Sewage treatment process fault monitoring method of width learning network for efficiently extracting dynamic characteristics

The invention discloses a sewage treatment process fault monitoring method based on a width learning network for efficiently extracting dynamic characteristics. The method is used for solving the problem of inaccurate monitoring results caused by the dynamic characteristics and nonlinearity of sewage treatment process data. The method comprises an off-line training stage and an on-line monitoring stage. The off-line training comprises the following steps: firstly, integrating collected normal data and fault data into training data, carrying out dynamic expansion on the training data, then carrying out normalization processing, and then establishing an off-line training model by utilizing a width learning network. The online monitoring comprises the steps that newly-collected data is subjected to dynamic expansion and then subjected to normalization processing, and online monitoring is achieved through a model trained in an offline state. According to the method, the features including nonlinear and dynamic in