SABO优化RCA-BiLSTM模型在复杂工业过程故障预测中的应用OA

Due to the time-varying characteristics caused by the drift of working conditions in complex industrial processes,in or-der to solve the problem of low fault prediction accuracy,a hybrid fault prediction model based on SABO-RCA-BiLSTM was con-structed.Firstly,the random forest algorithm was used to analyze the importance of the features and perform data filtering to re-duce data redundancy and retain key features.Then,the Convolutional Neural Network(CNN)was introduced to solve the prob-lem that the Bidirectional Long Short-Term Memory(BiLSTM)neural network cannot capture spatial features when facing multi-dimensional feature input,and the attention mechanism was added to assign different attention weights to each part of the input feature sequence to enhance the attention to the key information.Finally,the Subtractive Average-Based Optimization(SABO)algorithm was used to optimize the model parameters to further improve the fault prediction performance.The model was verified on the Tennessee-Eastman(TE)process.The results show that in the face of two different types of faults,the average ab-solute error of the optimized model is reduced by 32％and 30％respectively compared with which before optimization.Com-pared with CA-BiGRU,CA-BiLSTM,MVMD-CA-BiLSTM and SAC-BiLSTM models,the fault prediction accuracy determination coefficient is increased by 23.70％at most,which effectively solves the problem of low fault prediction accurary in complex in-dustrial processes.