# A Time-Frequency Image Quality Evaluation Method Based on Improved LIME
## 4 Conclusions  
Automatic and intelligent diagnosis with deep learning technology has become a hot topic in the field of fault diagnosis. However, there is a lack of discussion on network input. Different from general image input, time-frequency images often lack easy-to-describe feature components, so a new image quality evaluation method is urgently needed. To solve this problem, this paper proposes a time-frequency image quality evaluation method based on an improved LIME method, which comprehensively considers the accuracy of the diagnosis results and the consistency of the interpretation results with prior knowledge. Accuracy shows the decision-making ability of the model for the target task, while consistency explains the decision-making basis of the model, and this interpretation behavior gives the engineer a reason to choose or not to choose the model. The two together constitute the quality evaluation standard of time-frequency images.  
Experiments have been carried out on the measured data set, and the quality of the images obtained by five kinds of time-frequency transform has been evaluated, which proves the feasibility of the proposed method.  
It should be noted that the evaluation method proposed in this paper is only for the application scenario in which time-frequency images are used as inputs for deep learning networks. The experimental results show that in this application scenario, more complex time-frequency conversion methods do not result in higher quality. However, in other applications or scientific research scenarios, the pursuit of higher time-frequency resolution and energy concentration is still a very important research direction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time–Frequency Transform Method** & **Accuracy** & **Consistency** & **Quality Score** \\ \hline Short-Time Fourier Transform & 0.9300 & 0.0449 & 0.8851 \\ Wavelet Transform & 0.9560 & 0.2407 & 0.7153 \\ Winger–Ville Transform & 0.8820 & 0.0827 & 0.7993 \\ Empirical Mode Decomposition & 0.8100 & 0.1364 & 0.6736 \\ Synchrosqueezing Transform & 0.8960 & 0.4689 & 0.4262 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quality evaluation of five time–frequency images.  
Figure 9: The interpretation of the judgment basis of the target task in the original model with five kinds of time–frequency images as the input. The larger the value in the x direction, the greater the influence of the corresponding feature on the model’s decision making.  
**Author Contributions:** Conceptualization, Y.B.; methodology, Y.B.; software, Y.B. and Y.L.; validation, Y.B. and Y.L.; data curation, Y.B.; writing--original draft preparation, Y.B.; writing--review and editing, W.W.; supervision, W.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the anti-wear and anti-fatigue mechanism of aeroengine bearing steels with strength and toughness improved by the synergism of deep cryogenic temperature and ion implantation project, grant number 52375163.  
**Data Availability Statement:** Restrictions apply to the availability of these data. Data were obtained from the School of Mechanical and Electronic Control Engineering, Beijing Jiaotong University, and are available from the authors with the permission of the School of Mechanical and Electronic Control Engineering, Beijing Jiaotong University.  
**Conflicts of Interest:** The authors declare no conflicts of interest.