Study On Remote Test And Intelligent Fault Diagnosis Of Rotating Machinery

Rotating machinery includes a series of key machine devices and it plays a very important role in modern industry. So, making sure the rotating machinery runs in normal state is significant to enterprise and even national economy. In allusion to the complexity of large-scale machine and the execrable environment it maybe located, this paper designed a remote intelligent fault diagnosis system to inspect the machine's faradism and vibration signal, analyze the signal using wavelet as well as neural network and judge its state consequently based on Labview.Both wavelet and neural network are widely used in fault diagnosis. This paper introduced the basic knowledge of wavelet and neural network, the state of research and its applications in failure diagnosis based on summarization of previous method of fault diagnosis. A novel arithmetic that combines the modified LDB(Local Discriminant Basis)algorithm and SOM-BP network was proposed to fault diagnosis and location. Extracting primal fault feature by improved LDB algorithm, the new method then mapped this incipient fault feature into a new feature space with high class separability via SOM (Self-Organizing Feature Map) nonlinearly transform. At last, the BP was used as the classifier.