scholarly journals Support Vector Machine for Vibration Fault Classification of Steam Turbine-Generator Sets

2011 ◽  
Vol 24 ◽  
pp. 38-42 ◽  
Author(s):  
Huo-Ching Sun ◽  
Yann-Chang Huang
Keyword(s):  
Support Vector Machine ◽  
Steam Turbine ◽  
Fault Classification ◽  
Support Vector ◽  
Turbine Generator ◽  
Generator Sets

Multi-Fault Classifier Based on Support Vector Machine and Its Application

2005 ◽  
Vol 293-294 ◽  
pp. 483-492 ◽  
Author(s):  
Zhou Suo Zhang ◽  
Minghui Shen ◽  
Wenzhi Lv ◽  
Zheng Jia He
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Time Domain ◽  
Simple Algorithm ◽  
Fault Classification ◽  
Support Vector ◽  
Turbine Generator ◽  
Signal Features ◽  
Multi Classification

Aiming at problem on limiting development of machinery fault intelligent diagnosis due to needing many fault data samples, this paper improves a multi-classification algorithm of support vector machine, and a multi-fault classifier based on the algorithm is constructed. Training the multi-fault classifier only needs a small quantity of fault data samples in time domain, and does not need signal preprocessing of extracting signal features. The multi-fault classifier has been applied to fault diagnosis of steam turbine generator, and the results show that it has such simple algorithm, online fault classification and excellent capability of fault classification as advantages.

Optimization of support vector machine based multi-fault classification with evolutionary algorithms from time domain vibration data of gears

2013 ◽  
Vol 227 (11) ◽  
pp. 2428-2439 ◽  
Author(s):  
DJ Bordoloi ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Time Domain ◽  
Angular Speed ◽  
Fault Classification ◽  
Support Vector ◽  
Vibration Data ◽  
Bee Colony ◽  
Time Domain Data ◽  
Grid Search Method

In the present work, a multi-fault classification of gears has been attempted by the support vector machine learning technique using the vibration data in time domain. A proper utilization of the support vector machine is based on the selection of support vector machine parameters. The main focus of this article is to examine the performance of the multiclass ability of support vector machine techniques by optimizing its parameters using the grid-search method, genetic algorithm and artificial bee colony algorithm. Four fault conditions were considered. A group of statistical features were extracted from time domain data. The prediction of fault classification is attempted at the same angular speed as the measured data as well as innovatively at the intermediate and extrapolated angular speed conditions. This is due to the fact that it is not feasible to have measurement of vibration data at all continuous speeds of interest. The classification ability is noted and it shows an excellent prediction performance.

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