DSSAE-based intermittent process fault diagnosis method

Abstract

The invention discloses a DSSAE-based intermittent process fault diagnosis method, and relates to the technical field of fault diagnosis of machine learning. The method comprises three stages of datapreprocessing, model training and fault diagnosis, wherein the'data preprocessing 'comprises the step of unfolding three-dimensional data in the fermentation process along the variable direction; secondly, corroding input data into a damaged data sample by utilizing random mapping, namely adding a noise reduction code; and finally, carrying out normalization processing on the corroded damaged datasample. And the model training comprises two processes of unsupervised pre-training and supervised tuning. And the fault diagnosis comprises the steps of inputting the collected test data into a trained network for fault diagnosis, outputting a fault category, and counting the accuracy. According to the DSSAE fault diagnosis method and device, only the data in the fault time period are filled, and the influence on the accuracy of DSSAE fault diagnosis due to the fact that too many unknowned data are manually filled is reduced. And meanwhile, the noise reduction codes are added, so that the robustness of the network is improved.

Description

technical field [0001] The invention relates to the technical field of machine learning fault diagnosis, in particular to an online fault diagnosis technology for batch processes. The method based on machine learning of the present invention is a specific application in the fault diagnosis of a typical batch process—penicillin fermentation process. Background technique [0002] As an extremely important production method in modern industrial processes, batch process has been widely used in the production of various and high value-added products such as medicine, food, biochemical industry, and semiconductor. However, in the actual process, due to a series of problems such as equipment aging and sudden changes in the external environment, failures occur from time to time. Therefore, fault diagnosis of batch process becomes very important to ensure the safety of production process and improve product quality. [0003] For the fault diagnosis of batch process, the methods com...

AI Technical Summary

Problems solved by technology

However, they can only achieve good classification results under small samples, and the robustness of the model is weak, and the accuracy of diagnosis is relatively low.

Images