Short communication: A case study of stress monitoring with non-destructive stress measurement and deep learning algorithms

Abstract

Non-destructive stress measurement is necessary to provide safety maintenance in some extreme machining environments. This paper reports a case study that reveals the potential application of automatic metal stress monitoring with the aid of the magnetic Barkhausen noise (MBN) signal and deep learning algorithms (convolutional neural network, CNN, and long short-term memory, LSTM). Specifically, we applied the experimental magnetic signals from steel samples to validate the feasibility and efficiency of two deep learning models for stress prediction. The results indicate that the CNN model possesses a faster training speed and a better test accuracy (91.4 %), which confirms the feasibility of automatic stress monitoring applications.