WASTEWATER TREATMENT PROCESS FAULT MONITORING METHOD USING OICA-RNN FUSION MODEL

A smart fault monitoring method based on a high-order information-enhanced recurrent neural network, said method being used to carry out real-time monitoring of wastewater treatment process faults, and comprising two phases: offline training and online soft measurement. The offline phase first uses OICA to extract raw data into high-dimensional high-order information features, used for the effective processing of non-Gaussian properties of the data and determining the correlation between variables. Then training of the extracted features is carried out by means of a DRNN. In the online phase, the data are mapped directly into new high-order feature components and are classified and discriminated by the DRNN that was trained offline. If the results are fault-free, then a monitoring model composed only of OICA is used to carry out unsupervised monitoring. If a fault has still not been detected at this point, the process is determined to be fault-free, and if a fault occurs, then the process is determined to be