HAGC System Fault Multi Criteria Diagnosis Research Based On Data Fusion

As a kernel technology of modern strip rolling mill-Hydraulic Automatic GaugeControl system(HAGC system), gathering mechanism, electric, and hydraulic pressuretechnique, has the attribute of complicated and high precision, high integration inconfiguration and costly in price. The working state of HAGC has the important effect onthe quality and output of rolling production and decides the performance of rolling milldirectly. HAGC system is a complicated dynamics system in running state, has thecharacteristics of uncertainty, non-linearity and time-variety. It's complexity bring manydifficulties to state monitor and fault analysis. So, research on HAGC system faultdiagnosis technology has important theoretical and practical significance.From the perspective of extracting three independent fault signal characters:transient, steady and singularity, multi diagnosis criterions are developed in this paper.Information fusion is researched based on the new methods in order to integratemulti-angle information. The result of simulation and fault example diagnosis verify thecorrectness and feasibility of the proposed methods.Analyzing the rolling course of continuous rolling mill, it is proposed that under thecondition of needn't work stop and external exciting signal, bitting course and abruptcontrol input course can be taken as step input signal. So, transient response can beachieved on line. The numerical property algorithm is established by contrasting theeffect of frequent faults to transient response performance index with the parameters onhealthy states.Based on system mathematical model and unknown inputs observer theory, HAGCsystem observer group diagnosis method is established after different fault directions aredecided by analyzing with fault state equation. Fault isolation is achieved by combinationlogic. Numerical algorithm is proposed which make the criteria has the numericalproperty. Theory analysis and experimental research have proved that observer groupdiagnosis method is effective in extracting fault steady information. In addition, loaduncertainty decoupling subsystem of nonlinear model for HAGC system is establishedbased on differential geometry theory and nonlinear observer is designed. Limited to thediagnosticable fault mode, it is taken as a subsidiary check to linear observer groupmethod.Based on history data of rolling course, an RBF network rolling force model anddynamic recurrent network model based on ARMA model for each loop of screw downsystem are established respectively. Network structure and learning algorithm areoptimized, then the convergence rate and generalizing capability of networks areimproved. In order to exclude the error alarm problem caused by abrupt input, systemMA model is introduced and decomposed by wavelet transform, by which accurate faultinformation can be achieved. In this paper, wavelet transform is chosen as secondarydealing to the abnormity of network monitor, which can solve the defect of largecalculation and bad real time problem of wavelet transform. Owing to the sensitivity tosingularity information, wavelet is chosen as singularity information criteria andcorresponding numerical algorithm is proposed.Considering the advantages of D-S evidence theory at dealing withmulti-information, multi-criteria fusion is carried out using D-S evidence theory.Reasonable basic probability assignment algorithm is constructed with fault measuredegree, by which the shortcoming of basic probability assignment depending on expertknowledge and has excessive subjectivity is eliminated. Diagnosis example verify thatinformation fusion reduce the uncertainty, improve the diagnosis belief degree anddiagnosis system capability is improved greatly.