Abstract
In the field of rotating machinery diagnosis using traditional intelligent diagnosis method, the state judgment and fault detection are usually carried out by symptom parameters (SPs). However, it is difficult to find the general and highly sensitive SPs for rotating machinery diagnosis. Intelligent methods, such as neural networks, genetic algorithms, etc., often cannot converge when being trained. In order to solve these problems, this paper proposes a new intelligent diagnosis based on distinctive frequency components (DFCs) and support vector machines (SVMs) which can be used to detect faults and recognize fault types of rotating machinery. The method has been applied to detect the structural faults of rotating machinery, and the efficiency of the method is verified by practical examples.
Published Version
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