Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review

Abstract

With the increase of the scale and complexity of mechanical equipment, traditional intelligent fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the demand of coupling faults. In the past decades, the vigorous development of deep learning (DL) brings new opportunities for IFD, especially the representation learning based on Autoencoder (AE) theory has been widely applied. To provide a more comprehensive reference, the theoretical foundations of multi-type AEs and the training method of stacked autoencoder (SAE) are briefly introduced. Then the application advances of AE are reviewed from optimization and combination aspects, which are aiming at improving the representation learning ability. To provide ways for the application of AE-based methods, two typical study cases for ideal and complex engineering systems are illustrated respectively. Finally, the challenges and prospects of AE-based representation learning are reported from four aspects, which give a guidance for the future research direction.