Acoustic emission and LSTM based end-to-end fault diagnosis of gas high-pressure regulator

In the gas transmission and distribution system, the gas pressure regulator is in a key position for the timely and effective diagnosis of its fault types. In this paper, a long short term memory network (LSTM) based end-to-end fault diagnosis method is proposed, in which the acoustic emission signals is used to diagnose the operation condition of the gas pressure regulator. First, a second-order Butterworth high-pass filter is designed to preprocess the acquired acoustic emission signals. Then, by using the memory characteristics of long short term memory networks, an e2e-LSTM fault diagnosis model is established. The experimental results show that, with the input acoustic emission signals collected from the high-pressure regulator station, the model can diagnose five kinds of high-pressure regulator faults at a time in the end-to-end mode.