including MLP, AE, CNN, and RNN for different datasets and provide the benchmark accuracy to make the future studies in this field more comparable and meaningful. We also use the experimental examples to discuss the existing problems in intelligent fault diagnosis including class imbalance, generalization ability, interpretability, few-shot learning, and model selection problems.

3)  *Open source codes*. For enhancing the importance and reproducibility of DL-based intelligent diagnosis algorithms, we release the whole evaluation codes in a code library for the better development of this field. At the same time, this is a unified intelligent fault diagnosis library, which retains an extended interface for everyone to load their own datasets and models by themselves to carry out new studies. The code library is available at <https://github.com/ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark>.

The outlines of the paper are listed as follows: In Section \[S:2\], we give a brief review of recent development of DL-based intelligent diagnosis algorithms. Then, Sections \[S:3\] to \[S:9\] discuss the evaluation algorithms, datasets, data preprocessing, data augmentation, data split, evaluation methodologies and evaluation results, respectively. After that, Section \[S:10\] makes some further discussions and the results, followed by conclusions in Section \[S:11\].

Brief Review {#S:2}
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Recently, DL has become a promising method 