Scintillation detector fault diagnosis based on wavelet packet analysis and multi-classification support vector machine

This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB) with support vector machine (SVM). The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT-) based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

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Research on Gyro Fault Diagnosis Method Based on Wavelet Packet Decomposition and Multi-class Least Squares Support Vector Machine

Recent Trends in Intelligent Computing, Communication and Devices - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-13-9406-5_94 ◽

2019 ◽

pp. 789-797

Author(s):

Qiang Liu ◽

Jinjin Cheng ◽

Wenhao Guo

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Least Squares ◽

Wavelet Packet ◽

Support Vector ◽

Wavelet Packet Decomposition ◽

Diagnosis Method

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Fault diagnosis method for disc slitting machine based on wavelet packet transform and support vector machine

International Journal of Computer Integrated Manufacturing ◽

10.1080/0951192x.2020.1795927 ◽

2020 ◽

Vol 33 (10-11) ◽

pp. 1118-1128

Author(s):

Yiwei Zhu ◽

Qiusheng Yan ◽

Jiabin Lu

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Wavelet Packet ◽

Wavelet Packet Transform ◽

Support Vector ◽

Diagnosis Method

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Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.121-122.813 ◽

2010 ◽

Vol 121-122 ◽

pp. 813-818 ◽

Cited By ~ 1

Author(s):

Wei Guo Zhao ◽

Li Ying Wang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Wavelet Packet ◽

Rolling Bearing ◽

Support Vector ◽

Wavelet Packet Decomposition ◽

Intelligent Fault Diagnosis ◽

Fuzzy Support Vector Machine ◽

Bearing Fault Diagnosis ◽

Diagnosis Method

On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings，in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted，the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

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Fault Diagnosis of Automobile Rear Axle Based on Wavelet Packet and Support Vector Machine

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.211-212.1021 ◽

2011 ◽

Vol 211-212 ◽

pp. 1021-1026 ◽

Cited By ~ 1

Author(s):

Yong Chen ◽

Bao Qiang Wang ◽

Jin Yao

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Frequency Band ◽

Rear Axle ◽

Wavelet Packet ◽

Support Vector ◽

Svm Classifier ◽

Failure State ◽

Diagnosis Method ◽

Specific Frequency

This paper presents a fault diagnosis method of automobile rear axle based on wavelet packet analysis (WPA) and support vector machine (SVM) classifier. By Fourier transformation we find out the frequency band that can mostly reflect the rear axle failure state and use wavelet packet to decompose and reconstruct the vibration signals of rear axle, then extract each band’s energy and the variance, standard deviation, skewness, kurtosis of the specific frequency band to constitute a feature vector. We use the feature vectors which are come from some pieces of normal and abnormal samples to train support vector machine classifier for obtaining the best classification，at the same time, discuss the optimization of SVM parameters. Application shows that the method is effective in real time fault diagnosis for the automobile rear axle and has a strong anti-interference ability in different working conditions.

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