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Kernel principal component analysis (KPCA) has been widely applied to the nonlinear process fault diagnosis field. However, it often does not perform well in the case of incipient faults because of the omission of local data information. To overcome this problem, one enhanced KPCA method, called the two-step localized KPCA (TSLKPCA), is proposed for incipient fault diagnosis in this work. The two steps are designed to mine the local data information better. At the first step, the KPCA optimization objective is modified by integrating the local structure preservation so that the extracted kernel components preserve the global and local data structure information simultaneously. At the second step, for the extracted kernel components, the local probability information is further mined by the Kullback Leibler divergence (KLD), which measures the variations of the kernel components’ probability distributions. On the basis of these two steps, the original kernel components are transformed into the KLD components, and the corresponding model is developed for incipient fault detection. To isolate the faulty variables, the contribution plot is constructed based on the mutual information between the measured variables and the KLD components obtained by TSLKPCA. Finally, two simulations of a numerical example and the continuous stirred tank reactor (CSTR) control system show that the proposed method has good incipient fault detection and diagnostic performance.

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.INDEX TERMS Incipient fault, fault detection, kernel principal component analysis, statistical local analysis, prior fault information.

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