Abstract
Statistical local kernel principal component analysis (SLKPCA) has demonstrated its success in incipient fault detection of nonlinear industrial processes by incorporating the statistical local analysis (SLA) technology. However, the basic SLKPCA method builds the statistical model only based on the normal data and neglects the utilization of the prior fault information, which is often available in many industrial cases. To take full advantage of the prior fault information, this paper proposes an enhanced SLKPCA method, called primary-auxiliary SLKPCA (PA-SLKPCA), for better incipient fault monitoring. The contribution of the proposed method includes three aspects. First, one primary-auxiliary statistical monitoring framework is designed, by which not only the normal training data are applied to develop a primary SLKPCA model, but also the prior fault data are used to build the auxiliary SLKPCA models. Second, a double-block modeling strategy is developed to construct the auxiliary SLKPCA model for each fault case, where a variable grouping strategy based on Kullback-Leibler divergence is applied to divide the process variables into the fault-relevant group and fault-independent variable group, and the sub-model is developed for each group. Third, the Bayesian inference is used to combine the statistical results of each variable group, and one weighted fusion strategy is further designed to integrate the monitoring results from the primary and auxiliary models. Lastly, two case studies including one numerical system and the simulated continuous stirred tank reactor (CSTR) system are used for method evaluation and the simulations show that the proposed method can detect the incipient faults effectively and outperform the traditional SLKPCA method.
Highlights
Due to the higher requirements for the safety and continuity in the modern industries, the fault diagnosis technologies become the focus of attention in the field of process system engineering
To handle the issue of the nonlinear process incipient fault monitoring, Ge et al [32] developed the statistical local kernel principal component analysis (PCA) (SLKPCA) by integrating statistical local analysis (SLA) with KPCA and the applications show that Statistical local kernel principal component analysis (SLKPCA) is more effective than the traditional KPCA in terms of incipient fault detection of the benchmark Tennessee Eastman process
This paper proposes an improved SLKPCA method PA-SLKPCA for incipient fault detection
Summary
Due to the higher requirements for the safety and continuity in the modern industries, the fault diagnosis technologies become the focus of attention in the field of process system engineering. As its influence is unclear and underlying at the initial phase, it is often extremely difficult to detect by the basic PCA and KPCA methods [18] Based on these characteristics, the incipient fault detection problem is one of the most challenging tasks in the process monitoring field. For detecting the incipient sensor faults in the multimode processes, a modified recursive transformed component statistical analysis method was developed by Shang et al [27] by utilizing conditionally independent Bayesian learning. To handle the issue of the nonlinear process incipient fault monitoring, Ge et al [32] developed the statistical local kernel PCA (SLKPCA) by integrating SLA with KPCA and the applications show that SLKPCA is more effective than the traditional KPCA in terms of incipient fault detection of the benchmark Tennessee Eastman process. When one of these two statistics exceeds the corresponding confidence limit, it means one fault is detected
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