Machine Learning Approach for Shaft Crack Detection through Acoustical Emission Signals

A research approach of crack detection of rotating shafts based on acoustic emission (AE) signals and machine learning is proposed in this paper. The relationship between crack intensity and domain features are investigated, and the features which could well indicate the crack condition are selected for modelling and crack prediction. Multiple Linear Regression (MLR), Artificial Neural Networks (ANN) and Adaptive Neural-Fuzzy Inference System (ANFIS) methods are used to establish the predictive correlation models by using selected features. A case study is carried out to emulate online working conditions of rotating shafts by using 10 normal shafts with 0.8 mm - 8 mm crack intensities. It is proved that AE signals can be used for earlier crack intensity detection, for example 0.8 mm - 2.4 mm cracks can be fully detected according to experimental results in this study. Different modelling methods are also compared and discussed. Results show that ANFIS is a good choice in terms of overall predictive accuracy for earlier crack detection and prediction.