An effective deep recurrent network with high-order statistic information for fault monitoring in wastewater treatment process

The wastewater treatment process (WWTP) is a complex biochemical reaction process that features highly nonlinear, non-Gaussian and time correlation. As a new monitoring method, deep recurrent neural net-work (DRNN) has an effective performance in dealing with the nonlinear and time correlation of the data, but it is insufficient in dealing with the non-Gaussian characteristics. In this study, an effective deep recurrent network with high-order statistic information (HSI-DRN) is proposed for solving the insufficiency in dealing with the non-Gaussian characteristics. The proposed method extracts the high-order statistics characteristics by the over-complete independent component analysis (OICA) method. After that, weights in DRNN can be trained based on the obtained high-order statistics information and their corresponding fault labels. Because of the architecture of the network, the ability of extracting the non-Gaussian characteristics by HSI-DRN can be improved by the high-order statistics characteristics. Finally, HSI-DRN can generate visual monitoring results by discretizing the output data, which leads to more intuitively reflection of faults. Simulation studies on the BSM1 model has been performed to verify the performance of the method. For the different faults, the proposed method have higher fault monitoring ability with average false alarm rate (FAR) and missed detection rate (MAR) for respective 0.0215% and 0.586% when compared to other state-of-the-art fault monitoring methods.