Early sub-surface fault detection in rolling element bearing using acoustic emission signal based on a hybrid parameter of energy entropy and deep autoencoder

Abstract

Bearings are a crucial component of wind turbines. The acoustic emission (AE) technique offers the advantage of earlier detection of defects and failures of bearings in comparison to traditional vibration techniques. Parameter-based analysis is the most widely used approach to interpret AE waveforms, partly due to the challenges arising in the processing of large amounts of streaming data. In this work, the AE technique is applied to monitor a run-to-failure process of a roller bearing, and it is found that the use of multiple known parameters, such as the root mean square, skewness, crest factor, impulse factor etc, fails to characterise the evolution of the acquired AE signals, thus highlighting the long-standing necessity and significance of developing new AE indicators that are more adequate to detect the failure of rotating machines. We propose a hybrid parameter—the information entropy penalty factor (IEPF)—which uses the advantages of the entropy theory and deep learning methods. The effectiveness of the proposed method has been investigated and demonstrated for roller bearing contact fatigue experiments, and the results show that IEPF can timely and accurately detect the incipient sub-surface faults.