Automatic diagnosis method for structural fault of rotating machinery based on distinctive frequency components and support vector machines under varied operating conditions

Abstract This paper presents a new, intelligent diagnostic method for identifying structural faults in rotating machinery based on distinctive frequency components (DFCs) and support vector machines (SVMs) under varied operating conditions. This method consists of three stages. First, when investigating and comparing the spectrum feature of structural faults in the most salient frequency band, the DFCs can be detected and extracted. Second, when analyzing the common DFCs from various operating conditions, the DFCs are normalized on the universal standard to reduce the difference. Then, the optimal DFC area of any state under various operating conditions can be detected using probability theory. Finally, the optimal DFCs are input into the SVMs to detect faults and sequentially identify fault types from rotating machinery. The proposed method has been applied to detect structural faults from rotating machinery, and the efficiency of the method has been verified using practical examples.

[1]  Peng Chen,et al.  Automated function generation of symptom parameters and application to fault diagnosis of machinery under variable operating conditions , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[2]  Dong Wang,et al.  A hybrid neural networks based machine condition forecaster and classifier by using multiple vibration parameters , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[3]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[4]  Toshio Toyota,et al.  Fuzzy diagnosis and fuzzy navigation for plant inspection and diagnosis robot , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[5]  Bo-Suk Yang,et al.  Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors , 2007, Expert Syst. Appl..

[6]  B. Samanta,et al.  ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES , 2003 .

[7]  Silvio Simani,et al.  Fault diagnosis in power plant using neural networks , 2000, Inf. Sci..

[8]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[9]  J. Jeffrey Richardson,et al.  Artificial Intelligence in Maintenance , 1985 .

[10]  Asoke K. Nandi,et al.  Fault detection using genetic programming , 2005 .

[11]  Bo-Suk Yang,et al.  Condition classification of small reciprocating compressor for refrigeration using artificial neural networks and support vector machines , 2005 .

[12]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

[13]  Qiao Hu,et al.  Intelligent Fault Diagnosis in Power Plant Using Empirical Mode Decomposition, Fuzzy Feature Extraction and Support Vector Machines , 2005 .