

Wei¹

2016

Proceedings of the Fourth International Conference on Information Science and Cloud Computing — PoS(ISCC2015)

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Section: Wavelet Packet Transformmentioning

confidence: 99%

Skin Electrical Signal Feature Extraction Based on Wavelet Packet and Establishment of Human Thermal Comfort Model

Wei¹

2016

Proceedings of the Fourth International Conference on Information Science and Cloud Computing — PoS(ISCC2015)

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“…Thus, introduces good properties such as limited redundancy, provides approximately shift-invariance and geometrically oriented signal in multiple dimensions [14] which were lacking in conventional wavelet transform. Its multi-resolution feature extraction break a signal into sine waves of various frequencies capable of extracting local and global information revealing important aspects like trends, breakdown points, discontinuities in higher derivatives, and self-similarity which characterizes a signal [15]. The transform is approximately shift-invariant meaning if the input sequence is changed by an arbitrary number of samples; the energy in each subband is preserved.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Dual-Tree Complex Wavelet Packet Based Entropy Features for Voice Pathologies Detection

et al. 2020

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The Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) has been successfully implemented in numerous field because it introduces limited redundancy, provides approximately shift-invariance and geometrically oriented signal in multiple dimensions where these properties are lacking in traditional wavelet transform. This paper investigates the performance of features extracted using DT-CWPT algorithms which are quantified using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) classifiers for detecting voice pathologies. Decomposition is done on the voice signals using Shannon and Approximate entropy (ApEn) to signify the complexity of voice signals in time and frequency domain. Feature selection methods using the ReliefF algorithm and Genetic algorithm (GA) are applied to obtain the optimum features for multiclass classification. It is observed that the best accuracies obtained using DT-CWPT with ApEn entropy are 91.15 % for k-NN and 93.90 % for SVM classifiers. The proposed work provides a promising detection rate for multiple voice disorders and is useful for the development of computer-based diagnostic tools for voice pathology screening in health care facilities.

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“…Finally, they concluded that Meyer and complex Meyer wavelet with the SVM gave the best performance for fault diagnosis of bearings. Chattopadhyay and Konar [28] used features of the continuous and discrete wavelets for the fault diagnosis of BRB based on the RBF and multilayer perceptron (MLP) neural networks, and the SVM. To examine the impact of wavelets on the feature extraction, four wavelets from Daubechies family are selected.…”

Section: Introductionmentioning

confidence: 99%

Diagnostics of mechanical and electrical faults in induction motors using wavelet-based features of vibration and current through support vector machine algorithms for various operating conditions

Gangsar

Tiwari

2019

J Braz. Soc. Mech. Sci. Eng.

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Fault diagnosis of induction motors (IMs) is always a challenging task in the practical industrial field, and it is even more challenging in the case of inadequate information of IM working conditions. In this paper, a new methodology for fault detection has been proposed for IMs to detect various electrical and mechanical faults as well as their severities, where the data are unavailable at required operating conditions (i.e., speed and load) based on wavelet and support vector machine (SVM). For this, the radial, axial and tangential vibrations, and three-phase current signals are acquired from IMs having different faults. The acquired time domain signal is then transformed to time-frequency signals using continuous wavelet transform (CWT). Ten different base wavelets are used to investigate the impact of different wavelet function on the fault diagnosis of IMs. Statistical features are extracted based on the CWT, and then appropriate feature(s) are selected using the wrapper model. These features are fed to the SVM to detect whether a defect has occurred. The fault detection is performed for identical speed and load case using a number of mother wavelets. To analyze the robustness of the present system, diagnosis is attempted for various operational conditions of IMs. The result showed that the feature(s) selected using the Shannon wavelet diagnose, the fault categories of IM more accurately as compared to other wavelets, and remarkably found to be robust at all working conditions of IMs. The work is finally extended to perform the fault diagnosis when limited information is available for the training. From the results, it is observed that the proposed methodology does not only take care of the practical problem of unavailability of data at different operating conditions, but also shows good performance and takes low computation time, which are vital requirements of a condition monitoring and diagnostic system. Keywords Induction motor (IM) fault • Continuous wavelet transform (CWT) • Multi-fault diagnostic • Support vector machine (SVM) • Intermediary working condition Abbreviations ABCA Artificial-bee-colony algorithm AI Artificial intelligence ANN Artificial neural network BF Bearing fault BP Back propagation BR Bowed rotor BRB Broken rotor bar CACO Chaotic ant colony optimization algorithm CF Crest factor CWC Continues wavelet coefficient CWT Continues wavelet transform CV Cross-validation DAG Direct acyclic graph DAQ Data acquisition system DWT Discrete wavelet transform FDI Fault diagnosis and isolation FL Fuzzy logic FFT Fast Fourier transform FNN Fuzzy neural network

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Feature Extraction using Wavelet Transform for Multi-class Fault Detection of Induction Motor

Cited by 12 publications

References 23 publications

Skin Electrical Signal Feature Extraction Based on Wavelet Packet and Establishment of Human Thermal Comfort Model

Skin Electrical Signal Feature Extraction Based on Wavelet Packet and Establishment of Human Thermal Comfort Model

Optimization of Dual-Tree Complex Wavelet Packet Based Entropy Features for Voice Pathologies Detection

Diagnostics of mechanical and electrical faults in induction motors using wavelet-based features of vibration and current through support vector machine algorithms for various operating conditions

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