A DWT AND SVM BASED METHOD FOR ROLLING ELEMENT BEARINGFAULT DIAGNOSIS AND ITS COMPARISON WITH ARTIFICIAL NEURALNETWORKS

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TYAGI SUNIL; PANIGRAHI SASHI KANTA

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Journal Paper

Paper Information

Journal: JOURNAL OF APPLIED AND COMPUTATIONAL MECHANICS Year:2017 | Volume:3 | Issue:1 Page(s): 80-91

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Title

A DWT AND SVM BASED METHOD FOR ROLLING ELEMENT BEARINGFAULT DIAGNOSIS AND ITS COMPARISON WITH ARTIFICIAL NEURALNETWORKS

Author(s)

TYAGI SUNIL | PANIGRAHI SASHI KANTA | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORK (ANN)

DISCREET WAVELET TRANSFORM (DWT)

FAULT DIAGNOSIS

ROLLING ELEMENT BEARING

SUPPORT VECTOR MACHINE (SVM)

Abstract

A classification technique using Support Vector Machine (SVM) classifier for detection of rollingelement bearing fault is presented here. The SVM was fed from features that were extracted from ofvibration signals obtained from experimental setup consisting of rotating driveline that was mounted onrolling element bearings which were run in normal and with artificially faults induced conditions. Thetime-domain vibration signals were divided into 40 segments and simple features such as peaks in timedomain and spectrum along with statistical features such as standard deviation, skewness, kurtosis etc. were extracted. Effectiveness of SVM classifier was compared with the performance of Artificial NeuralNetwork (ANN) classifier and it was found that the performance of SVM classifier is superior to that ofANN. The effect of pre-processing of the vibration signal by Discreet Wavelet Transform (DWT) prior tofeature extraction is also studied and it is shown that pre-processing of vibration signal with DWTenhances the effectiveness of both ANN and SVM classifiers. It has been demonstrated from experimentresults that performance of SVM classifier is better than ANN in detection of bearing condition and preprocessingthe vibration signal with DWT improves the performance of SVM classifier.

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APA: Copy

TYAGI, SUNIL, & PANIGRAHI, SASHI KANTA. (2017). A DWT AND SVM BASED METHOD FOR ROLLING ELEMENT BEARINGFAULT DIAGNOSIS AND ITS COMPARISON WITH ARTIFICIAL NEURALNETWORKS. JOURNAL OF APPLIED AND COMPUTATIONAL MECHANICS, 3(1), 80-91. SID. https://sid.ir/paper/353173/en

Vancouver: Copy

TYAGI SUNIL, PANIGRAHI SASHI KANTA. A DWT AND SVM BASED METHOD FOR ROLLING ELEMENT BEARINGFAULT DIAGNOSIS AND ITS COMPARISON WITH ARTIFICIAL NEURALNETWORKS. JOURNAL OF APPLIED AND COMPUTATIONAL MECHANICS[Internet]. 2017;3(1):80-91. Available from: https://sid.ir/paper/353173/en

IEEE: Copy

SUNIL TYAGI, and SASHI KANTA PANIGRAHI, “A DWT AND SVM BASED METHOD FOR ROLLING ELEMENT BEARINGFAULT DIAGNOSIS AND ITS COMPARISON WITH ARTIFICIAL NEURALNETWORKS,” JOURNAL OF APPLIED AND COMPUTATIONAL MECHANICS, vol. 3, no. 1, pp. 80–91, 2017, [Online]. Available: https://sid.ir/paper/353173/en

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