Research On Fractal Method Of Rotary Mechanical Fault Feature Extraction

Vibration signals generated by rotary machinery contain lots of fault information. By analysis into them, state changing of the parts in the mechanical equipment can be recognized, and the fault type can be found out. The most important and crucial problem in the mechanical fault diagnosis is the feature extraction method of the fault characteristic signal, while it is the very problem most difficult to solve. The vibration signals of mechanical equipment often represent nonstationarities due to occurrence and variance of fault, influence of nonnormal working condition and inherent nonlinearity of equipment. Because of the complexity of the dynamic signal and the multidisciplinary cross and fusion characteristic of the extracted signal, the feature extraction method has been the most important research direction concerned by the researchers, in which signal de-noising, instantaneous frequency feature extraction,diagnostic bases determination are the main elements.Making use of the non-stationary signal processing methods merits such as Wavelet Transform (WT) and Hilbert-Huang Transform (HHT), combining with fractal theory, the fault diagnosis method based on wavelet packet transform, fractal theory and neural network are put forward, and this dissertation investigates noise reducing method of the machinery vibration signal and the feature extraction technique of the mechanical equipment thoroughly. Based on these methods, a new kind of message bus based testing system architecture was proposed. Its contributions list as follows:In terms of signal de-noising, a new method of filtering and de-noising based on optimal Gaussian wavelet and Singular Value Decomposition (SVD) is put forward. Feature extraction and signal de-noising of the fault signal have been the most important investigation in the signal processing. By reducing the noise of machinery vibration signal, the mechanical fault information can be obtained effectively. The new de-noising method, which possesses better transient information extraction ability, could reduce the noise and extract the period of the signal effectively and assure the validity of the fault feature recognition.In terms of HHT improving, a new extraction method based on improving masking signal is put forward. Aim at solve the mode mixing problem, the masking signal is added before Empirical Mode Decomposition (EMD). As a result of simulation and experiment, it is shown that mode mixing can be effectively avoided. Through extending the data, it's the effective technique to restrain the ending effect of HHT. The example shows that the method improves the rationality and veracity of the signal feature extraction with HHT.After the investigating of fractal theory and fractal dimension calculation, the impvoved algorithm of fractal dimension calculation based on the result of HHT is proposed. The algorithm is determined the range of the weighting factor q by the EMD result, that not only reflecting the characteristics of the multifractal, but also minimizing the correlation among the parameters as much as possible. The method reduced the computation and improved the operating efficiency of the algorithm.By the integration of time-frequency analysis methods and fractal theory, the fault characteristics identification method based on utilization of fractal theory, wavelet, neural network is proposed. It has realized parametric description on signal characteristics of non-stationary in both time and frequency domain.In terms of system exploitation, a new kind of message bus based testing system architecture is proposed. The specific implementation is that gathering signal by computer standard bus based data acquisition card; processing, analyzing the data and illustrating the result by virtual instrument; and operating virtual instrument by message bus. Investigating the system structure of the module software, designed the uniform framework of the system module.There are the summarization of the article and expectation of the feature extraction technology development in the end of article.