Noise-containing nonlinear process fault detection model construction method and detection method thereof

The invention relates to the technical field of fault detection methods based on manifold learning, in particular to a noise-containing nonlinear process fault detection model construction method and a detection method thereof. The construction method comprises the following steps: carrying out standardization processing on a sample data set to obtain a calibration data set of the sample data set; calculating the standard deviation and the standard residual error of the calibration data set according to the calibration data set, removing noise data in the calibration data set to obtain a normal data set, and calculating a low-dimensional representation data set of the normal data set; and according to the low-dimensional representation data set, establishing a fault multi-classification model by using a support vector machine algorithm. The method is based on mathematical statistics, and regression analysis is carried out on manifold characteristics of original data by using standardized residual errors, nois