Root cause diagnosis for process faults based on multisensor time-series causality discovery. (February 2023)

• Record Type: Journal Article
• Authors: Wang, Sheng; Zhao, Qiang; Han, Yinghua; Wang, Jinkuan
• Is Part Of: Journal of process control. Volume 122(2023)
• Page Start: 27
• Page End: 40
• Publication Date: 2023-02
• Subjects: Root cause diagnosis -- Time-series -- Causality discovery -- Dilated convolutional neural network -- Layerwise relevance propagation; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.12.006
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: Root cause diagnosis (RCD) focuses on locating the critical root causes and identifying the propagation paths of the industrial process faults, which is superior to conventional fault diagnosis methods and has attracted extensive attention. However, RCD is challenging in terms of dense sensor layout and diversified interconnected subsystems in complex industrial processes, since a fault originating in one unit can propagate throughout the entire plant along with the flow of information and material, obscuring its root causes. Moreover, the previous RCD methods ignore the more crucial temporal information between multisensor time-series, suffering from the disadvantages of requiring expert knowledge and responding untimely. Therefore, a novel data-driven RCD methodology named multisensor time-series causality discovery (MTCD) is proposed for industrial processes fault diagnosis. Firstly, a temporal registration network (TRN) based on the dilated convolutional neural network (DCNN) is proposed to extract the temporal correlation between time-series and implement the time-series prediction. Ulteriorly, a permutation importance causality validation (PICV) is designed to verify the causality between time-series based on the TRN prediction results without prior knowledge or expert experience. Besides, a causal time delay discovery approach based on layerwise relevance propagation (LRP) is presented by interpreting the weights of the trained TRN. Finally, we demonstrate the effectiveness of the proposed method with a stochastic synthesis process and the Tennessee Eastman process. The experimental results show that the proposed method has better performance in both root cause localization and propagation path identification. Highlights: Fault causes are obscured in complex industrial processes. Locates the fault's root cause and identifies how it propagates. The identification of fault root causes facilitates the orientation of manipulation. Causal delay reveals the delay before an operation takes effect.