An open-set fault diagnosis framework for MMCs based on optimized temporal convolutional network

Abstract

Reliability of the modular multilevel converters (MMCs) provides a vital guarantee for the uninterrupted operation of a system. Insulated Gate Bipolar Transistors (IGBTs) open circuit fault diagnosis is a common challenge in MMCs applications. In this paper, a novel open-set fault diagnosis framework called Multiscale-AAM-OTCN is proposed to solve both the known and unknown fault diagnosis problems of MMCs by outputting current signals. First, batch normalization and layer normalization are introduced into the original Temporal Convolutional Network (TCN) model to accelerate convergence and promote the generalization ability of the model for different tasks. Second, to strengthen the feature extraction ability of the model, the multiscale coordinate residual attention (MCRA) mechanism is designed for the one-dimensional (1D) current signal to improve the performance and stability of the method. Compared with recently developed attention mechanisms such as convolutional block attention module (CBAM), efficient channel attention (ECA), simple, parameter-free attention module (SimAM) and coordinate attention (CA), the proposed MCRA exhibits better performance in MMCs fault diagnosis tasks. Finally, the additive angular margin (AAM) loss and local outlier factor (LOF) algorithm are integrated into the Multiscale-OTCN framework to identify the density difference between known and unknown fault clusters by controlling the intra-class similarity and inter-class variance of the samples. The experimental results demonstrate the feasibility of the proposed fault diagnosis framework for known and unknown fault diagnoses.