A novel dynamic distance coding identification method for oil–gas gathering and transportation process. (May 2023) - Liu, Zijian; Tian, Wende; Liu, Bin; Cui, Zhe
Abstract: Deep learning (DL) has become a mainstream method for fault identification in petrochemical processes. However, the high noise and nonlinear coupling of complex data samples have led to different degrees of low accuracy and robustness problems in the method. Meanwhile, the fault cause is difficult to capture due to the complex chemical process operation mechanism. To address this challenge, a novel dynamic distance coding method incorporating DL is proposed to identify anomalies in real time. First, the collected normal process data are smoothed by the Savitzky–Golay filter to build a normal sample set. Then, dynamic coding based on the distance metric is introduced to compute the distribution of normal and real-time samples for extracting the spatial domain features. By a sliding window, dynamic coded maps are generated and analyzed for fault causes. Finally, the time-domain information is extracted by long short-term memory (LSTM) to learn the deep features of the encoded graph for fault identification. The proposed method was applied to an oil–gas gathering and transportation process, which proves its feasibility and effectiveness. Compared with the conventional LSTM, the F1 score of the method is improved by 0.193, reaching 0.986. The obtained visualization information enables explaining the causes and supplements the fault database, providing a valuable reference for workers' feedback operations. Highlights: A dynamic distance coding identification method is proposed to identify fault. The distance metric is used to make process features dynamic and displayable. The dynamic coding provides a mechanistic explanation of the fault. The F1 score of ED-LSTM under all conditions reaches 98.6%.