Fault Diagnosis System for Reciprocating Air Compressor Based on Support Vector Machine

Reciprocating air compressor's structure is complex, and it has various excitation sources when running, moreover, there are a few fault samples in actual fault diagnosis, so it is difficult to implement intelligent diagnosis. Support Vector Machine based on Statistical Learning Theory just overcomes this deficiency, and it provides a new approach for diagnosis technology to develop into intelligent diagnosis. The application of Support Vector Machine on fault diagnosis for reciprocating air compressor and a concrete implementation scheme are discussed in this paper. A fault diagnosis system for reciprocating air compressor is established, and the vibration signals of rolling bear in reciprocating air compressor's crankcase are simulated in a test-bed. The test result shows that this system has strong adaptability for reciprocating air compressor diagnosis of a few samples and could recognize fault rapidly and accurately.