Study On Auxiliaries Condition Monitoring And Fault Diagnosis Of Power Plant Based On Virtual Instrument

Fan is one of essential auxiliaries, which running status is directly concerned with the safety and economy of whole power plant. The thesis introduces Virtual Instrument and technology and artificial neural network technology into fault diagnosis, constructs a remote condition monitoring and fault diagnosis based Virtual Instrument technology, from the perspective of remote monitoring by the computer network.The thesis brings forward a reasonable project for condition monitoring of fan with the current system. For LabVIEW has network communication function, the TCP/IP technology is used in the project to accomplish remote condition monitoring and fault diagnosis. The system is made of server-client model, the server is responsible for data acquisition of vibration and transform. While the client is responsible for receiving and saving data, accomplish signal processing and fault diagnosis of device.The thesis researches the implementation of Virtual Instrument based on DAQ card. The system software is designed by modules, each module can be developed separately and used to finish a subtask, this makes the system easy to be rebuild. Date acquisition module, network communication module, monitoring and warning module, frequency spectrum analysis module, date management module and intelligence diagnosis module are developed.The thesis discusses the amplitude-domain, time-domain and frequency-domain analysis for signal based on LabVIEW. The author attaches key importance to making a further research on Zoom FFT, Quefrency, Holograph and their implements in LabVIEW, which solves the problems of low frequency resolution. The thesis researches SOM neural network on the intelligent fault diagnosis of fan, using the node technology of the MATLAB Script in LabVIEW. The system draw the fault symptom from the frequency spectrum of vibration signal, diagnose the fault type of fan and accomplish the task of fault diagnosis by the neural network intelligence diagnosis module.The thesis provides a useful way for the application of virtual instrument to the condition monitoring and fault diagnosis for auxiliary in power plants. It is proved that the system had highly practical value.