A review of the application of deep learning in intelligent fault diagnosis of rotating machinery

With the rapid development of industry, fault diagnosis plays a more and more important role in maintaining the health of equipment and ensuring the safe operation of equipment. Due to large-size monitoring data of equipment conditions, deep learning (DL) has been widely used in the fault diagnosis of rotating machinery. In the past few years, a large number of related solutions have been proposed. Although many related survey papers have been published, they lack a generalization of the issues and methods raised in existing research and applications. Therefore, this paper reviews recent research on DL-based intelligent fault diagnosis for rotating machinery. Based on deep learning models, this paper divides existing research into five categories: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN). This paper introduces the basic principles of these mainstream solutions, discusses related applications, and summarizes the application features of various solutions. The main problems of existing DL-based intelligent fault diagnosis (IFD) research are summarized as small-size sample imbalance and transfer fault diagnosis. The future research trends and hotspots are pointed out. It is expected that this survey paper can help readers understand the current problems and existing solutions in DL-based rotating machinery fault diagnosis, and effectively carry out related research.

Automated summary

With the rapid development of industry, fault diagnosis plays an important role in maintaining equipment health and ensuring safe operation. This paper reviews recent research on deep learning-based intelligent fault diagnosis for rotating machinery and categorizes the existing research into five categories based on deep learning models. The paper introduces the principles, applications, and features of these solutions, summarizes the main problems, and points out future research trends and hotspots.