Application of support vector machine based on pattern spectrum entropy in fault diagnostics of bearings

2010 ◽  
Author(s):  
Rujiang Hao ◽  
Zhipeng Feng ◽  
Fulei Chu
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Fault Diagnostics ◽  
Pattern Spectrum

Intelligent Bearing Diagnostics Using Wavelet Support Vector Machine

2014 ◽  
Vol 493 ◽  
pp. 337-342 ◽  
Author(s):  
Achmad Widodo ◽  
I. Haryanto ◽  
T. Prahasto
Keyword(s):  
Support Vector Machine ◽  
Input Data ◽  
Intelligent System ◽  
Feature Space ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Fault Diagnostics ◽  
Standard Data ◽  
Rolling Element

This paper deals with implementation of intelligent system for fault diagnostics of rolling element bearing. In this work, the proposed intelligent system was basically created using support vector machine (SVM) due to its excellent performance in classification task. Moreover, SVM was modified by introducing wavelet function as kernel for mapping input data into feature space. Input data were vibration signals acquired from bearings through standard data acquisition process. Statistical features were then calculated from bearing signals, and extraction of salient features was conducted using component analysis. Results of fault diagnostics are shown by observing classification of bearing conditions which gives plausible accuracy in testing of the proposed system.

Vibration Gear Fault Diagnostics Technique Using Wavelet Support Vector Machine

2014 ◽  
Vol 564 ◽  
pp. 182-188
Author(s):  
Achmad Widodo ◽  
D.P. Dewi Widowati ◽  
D. Satrijo ◽  
I. Haryanto
Keyword(s):  
Support Vector Machine ◽  
Input Data ◽  
Feature Space ◽  
Testing Procedure ◽  
Haar Wavelet ◽  
Support Vector ◽  
Fault Diagnostics ◽  
Test Rig ◽  
Vibration Data ◽  
Gear Fault

Intelligent diagnostics tool for detecting damaged bevel gears was developed based on wavelet support vector machine (WSVM). In this technique, the existing method of SVM was modified by introducing Haar wavelet function as kernel for mapping input data into feature space. The developed method was experimentally evaluated by vibration data measured from test rig machinery fault simulator (MFS). There were four conditions of gears namely normal, worn, teeth defect and one missing-teeth which has been experimented. Statistical features were then calculated from vibration signals and they were employed as input data for training WSVM. Fault diagnostics of bevel gear was performed by executing classification task in trained WSVM. The accuracy of fault diagnostics were evaluated by testing procedure through vibration data acquired from test rig. The results show that the proposed system gives plausible performance in fault diagnostics based on experimental work.

scholarly journals Fault diagnosis using improved pattern spectrum and fruit fly optimization algorithm–support vector machine

2018 ◽  
Vol 10 (11) ◽  
pp. 168781401881093 ◽  
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Wei Wang ◽  
Dejian Sun
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Simulation Analysis ◽  
Roller Bearing ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Pattern Spectrum

A fault diagnosis method using improved pattern spectrum and fruit fly optimization algorithm–support vector machine is proposed. Improved pattern spectrum is introduced for feature extraction by employing morphological erosion operator. Simulation analysis is processed, and the improved pattern spectrum curves present a steady distinction feature and smaller calculating amount than pattern spectrum method. Support vector machine with fruit fly optimization algorithm which can help seeking optimal parameters is employed for pattern recognition. Experiments were conducted, and the proposed method is verified by roller bearing vibration data including different fault types. The classification accuracy of the proposed approach reaches 87.5% (21/24) in training and 91.7% (44/48) in testing, showing an acceptable diagnosis effect.

Fault Diagnostics Based on Pattern Spectrum Entropy and Proximal Support Vector Machine

2009 ◽  
Vol 413-414 ◽  
pp. 607-612 ◽  
Author(s):  
Xiang Tao Yu ◽  
Wen Xiu Lu ◽  
Fu Lei Chu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Support Vector ◽  
Vibration Signals ◽  
Pattern Spectrum ◽  
Ring Fault ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Proximal Support Vector Machine

Based on pattern spectrum entropy and proximal support vector machine (PSVM), a motor rolling bearing fault diagnosis method is proposed in this paper. It is very difficult to filter the fault vibration signals from the strong noise background because the roller bearing fault diagnosis is a problem of multi-class classification of inner ring fault, outer ring fault and ball fault. Firstly, vibration signals are processed by the pattern spectrum. Secondly, the morphological pattern spectrum entropy, and pattern spectrum values are utilized to identify the fault features of input parameters of PSVM classifiers. The experiment results demonstrate that the pattern spectrum quantifies various aspects of the shape-size content of a signal, and PSVM costs a little time and has better efficiency than the standard SVM.

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