## 5 Conclusions  
In this paper, an approach for the bearing fault severity monitoring based on the texture feature extraction of sparse TFIs was proposed. The STFA-PD was developed to obtain the sparse TFIs. Then, the GLCM-based texture features of the sparse TFIs were extracted for an automatic classification to realize fault severity monitoring. The developed STFA-PD was validated using a simulated signal. The result showed that the STFA-PD method can overcome the drawbacks of the traditional time-frequency analysis methods, such as low resolution and cross-term interference. Moreover, it was found that the STFA-PD method is better in terms of denoising performance. Therefore, the STFA-PD method can be used to obtain high-quality TFIs without suffering from cross-term interference. Since the STFA-PD method is a universal time-frequency analysis method, it can be used to analyze other non-stationary signals. Experiments were conducted on the popular CWRU bearing dataset to validate the effectiveness of the proposed bearing fault severity monitoring approach. It was found that the STFA-PD method obtained the most high-quality TFIs of bearing vibration signals. As a result, the proposed approach obtained higher overall accuracy than those based on STFT, PWVD, and WT. This study clearly showed that the proposed approach has significant potential to be a powerful tool for the fault severity monitoring of rolling bearings and can be applied to other rotating machinery such as gearboxes and rotors. However, there were some limitations to the proposed approach. One is that a training feature set is needed for this approach. In practice, this set is often difficult to obtain, which restricts the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \cline{2-5}  & **Test 1** & **Test 2** & **Test 3** \\ \hline STFA-PD & & 99.50 & 99.25 & 83.75 \\ STFT & & 97.00 & 97.00 & 77.25 \\ PWVD & & 85.75 & 94.00 & 75.00 \\ WT & & 91.50 & 98.75 & 74.00 \\ \hline STFA-PD & & 100.00 & 100.00 & 96.50 \\ STFT & LS-SVM & & 100.00 & 100.00 & 84.25 \\ PWVD & & 98.25 & 99.00 & 84.50 \\ WT & & 100.00 & 100.00 & 93.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of bearing fault severity recognition. application of the approach. Future work will focus on monitoring the bearing fault severity without a training feature set. Noting that 1 NNC and LS-SVM are supervised learning algorithms, in future work, we will attempt to use some semi-supervised or unsupervised learning algorithms to recognize the bearing fault severity. Another limitation of the proposed approach is that the GLCM-based features are not correlated with physical significance. We cannot identify the bearing fault severity from the feature values. In the future, we intend to search for or construct more powerful features with physical meaning to more effectively capture the pattern of fault severity. The energy (or amplitude) information of signals expressed by the TFIs will be used in future studies to extract features correlated with physical significance. All authors discussed and agreed upon the idea and made scientific contributions. [PERSON] and [PERSON] conceived the original idea and designed the experiments; [PERSON] and [PERSON] conducted the experiments; [PERSON] wrote the paper. [PERSON] is the advisor of [PERSON], provided the funding, and revised the paper.