Research On Fault Diagnosis Of Control System Based On Slow Feature Analysis

With the continuous improvement of modernization level,the control system is becoming more scale and more complex,which puts forward higher requirements for its safety and reliability,therefore,the fault diagnosis technology of the control system has received more and more attention.In addition,due to the rapid development of modern sensing technology and the continuous improvement of computer level,a large amount of process data is recorded and stored,how to extract valid information from these data has become an important research direction in the field of fault diagnosis.In this case,slow feature analysis appeared which acts as a feature extraction method,it can extract the slowest component of the time series signal and effectively represent the inherent properties of the system.This paper introduces the slow feature analysis method to the fault diagnosis technology,and related researches have been conducted with the control system as research object.Firstly,the paper introduces the idea and principle of slow feature analysis algorithm,and the complete fault diagnosis model based on slow feature analysis is studied: using the data extracted by slow feature analysis to construct monitoring statistics which can reflect the operating status of the system to achieve fault detection;if the fault occurs,the contribution graph method is used by calculating the contribution rate of each variable to statistics to realize the fault location.Secondly,In order to solve the problem that slow feature analysis cannot handle the nonlinear data well,the Gaussian kernel function is introduced to nonlinear expansion of the algorithm,and the kernel slow feature analysis method is applied to the fault diagnosis of the control system.The monitoring statistic method based on kernel slow feature analysis is proposed for fault detection,and because the kernel method can not find the inverse mapping function from high-dimensional feature space to original space,the idea of kernel sample equivalent replacement is introduced to realize the fault identification,which can find the correspondence between input variables and the monitoring statistics indirectly by revealing the relationship between the kernel matrix and the input variable matrix.The simulation results show that this method is superior to the traditional slow feature analysis in dealing with nonlinear process data,and the kernel sample equivalent replacement idea not only guarantees the calculation accuracy,but also greatly improves the diagnosis efficiency.Finally,In order to accurately identify the fault type of the actuator of control system,taking control valve as the research object,a fault diagnosis method based on information fusion of characteristic index is proposed.According to the relationship between the input and output variables of control valve,several characteristics indicators reflecting control valve's different fault phenomena have been found;and then,the D-S evidence theory is used to integrate each index to get the final diagnosis result,and finally the effectiveness and superiority of the method were verified by experiments.