MS-TCN: A Multiscale Temporal Convolutional Network for Fault Diagnosis in Industrial Processes
Jiyang Zhang, Yuxuan Wang, Jianxiong Tang, Jianxiao Zou, Shicai Fan
Abstract
Fault diagnosis is an important way to ensure the operation security in complex industrial processes. Considering the inherent multiscale characteristics and time dependency about industrial process monitoring data, a novel fault diagnosis method based on multiscale temporal convolutional network (MS-TCN) was proposed in this paper. Firstly, different from the widely used time-domain features with one single scale, the multiscale time-frequency information extracted with the discrete wavelet transform was also introduced to represent the raw data. And a temporal convolutional network was then combined to capture longer-term temporal feature from the sequential processing data. The experimental results on the Tennessee Eastman process indicated that, our proposed method outperformed these state-of-the-art fault diagnosis methods, especially for the 3 incipient faults hard to classify.