The Online Monitor And Fault Diagnosis System Based On LabVIEW For Steam Turbine

A turbine generator is a typical circumvolve machine. The vibration signals of a circumvolve machine reflect the fault information in the amplitude domain, the frequency domain, and the time domain. Therefore, understanding and mastering the vibration features of the turbine generator in the faulty status have an important theoretical significance and a practical engineering application value in monitoring the running status of the machine and heightening the accuracy of diagnosing faults.The mechanical vibration theory and fault mechanism of the circumvolve machine shows that most fault symptoms have the corresponding vibration features. Through the analysis and disposal of the vibration signals, the vibration features can be drawn out. The spectrum analysis on the non-stationary vibration signals by using the Fourier transform shows that the signals keep stationary on the analyzed period but the specific wavelets transform cannot be reflected, thus leading to inaccurate identification of the fault in the rotor. The early fault impact amplitude value is small, so it is easily interfered by other vibrations, thus leading to a low signal-to-noise ratio. In this sense, the key to diagnosing the fault in the rotor is to separate signals and noises and withdraw the faulty information.Wavelet analysis is a very popular method for time frequency analysis. It can be used to eliminate the noise from the signal and restore the signal, so it is applicable for analyzing and suiting the signals with a very low signal-to-noise ratio. This dissertation uses virtual apparatus software to analyze the wavelets. The monitored vibration signals are first decomposed into several frequency bands within the full-monitoring frequency band. We design a BP network of nerve by Matlab and distill the coefficient and threshold of the network, which were programmed by the LabVIEW to analysis the vibration signals. The characteristic frequency of the faulty components of the rotor is used as the input of the nerve network, and then through calculating the parallel values of the nerve network the corresponding fault is output to realize the mapping between the fault symptom space and fault space, thus realizing the identification and diagnosis of faults.We make the fault signal generator to measure the system that we design, it indicates that system can reflect the state of the machine. It operates steadily reliably and practically. It has a lot of practical worth.