Research Of Fault Diagnosis Method Based On Principle Component Analysis And Application

Due to the rapid development of modern production and science and technology progress, industrial process becomes more and more complex, amounts of investment becomes larger and larger, automatic level has been higher and higher. Therefore, the security and reliability of industrial process become more important. Fault detection and diagnosis technology based on the principal component analysis is one of the hot researches of processes automation. In recent years, with the rapid development of pipeline transportation industry in our country, the safety of pipeline transportation is attracted attention widely. Pipeline leak detection is an important part of pipeline safety transportation. Whether the incidence of oil pipeline leakage could be detected and determined accurately and timely is of great practical significance to reduce economic losses, protect the natural environment and ensure the property of resident safe around the pipeline.In this thesis, the fault diagnosis methods and pipeline leakage detection methods have been briefly studied. The fault detection approach based on principal component analysis and the fault diagnosis approach based on contribution plot are deeply considered. For some existing shortages of the fault detection and diagnosis approach based on the principal component analysis, the fault detection and diagnosis based on kernel principal component analysis are proposed. The simulation researches and the result analysis have been done and discussed by Tennessee-Eastman model. At last, the kernel principal component analysis method was applied to pipeline leakage detection. In this dissertation, the main achievement of research can be summarized as follows.1. The basic principle of principal component analysis is studied, and the process algorithm of fault detection and diagnosis based on principal component analysis is presented. By the simulating researches, it has noted that the changes of two statistics have been used to determine whether fault occurred and the contributions of variable to statistics are used to identify the fault source for fault detection and diagnosis. The simulation results show a good performance in fault detection and there are some limitations in fault diagnosis.2. For the nonlinear limitations of conventional principal component analysis, a new fault diagnosis method of kernel principal component analysis is presented, which is built on the basis of differential contribution plots and the derivative of kernel function. The algorithm process is designed. The simulation results show that this method possesses higher accuracy than the method of principal component analysis in identifying faulty variables.3. The result of researching on kernel principal component analysis method was applied to the pipeline leakage detection. The flow diagram of the detection algorithm was designed and verified using actual industrial data. The results show that the method of kernel principal component analysis shows a good effect on the pipeline leakage detection, and has a certain advanced nature and practicality.Finally, on the bases of summary for this thesis, it made a prospect for the future research topics and work.