A Fault Diagnosis Approach Integrated LPP with AROMF for Process Industry

Abstract

As the data collected in the industrial process presents high-dimensional and nonlinear characteristics, it brings great challenges to the realization of timely and effective fault diagnosis. In this article, a fault diagnosis method is proposed integrated local preserving projections (LPP) with adaptive rank-order morphological filter (AROMF). First, in order to deal with the problem of high-dimensional and non-linearity of data, LPP algorithm is used to extract the required template trend and test trend. Second, AROMF performs morphological transformation on the test trend under the supervision of the template trend to obtain the output trend signal. Third, the iterative total error of the output trend and the corresponding template trend is calculated to classify the fault. Finally, the proposed method is verified by simulation on the Three-phase flow facility (TFF) dataset. The simulation results prove that this method can improve the accuracy of fault diagnosis.