Dynamic Minimax Probability Machine-Based Approach for Fault Diagnosis Using Pairwise Discriminate Analysis

Fault diagnosis plays a key role in the safe and efficient operation of industrial processes. With the emerging big data era, the analytic methods based on probabilistic representations have attracted growing research interest. In this brief, a dynamic minimax probability machine (DMPM) approach based on the framework of probabilistic representations is proposed for diagnosing process faults, without imposing any assumptions on data distributions. In addition, an information criterion is put forward to determine the optimal dimensionality reduction order and time lags of DMPM. The proposed DMPM-based method allows for the enhanced performance of fault diagnosis due to the following advantages over conventional diagnostic approaches. First, DMPM maximizes the pairwise separation probability between each pair of faulty data sets, directly yielding improved discriminatory power in the projected space. Second, the proposed approach is less likely to be influenced by “outlier” classes since its objective function is a summation of probabilities, thereby enabling it to be beneficial for the classification of imbalanced data. Third, DMPM has superior capability on capturing dynamic information from the process data by augmenting observation vectors with time lags. The effectiveness of the proposed approach is demonstrated on the Tennessee Eastman process.