A Mixed Feature Extractor for Intelligent Fault Detection and Diagnosis of Tunnel Diode Circuit Systems

Abstract

This paper presents a mixed feature extractor (MFE) for the fault detection and diagnosis of tunnel diode circuit systems described by Takagi-Sugeno (T-S) fuzzy model-based Markov jump systems (MJSs). A novel neural network model is constructed, which is composed of the 1-D convolutional neural network (CNN) and Transformer. In order to make full use of feature information, the 1-D CNN model is utilized to extract the local features, and the Transformer is established to obtain the global features. Then, the features taken from the MFE are concatenated and fed into a classification layer for fault detection and diagnosis. Finally, through experimental results, the proposed MFE is validated to be effective and outperform the commonly used diagnosis methods.