Research on an Abnormal Recognition Method of the UHV Reactor Based on the DAGMM

Abstract

The data of dissolved gas in oil is an important state parameter of the power reactor, and its content directly reflects the working state of the reactor. Fully considering the irregularity of the data distribution of dissolved gas in oil and the problem that the traditional algorithm model cannot effectively deal with high-dimensional data, this paper proposes an abnormal recognition method for UHV reactors based on the Deep Auto-encoding Gaussian Mixture Model (DAGMM). Based on historical detection data, the abnormal state of high-dimensional dissolved gas data in oil is realized using end-to-end training, combined with data dimensionality reduction capabilities of autoencoders and Gaussian mixture model clustering. It is verified with an example analysis that the proposed method can accurately identify the abnormal state of the UHV reactor.