Title :
Fault Diagnosis for Reciprocating Air Compressor Valve Using P-V Indicator Diagram and SVM
Author :
Wang, Fengtao ; Song, Lutao ; Zhang, Liang ; Li, Haifeng
Author_Institution :
Res. Inst. of Vibration, Dalian Univ. of Technol., Dalian, China
Abstract :
This paper presents a method of fault diagnosis for the reciprocating air compressor valve based on the indicator diagram and the support vector machine (SVM). This paper strikes 7 invariant moments of the indicator diagram of reciprocating air compressor, using image processing methods, according to the same moment theory. Then the method can be used to extract effective features as feature vectors for training the support vector machine, and achieve fault diagnosis for reciprocating air compressor valve. Finally, the paper simulate 5 kinds of working conditions of valve to identify using the fault monitoring system of reciprocating compressor valve, in order to verify the feasibility and effectiveness of the method.
Keywords :
compressors; condition monitoring; fault diagnosis; feature extraction; image processing; indicators; learning (artificial intelligence); mechanical engineering computing; support vector machines; valves; P-V indicator diagram; SVM; fault diagnosis; fault monitoring system; feature extraction; feature vector; image processing method; moment theory; reciprocating air compressor valve; support vector machine; Fault diagnosis; Feature extraction; Image coding; Support vector machines; Training; Valves; Vibrations; SVM; indicator diagram; reciprocating air compressor; valve failure;
Conference_Titel :
Information Science and Engineering (ISISE), 2010 International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-428-2
DOI :
10.1109/ISISE.2010.91