Bearing fault detection using logarithmic wavelet packet transform and support vector machine

Bearing fault detection using logarithmic wavelet packet transform and support vector machine

Автор: Om Prakash Yadav, G. L. Pahuja

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 5 vol.11, 2019 года.

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Objective: This paper presents an automated approach that combines Fisher ranking and dimensional reduction method as kernel principal component analysis (KPCA) with support vector machine (SVM) to accurately classify the defects of rolling element bearing used in induction motor. Methodology: In this perspective, vibration signal produced by rolling element bearing was decomposed to four levels using wavelet packet decomposition (WPD) method. Thirty one Logarithmic Root Mean Square Features (LRMSF) were extracted from four level decomposed vibration signals. Initially, thirty one features were rank by Fisher score and top ten rank features were selected. For effective detection, top ten features were reduced to a new feature using dimension reduction methods as KPCA and generalized discriminant analysis (GDA). After this, the new feature applied to SVM for binary classification of bearing defects. For analysis of this thirty six standard vibration datasets taken from online available bearing data center website of Case Western Reserve University on bearing conditions like healthy (NF), inner race defect (IR) and ball bearing (BB) defects at different loads. Results: The simulated numerical results show that proposed method KPCA with SVM classifier using Gaussian Kernel achieved an accuracy (AC) of 100, Sensitivity (SE) of 100%, Specificity (SP) of 99.3% and Positive prediction value (PPV) of 99.3% for NF_IRB dataset, and an AC of 100, SE of 99.8%, SP of 100% and PPV of 100% for NF_BBB dataset.

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Fisher’s ranking method, inner raceway defect, ball bearing defect, kernel principal component analysis, support vector machine, wavelet packet decomposition

Короткий адрес: https://sciup.org/15016052

IDR: 15016052   |   DOI: 10.5815/ijigsp.2019.05.03

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