sparse decomposition) and AI-based methods (KNN, neural network, and SVM).

In machinery condition monitoring, Zhao et al. [@zhao2016research] and Duan et al. [@duan2018deep] reviewed diagnosis and prognosis of mechanical equipment based on DL algorithms such as DBN and CNN. Zhang et al. [@zhang2017comprehensive] reviewed computational intelligent approaches including ANN, evolutionary algorithms, fuzzy logic, and SVM for machinery fault diagnosis. Zhao et al. [@zhao2019deep] reviewed data-driven machine health monitoring through DL methods (AE, DBN, CNN, and RNN) and provided the data and codes (in Keras) about an experimental study.

In addition, Nasiri et al. [@nasiri2017fracture] surveyed the state-of-the-art AI-based approaches for fracture mechanics and provided the accuracy comparisons achieved by different machine learning algorithms for mechanical fault detection. Tian et al. [@tian2018review] surveyed different modes of traction induction motor fault and their diagnosis algorithms including model-based methods and AI-based methods. Khan et al. [@khan2018review] provided a comprehensive review of AI for system health management and emphasized the trend of DL-based methods with limitations and benefits. Stetco et al. [@stetco2018machine] reviewed machine learning approaches applied to wind turbine condition monitoring and made a discussion of the possibility for the future research. Ellefsen et al. [@ellefsen2019comprehensive] reviewed four well-established DL algorithms including AE, 