introduce the main contents of different review papers to allow readers who just enter this field to find suitable review papers quickly.

In bearing fault diagnosis, Li et al. [@li2018systematic] provided a systematic review of fuzzy formalisms including combination with other machine learning algorithms. Hoang et al. [@hoang2019survey] provided a comprehensive review of three popular DL algorithms (AE, DBN, and CNN) for bearing fault diagnosis. Zhang et al. [@zhang2019machine] systematically reviewed the machine learning and DL-based algorithms for bearing fault diagnosis and also provided a comparison of the classification accuracy of CWRU with different DL-based methods. Hamadache et al. [@hamadache2019comprehensive] reviewed different fault modes of rolling element bearings and described various health indexes for PHM. Meanwhile, it also provided a survey of artificial intelligence methods for PHM including shallow learning and deep learning.

In rotating machinery intelligent diagnosis, Ali et al. [@ali2016acoustic] provided a review of AI-based methods using acoustic emission data for rotating machinery condition monitoring. Liu et al. [@liu2018artificial] reviewed Al-based approaches including KNN, SVM, ANN, Naive Bayes, and DL for fault diagnosis of rotating machinery. Wei et al. [@wei2019review] summarized early fault diagnosis of gears, bearings, and rotors through signal processing methods (adaptive decomposition methods, WT, and 