Fault detection of petrochemical process based on space-time compressed matrix and Naive Bayes

Due to the high available and reliable requirements of petrochemical processes, it is critical to develop real-time fault detection approaches with high performance. Some machine learning approaches have shown good results but the learning process is too complicated to meet the requirements of online application, such as plenty of samples or the laborious hyper-parameter optimization is needed. In this paper, a fault detection approach based on space-time compressed matrix (STCM) and Naive Bayes (NB) is proposed to realize the fast learning and prediction. First, the slowly varying features which reflect the inherent dynamic information of petrochemical processes are extracted by slow feature analysis. Second, the accumulative importance of slow features and the reconstructive advantage of slow feature under-sampling are proposed to achieve the space-time compression of data matrix. Finally, the STCM is employed to establish the NB model, which can significantly reduce the learning complexity while ensuring classification performance. Experiments on the Tennessee Eastman benchmark show that the proposed approach reduces the sample-size and feature-size by 75% and 92% respectively. Both the average classification accuracy and F1 score on 21 faults exceed 84%, achieving the state-of-the-art results among the comparative approaches.