Research And Application Of Digital Image Processing And Analysis In Fault Diagnosis

With the continuous development of science and technology, the structure of the rotating machinery and equipment grows more and more complicated, increasingly close ties between the different parts lead to the increasingly high manufacturing cost, etc., the risk of failure is also gradually increased. It is necessary to sample the state information of the machinery timely and accurately, then identify the working state of the machinery, thus to improve the reliability of the rotating machinery itself.Shaft orbit is an important means for rotating machinery fault diagnosis. It is synthesized by vibration signal, and reflects various fault information of rotating machinery, therefore, the shape characteristics of shaft orbit is very necessary for diagnosis shaft fault of rotating machinery. Based on these, the fault diagnosis problem can be transformed into the problems of image processing, analysis and identification. Traditional method is mainly concentrated in the research on the Fourier transform, wavelet transform, and moment-based feature extraction. But image feature extraction is to seek the best results with less mathematical description to express important information in the image. Therefore, it is undoubtedly effective to introduce a method of digital image processing for the feature extraction and identification of shaft orbit.The main contents and innovative results are listed as follows:(1) Considering the needs of automatic identification for the rotating machinery, the thesis establishes the foundation for shaft orbit automatic identification research, with digital image model description, basic computing, image transformation, image segmentation, target representation and description, and image pattern recognition theory.(2) By analyzing shaft orbit geometric characteristics, such as area, perimeter, roundness, and the dispersion index, the thesis put forward a method of modified chain code histogram. The method avoids the shortcomings in traditional way as rotation, translation and scale sensitive. However, it is not good for shaft orbit in ellipse and the outer "8" to only use the modified chain code histogram to extract the characteristics. It is difficult for classification and identification the state of the machinery in the case. Therefore, the thesis describes an improved extraction method based on boundary described and shape number. Meanwhile, the thesis uses probabilistic neural network to learn the mapping between the geometric characteristics and the type of shaft orbit. In addition, the fusion feature extraction methods of modified chain code histogram and other geometric information are described. The experimental results show that modified chain code histogram and shape number fusion feature extract method is better than the other fusion form, and have a high recognition rate.(3) Rotating machinery vibration signals are often associated with noise, which cause the shaft orbit curve not smooth. Therefore, feature extraction must have noise immunity. By pulse coupled neural network model derived from the study of the mammalian visual cortex neurons, the thesis improved pulse coupled neural network time signature and the roundness of shaft orbit fusion feature extraction method without training. In addition, the pulse coupled neural network information entropy time signature for the shaft orbit feature extraction is further used. Then, the feature vectors of two methods are all send to probability neural network and radial basis function neural network for classification. The experimental results show that the pulse coupled neural network time signature and shaft orbit roundness fusion feature extraction method can express original shaft orbit more accurate. And the probability neural network is superior to radial basis function neural network in multi-class recognition.(4) Contourlet transform can realize any direction decomposition at any scale, and work well at description of the image contour information. It can effective express the image contour by only using a small amount of the coefficient. The study improves wavelet transform and contourlet transform fusion shaft orbit feature extraction method. The method combines the good sparsity for singularity of wavelet transform and good detect ability for linear feature of contourlet transform to accurate description of the shape of the shaft orbit. It provides a new feature vector for shaft orbit automatic classification, also provides a new approach for rotating machinery fault diagnosis automation. The introduction of support vector machines to classify the feature vector, the shaft orbit automatic classification process becomes more rapid and effective.