Lorry Bearing Acoustic Emission Signal Feature Extraction And Intelligent Diagnosis

Bearings can support, guide and reduce the movement of fixed machinery and mechanical friction between the rotating machinery. It is the most important part of rotating machinery. The start of rotating machinery fault is due to misalignment, overloading, unbalance and other reasons. For any of these problems, the bearings will face serious consequences. Therefore, health monitoring and diagnosis of bearing is required, which can avoid the mechanical failure that can lead to serious problems occur. Many monitoring technology have used in rotating machinery fault diagnosis of bearings. This paper discusses the acoustic emission monitoring, analyzing acoustic emission signals (AE) which detected from the train bearing components. Use modern signal processing techniques of signal processing and analysis, and then use pattern recognition method realize intelligent fault diagnosis.This paper introduces the common technology of detection. Through the comparative analysis, In order to facilitate early detection of rolling bearing failure, finally choice the acoustic emission signals as a fault signal source, because the AE signals have anti-interference and strong sensitivity. And then introduced the acoustic emission diagnosis technology mechanism and processing method. As the train accidents can not be changed, and accurate grasp the state of the train, predict the risks of failure can avoid the train accident. So the train bearing condition monitoring is the future of the railway truck safety information system development trends. To achieve heavy freight railway vehicle dynamic control, this paper use wavelet packet decomposition and support vector machine fault diagnosis algorithm. In the condition of Rolling Bearing Fault, this paper use the support vector machine (SVM) method for the intelligent fault diagnosis of bearing trucks, and realize intelligent bearings diagnosis online.