in a large scope of fields, and a huge amount of papers related to DL have been published since 2012. This paper mainly focuses on a benchmark study of intelligent fault diagnosis, rather than providing a comprehensive review on DL for other fields. Some famous DL researchers have published more professional references and interested readers can refer to [@lecun2015deep; @goodfellow2016deep].

In the field of intelligent fault diagnosis, due to the efforts of many researchers in recent years, DL has become one of the most popular data-driven methods to perform fault diagnosis and health monitoring. In general, DL-based methods can extract representative features adaptively without any manual intervention and can achieve higher accuracy than traditional machine learning algorithms in most of the tasks when the dataset is large enough. We conducted a literature search using Web of Science with a database called web of science core collection. As shown in [Fig. \[Fig1\]]{}, it can be observed that the number of published papers related to DL-based intelligent algorithms increases year by year.

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Another interesting observation is that many review papers on this topic have been published in the recent four years. Therefore, in this paper, we only briefly review and 