Twofold Weighted-Based Statistical Feature KECA for Nonlinear Industrial Process Fault Diagnosis

Abstract

In order to ensure the safe operation of industrial systems, the timely diagnosis of incipient faults is gradually gaining attention. The kernel entropy component analysis (KECA) has been widely used in the fault diagnosis of nonlinear industrial processes. However, the KECA often performs unsatisfactorily in the case of incipient faults. Therefore, a novel incipient fault detection and diagnosis method based on the statistical feature KECA integrating the twofold weighted (TWSFKECA) is proposed. The residual function in the local approach is combined with the KECA to construct statistical features of the data. Then, in order to highlight the influence of incipient faults of statistical features, the statistical feature sample weighting strategy is established based on the dissimilarity analysis between the test and training samples. Furthermore, the statistical feature component weighting strategy is developed for the sensitive components, which are judged by applying the Durbin-Watson (DW) criterion to calculate the extent to which the sample-weighted statistical feature components contain significant information. Moreover, based on the statistical features of twofold weights, two statistics indexes are created for incipient fault detection. In addition, the strategy for process fault diagnosis using a variable contribution plot method is proposed to isolate faulty variables. Finally, the continuous stirred tank reactor control system and the Tennessee Eastman process illustrate the superiority of the proposed method for incipient fault detection and diagnosis. Note to Practitioners—Effective detection of incipient faults prevents the evolution of accidents and ensures the smooth operation of the production process. In nonlinear industrial processes, the statistical feature KECA integrating the twofold weighted is proposed for incipient fault detection and diagnosis. The residual function is introduced in the kernel entropy component analysis to construct the statistical features of the data, which helps extract the incipient fault information. Then, the twofold weighting strategy weights the statistical features in terms of samples and components, highlighting the influence of the main samples and sensitive components in the incipient faults, respectively. In addition, a variable contribution plot method is developed to solve the problem of not being able to find out the cause of faults through control plots. The experimental results further verify the applicability of the proposed method for monitoring the occurrence of incipient faults.