LSTM-Based Intelligent Fault Detection for Fuzzy Markov Jump Systems and Its Application to Tunnel Diode Circuits
Min Xue, Huaicheng Yan, Meng Wang, Hao Shen, Kaibo Shi
Abstract
This brief investigates the intelligent fault detection problem for a class of Takagi-Sugeno fuzzy Markov jump systems. The considered systems are modeled by a set of fuzzy rules and membership functions. With the help of deep learning technique, an intelligent fault detection method based on long short-term memory (LSTM) neural network with an attention mechanism is proposed. The improved LSTM model can accurately capture the fault features by making full use of previous information. Finally, the proposed approach is applied to the tunnel diode circuits and corresponding experiments are conducted to demonstrate its effectiveness, which achieves better performance in comparison with other conventional method for detection.