Condition monitoring and fault diagnosis of rotating machinery based on feature extraction and expression of vibration signals

Abstract

 To further explore the state monitoring of rotating machinery, the state monitoring and fault diagnosis functions have been presented by using vibration signal feature extraction and expression technology. After describing the development status of vibration signal extraction and expression, the state monitoring and fault diagnosis model of rotating machinery are constructed by using this technology. Based on the algorithm flow and evaluation model, an optimization and update scheme is proposed, and an adequate evaluation model is established by evaluating the fault detection status effectively. A genetic algorithm is added to the model to assist optimization to achieve timely analysis and processing of data. In the testing of mechanical fault detection model, the efficiency of data processing and the number of fault detection are tested for vibration signal feature extraction. The test results show that the vibration signal feature extraction technology speeds up the state detection effect and the processing results are more precise.