  Recent progress on intelligent fault diagnosis (IFD) has greatly depended on
deep representation learning and plenty of labeled data. However, machines
often operate with various working conditions or the target task has different
distributions with the collected data used for training (the domain shift
problem). Besides, the newly collected test data in the target domain are
usually unlabeled, leading to unsupervised deep transfer learning based
(UDTL-based) IFD problem. Although it has achieved huge development, a standard
and open source code framework as well as a comparative study for UDTL-based
IFD are not yet established. In this paper, we construct a new taxonomy and
perform a comprehensive review of UDTL-based IFD according to different tasks.
Comparative analysis of some typical methods and datasets reveals some open and
essential issues in UDTL-based IFD which are rarely studied, including
transferability of features, influence of backbones, negative transfer,
physical priors, etc. To emphasize the importance and reproducibility of
UDTL-based IFD, the whole test framework will be released to the research
community to facilitate future research. In summary, the released framework and
comparative study can serve as an extended interface and basic results to carry
out new studies on UDTL-based IFD. The code framework is available at
\url{https://github.com/ZhaoZhibin/UDTL}.
