and compare different DL-based intelligent diagnosis algorithms to provide the benchmark or the lower bound of their accuracies and performance, thereby helping further studies in this field for more persuasive and appropriate algorithms.

For comprehensive performance comparisons and evaluation, it is important to gather different kinds of datasets. Actually, there exist several datasets for intelligent fault diagnosis. However, not every dataset provides a detailed description and is suited for the fault classification. For some datasets, the category discrimination is relatively large, and even one simple classifier can achieve acceptable results. Therefore, to thoroughly perform data mining and assess the difficulty of datasets, it is necessary to collect different datasets in a library and evaluate the performance of algorithms for different datasets on a unified platform.

In addition, one common issue in intelligent fault diagnosis is that for splitting data, and researchers often use the random split strategy. This strategy is dangerous since if the preparation process exists any overlap for samples, the evaluation of classification algorithms will have test leakage [@riley2019three]. As for industrial data, they are rarely random and are always sequential (they might contain trends in time domain). Therefore, it is more appropriate to split data according to 