## 5 Conclusions and Future Work  
This study proposed a method to improve the performance of Denoising Autoencoders (DAEs), which are widely used for noise removal in image and time series data. Conventional DAE-based denoising approaches typically involve training the model with artificially added Gaussian random noise. However, Gaussian noise does not adequately \begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**High-Pass Noise**} & **Precision** & **Recall** & **F1-Score** & **F1-Score Average** \\ \hline \multirow{4}{*}{Feature} & Mean & 0.94 & 1 & 0.97 \\ \cline{2-5}  & RMS & 0.92 & 1 & 0.958 \\ \cline{2-5}  & Standard Deviation & 0.901 & 1 & 0.948 \\ \cline{2-5}  & Skewness & 0.94 & 0.749 & 0.834 \\ \cline{2-5}  & Kurtosis & 0.95 & 0.995 & 0.972 \\ \hline \end{tabular}
\end{table} 
Table 6: Result of classification performance (High-pass). \begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Gaussian Noise**} & **Precision** & **Recall** & **F1-Score** & **F1-Score Average** \\ \hline \multirow{4}{*}{Feature} & Mean & 0.936 & 1 & 0.967 \\ \cline{2-5}  & RMS & 0.935 & 1 & 0.966 \\ \cline{2-5}  & Standard Deviation & 0.936 & 0.986 & 0.96 \\ \cline{2-5}  & Skewness & 0.938 & 0.957 & 0.947 \\ \cline{2-5}  & Kurtosis & 0.93 & 0.558 & 0.697 \\ \hline \end{tabular}
\end{table} 
Table 4: Result of classification performance (Gaussian). \begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Wavelet Transform**} & **Precision** & **Recall** & **F1-Score** & **F1-Score Average** \\ \hline \multirow{4}{*}{Feature} & Mean & 0.986 & 0.496 & 0.66 \\ \cline{2-5}  & RMS & 0.904 & 0.501 & 0.644 \\ \cline{2-5}  & Standard Deviation & 0.587 & 0.3657 & 0.62 \\ \cline{2-5}  & Skewness & 0.896 & 0.513 & 0.652 \\ \cline{2-5}  & Kurtosis & 0.89 & 0.515 & 0.652 \\ \hline \end{tabular}
\end{table} 
Table 5: Result of classification performance (Wavelet). reflect the complex and diverse noise patterns present in real-world vibration time series data. To address this limitation, this study introduced a method that extracts high-frequency components from actual vibration data and uses them as training noise during the DAE learning process. To validate the proposed approach, the same feature extraction and fault classification procedures were applied as in the prior experiments using Gaussian noise and Wavelet Transform-based denoising. The final comparison results showed that utilizing high-frequency noise components led to improvements in F1-score by 0.2911 and 0.0293 over the Wavelet- and Gaussian-based methods, respectively. These results demonstrate that, in the context of denoising vibration time series data, it is more effective to train DAE models using noise components derived from the actual data--such as high-frequency noise--rather than relying on generic Gaussian random noise. This study has successfully demonstrated the practical effectiveness of training DAEs with noise extracted from real vibration data. The findings are expected to make meaningful contributions to the fields of predictive maintenance and condition monitoring for mechanical systems. In future work, we plan to further validate the generalizability of the proposed method using additional datasets collected from various industrial domains. Conceptualization, J.-g.J. and J.-c.L. ; methodology, J.-g.J. and J.-c.L. ; software, J.-g.J. and J.-c.L. ; validation, J.-g.J. ; formal analysis, J.-g.J. ; investigation, J.-g.J. and J.-c.L. ; resources, J.-g.J. and J.-c.L. ; data curation, J.-g.J. and J.-c.L. ; writing--original draft preparation, J.-g.J. ; writing--review and editing, J.-g.J., S.-s.L., S.-Y.H. and J.-c.L. ; visualization, J.-g.J. ; supervision, and S.-s.L. and S.-Y.H. ; project administration, J.-c.L. All authors have read and agreed to the published version of the manuscript. This work was supported by 2025 Ministry of Oceans and Fisheries (MOF) Marine Blue Tech Future Leader Training Project 'Training Blue Tech Leaders for Eco-Friendly Ships' (No. RS-2025-02220459). Not applicable.