Multimode complex process monitoring using double-level local information based local outlier factor method

Industrial processes typically have multiple operating modes with complex data distribution and locality faults, which challenges the traditional multivariate statistical process monitoring methods. To address this problem, a double-level local information-based local outlier factor (LOF) method is proposed in this work for multimode complex process monitoring. First, to handle the multimodality, the local neighborhood standardization strategy is adopted to utilize the statistical information of local data structure. Second, the variable LOF method is proposed to determine reasonable boundary for complex data distribution and simultaneously reflect local variable behaviors. For better online monitoring, a weighting strategy is applied to emphasize the local variable information, and the Bayesian inference is employed to integrate the LOF value of each variable. To isolate the fault variables, a contribution plot is designed. Finally, a numerical example and the benchmark Tennessee Eastman process are used to demonstrate the effectiveness and superiority of the proposed method. A novel double-level local information-based local outlier factor (DLI-LOF) method is proposed to monitor the multimode process effectively. The local neighborhood standardization (LNS) strategy is adopted to handle the multimodality, while the variable local outlier factor (VLOF) method is developed to determine reasonable boundary for complex data distribution. For better online monitoring, a weighting strategy is applied to emphasize the local variable information, and the Bayesian inference is employed to integrate the LOF values of each variable.