Effect and mechanism of phenyl content on the electrical insulation properties of silicone rubber

Key Words:ALGORITHM; LOCATION; FAILURE

Abstract:As a leak identification method in chemical processes, deep learning is limited by inadequate sample labels and highly coupled, nonlinear process variables, therefore difficult to model with a high identification accuracy. To address these problems, a fusion correlation -distance graph (CDG) coding aided deep learning method is proposed. First, the mechanism model is built based on dynamic process data with different leakage labels. Then, the weighted degree and spatial distribution of the dynamic dataset are computed through innovatively designed correlation and distance coding, aided by sliding window fusion into a correlation -distance matrix. Finally, the well -trained long short-term memory (LSTM) is constructed to learn the deep features of the leakage process and identify them. The applications in two industrial cases, CO 2 capture and ammonia synthesis, show that CDGLSTM presents state-of-the-art identification performance with R 2 scores of 0.994 and 0.986. 6.6 % and 18.4 % improvement over LSTM. In addition, the proposed method effectively categorizes the unknown leak samples into known labels with nearby locations, providing a valuable reference for operators to determine the unknown leak locations.

Volume:187

Issue:

Translation or Not:no