Incipient fault detection and diagnosis of nonlinear industrial process with missing data

Abstract

BackgroundIn real industrial process, timely detection and diagnosis incipient fault is often more meaningful. At the same time, due to sensor failures or data acquisition system failures, process data may be missing or corrupted, resulting in loss of process information. MethodsIn view of the above problems, a Mixed Kernel function Dissimilarity Neighborhood Preserving Embedding (MKDNPE) method is proposed. Firstly, Low Rank Matrix Decomposition (LRMD) is used to recover the missing data, the recovered low rank matrix contains the main information of the process. Then, the MKDNPE model is developed in the recovered low rank matrix, where the mixed kernel function is composed of a Gaussian radial basis kernel function and a polynomial kernel function. It can simultaneously extract the local information of process data and the global characteristics of data structure, and deal with the nonlinear characteristic of process. Finally, the dissimilarity statistic is introduced for incipient fault detection, and the method based on contribution chart is used for fault diagnosis. Significant findingsA numerical example and two benchmark processes are carried out for simulation verification. The simulation results further verified that the proposed method has good detection and diagnosis capabilities for incipient nonlinear faults in industrial processes with missing data.