CNN, DBN, and LSTM for PHM applications and discussed the chances and challenges for the future studies, especially in the field of PHM in autonomous ships. AI-based algorithms (traditional machine learning algorithms and DL-based approaches) and applications (smart sensors, intelligent manufacturing, PHM, and cyber-physical systems) were reviewed in [@ademujimi2017review; @chang2018review; @wang2018deep; @sharp2018survey] for smart manufacturing and manufacturing diagnosis.

Although a large body of DL-based methods and many related reviews have been published in the field of intelligent fault diagnosis, few studies thoroughly evaluate various DL-based intelligent diagnosis algorithms for most of the publicly available datasets, provide the benchmark accuracy, and release the code library for complete evaluation procedures. For example, a simple code written in Keras was published in [@zhao2019deep], which is not comprehensive enough for different datasets and models. The accuracy comparisons were provided in [@zhang2019machine; @nasiri2017fracture] according to existing papers, but they were not comprehensive enough due to different configurations and test conditions. Therefore, this paper is intended to make up for this gap and emphasize the importance of open source codes and the benchmark study in this field.

Evaluation Algorithm {#S:3}
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A large amount of DL-based intelligent diagnosis methods have been published in the field of 