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

This brief 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 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.