## 5 Discussion and Conclusions  
In the context of the growing concepts of Industry 4.0 and smart manufacturing, intelligent methods for detecting and classifying machine faults are the subject of increasing scientific research and interest. Different signal processing and ML-based approaches have been used in detecting and classifying bearing faults. Signal processing techniques in the time domain, frequency domain, and time-frequency domain have been used to analyze vibration signals. However, due to the various limitations of the typical signal processing and ML-based approaches, DL-based methods are preferred over them. An intelligent method for bearing fault detection and classification in one of the most used benchmark datasets, the CWRU dataset, is presented in this paper. The 1D CNN-based deep learning approach is implemented for the time-sequence bearing data. The raw vibration data from four datasets are divided into \(N\) samples, with each sample containing 1600 data points, and then fed into a 1-D CNN for feature extraction and classification. Moreover, a comparison of the proposed method with the 2-D CNN using 2-D image representation of the raw bearing signal as input is carried out. The result shows that the 1-D CNN performs efficiently for time-series data. In addition, sensitivity analysis of the proposed model is performed, in which metrics such as precision, recall, and f1-score are determined. We also compare the proposed method with some of the published works as well. We use Keras [50] for training the model, using TensorFlow [51] at the backend. The model is trained through the GeForce RTX 2080 Ti GPU of NVIDIA [52]. The results analysis also shows that the time for training the 1-D CNN model is lower than that for the 2-D CNN. The Results section thus shows that implementing a 1-D CNN is more efficient in terms of computational complexity for time-series data. With only 276,522 parameters, the proposed method achieves state-of-the-art accuracy. Simplicity and computational feasibility are the main advantages of this model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Article Reference** & **Model** & **Accuracy** \\ \hline
[46] & 2-D CNN using vibration image & 97.74\% \\ \hline
[42] & 1-D CNN & 97.1\% \\ \hline
[44] & 2-DCNN-based approach with & 99.41\% using 2 sensors \\  & multiple sensor fusion & 98.35\% with 1 sensor \\ \hline
[47] & CNN-based deep distance metric & 99.34\% for sample length of 8192 \\  & learning method & 99.34\% to 99.49\% for 4 different \\ \hline Our Model & 1-D CNN &
\begin{tabular}{c} 99.34\% to 99.49\% for 4 different \\ datasets \\ \end{tabular} \\ \end{tabular}
\end{table} 
Table 6: Comparison of the proposed model with some published works. Figure 8: Network architecture of 2-D CNN model used for comparison. **Author Contributions:** Conceptualization, D.N., J.S., Y.K. and J.H. ; methodology, D.N. ; software, D.N. ; validation, D.N., Y.K., J.S. and J.H. ; formal analysis, D.N., J.S., Y.K. and J.H. ; data curation, D.N. and Y.K. ; writing--original draft preparation, D.N. ; writing--review and editing, D.N., Y.K., J.S. and J.H. ; supervision, J.S. and J.H. ; funding acquisition, J.S. and J.H. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (MOE) (2021 RIS-003). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data was collected from a publicly available CWRU bearing database. The link is as follows: [[https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)]([https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)) (accessed on 19 October 2021).