Study On Kernel Pattern Analysis Methods Based Rotating Machinery Performance Degradation Assessment Technique

The rapid growth of technologies and market competition has already had a significant impact on commercial manufacture. The equipments development direction has some new characteristics, such as hugeness, distribution, high speed, automation and complexity. And also the equipments must face more and more harsh running condition. Once there is something wrong with them, production efficiency will fall or machine sets halt, even catastrophic accidents will occur. It is necessary and important to monitor key equipments and diagnose their faults in order to improve safety, allow predictive maintenance and shorten significantly the associated out of service time.Equipment performance degradation is a continuous process, and there are several stages from the initial degradation to the final failure. If the degree of the equipment performance degradation can be detected, it would be possible to make credible maintenance schedule and prevent the urgent broken. Equipment performance degradation assessment and prediction is proposed based on the above idea. Unlike the traditional fault diagnosis research, whose aim is to fault type classification, the emphases of this research is the equipment degradation degree and trend. By taking rolling element bearing and rotor as the research basis, several degradation assessment and prediction methods are brought. The contents are as follows:From the viewpoint of theoretical analysis and engineering application, the background and significance of the present study are elucidated. The state of the art review on equipment degradation assessment and prediction technology is thoroughly completed, respectively. The concrete research points are decided, then the research content of this paper are defined.The principles of kernel analysis method are briefly talked about. The wavelet kernel is inducted to several kernel analysis methods, such as support vector machine, support vector regression and kernel principle component analysis. Based on the translation invariant kernel Mercer theorem, the admissible wavelet kernel is constructed based on Mexican hat mother wavelet. Utilizing emulation and testing data set, the generalization ability of kernel analysis method using wavelet kernel and the one using RBF kernel are compared. The results indicate that the former has better performance.The definition of cyclostationarity entropy is brought forward as a monitoring tool according to CS characters of rolling element bearing. cyclostationarity entropy reflects the correlation between spectral lines, which will change with the development of failure and the deterioration of machine's operation situation. Wavelet kernel principle analysis (WKPCA) method is used to reduce the dimension of characteristic vector. WKPCA can improve the assessment efficiency and veracity. By using testing bearing data set, the performance of cyclostationarity entropy feature extraction method and WKPCA feature reducing method are verified.The degradation assessment method based on SVM multi-classifier is proposed. By utilizing the SVM binary tree algorithm, the decline problem of SVM based on"one-against-one"or"one-against-other"strategy is solved. The classification accuracy is improved by using the wavelet kernel. In order to increase the SVM training efficiency, the geometric distance probability parameters optimization is proposed. By using bearing data set with different pitting diameter, the performance of SVM assessment method and parameters optimization method are verified.The degradation assessment method based on SVM geometric distance is proposed. During solving the binary classification problem, SVM algorithm will construct an optimal hyperplan. The geometric distance between the vector and the hyperplan is taken as the measurement of degradation degree. Using the cracked rotor emulation data sets, the generalization of this assessment method is studied. Based on Pauta Criterion, the self-adapting alarm technology is studied.The performance degradation prediction method based on wavelet kernel support vector auto regression (WSVAR) is proposed. The inferences of different parameters on the prediction result are discussed. By using the binary grid searching method, the optimum prediction parameters are selected. By using bearing accelerated life testing data set, the prediction results of WSVAR and RBF-ANN and RBF-SVAR are compared. The results indicate that the WSVAR has more prediction accuracy.A bearing accelerated life test is performed on the accelerated bearing life tester (ABLT-1A) and several bearing data sets are collected. The effective of these above feature extraction method, the performance degradation assessment method and degradation prediction method are validated.A whole equipment performance degradation assessment system model based on analytic hierarchy process is proposed. And the system is developed based on the distributed programming model, WCF (Windows Communication Foundation), which is the realization technology of SOA (Service-Oriented Architecture). The utilization of WCF can solve the problem that the realization of performance degradation assessment algorithm is too complex to be satisfied with a single computer. A gas blower performance degradation assessment system is realized on the .NET platform, which validates the availability of the design.