ICA-PCA multi-working condition fault diagnosis method based on local neighborhood standardization and Bayesian inference

The invention discloses an ICA-PCA multi-working condition fault diagnosis method based on local neighborhood standardization and Bayesian inference. The method firstly carries out independent sampling of each normal working condition during an industrial course to obtain a training dataset, carries out the local neighborhood standardization of the training dataset to obtain a dataset which follows single distribution, and then uses an ICA-PCA method to respectively analyze and process Gaussian features and non-Gaussian features of the dataset so as to obtain an overall model. At an online monitoring stage, independent and repeated sampling is carried out to industrial course data, a plurality of statistical quantities are acquired by applying the model to carry out analysis and processing after the local neighborhood standardization processing, then the multiple statistical quantities are combined into one statistical quantity by the Bayesian inference, and a fault diagnosis result is acquired by comparing control limits. In comparison with traditional fault diagnosis methods, the ICA-PCA multi-working condition fault diagnosis method based on the local neighborhood standardization and the Bayesian inference disclosed by the invention can simplify processing courses, improve diagnosis effects and improve course monitoring performance, and can also make workers' monitoring and observation convenient, make for avoiding safety hidden dangers and guarantee normal running of the industrial course.