Dissertation > Industrial Technology > Machinery and Instrument Industry > Machinery Manufacturing Technology > Flexible manufacturing systems and flexible manufacturing cell > Fault diagnosis and maintenance

The Research of Turbine Generator Fault Diagnosis Based on Wavelet Transform Technique

Author LiuYi
Tutor ZhaoQi
School Hebei University of Engineering
Course Applied Computer Technology
Keywords Fault diagnosis Turbine Wavelettransform Denoising Feature extraction
CLC TH165.3
Type Master's thesis
Year 2010
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Turbine vibration signal generally contains a lot of noise, so requires de-noising the vibration signal.Based on the Donoho threshold de-noising, proposed a new definition of quantitative threshold, and implements a signal de-noising method based on genetic algorithm, Through the introduction of the estimated factor╬▒,to achieve the purpose of improving signal to noise ratio by genetic optimization the estimated factor. Comparing test results, we can see based on genetic algorithms de-noising effect is much better than the traditional threshold of Donoho de-noising.Wavelet packet as a time-frequency domain analysis tools introduced into the vibration signal analysis, wavelet packet coefficients can be very flexible to provide the information of signal in time domain and frequency domain. Through experimental analysis, feature extraction of turbine fault based on wavelet packet decomposition algorithm is compared with FFT spectrum analysis algorithm, also be able to fully meet the requirements of vibration signal analysis.Also get the distribution of vibration signal energy in the frequency, this provide the basis for the construction of Bayesian networks.Experiments show that the method used to signal feature extraction is very effective and practical.Discretization the energy-frequency table which obtained in Bently Rotor laboratory, to build a Bayesian network model.At the same time, de-noising the Rubbing data from Bently experimental table, feature extraction, obtained the fault symptoms under the rub fault,combined with Bayesian network model, and the expertise to determine a priori probability, to achieve the fault classification by the Bayesian networks. Experimental results show that according to turbine vibration signals,the expertise-based Bayesian network model and vibration signal de-noising and feature extraction techniques, can accurately determine what the fault it is.

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