###### Abstract  
This study analyzes the impact of different types of random noise applied in Denoising Autoencoder (DAE) training on fault diagnosis performance, with the aim of improving noise removal for vibration time series data. While conventional studies typically train DAEs using Gaussian random noise, such noise does not fully reflect the complex noise patterns observed in real-world industrial environments. Therefore, this study proposes a novel approach that uses high-frequency noise components extracted from actual vibration data as training noise for the DAE. Both Gaussian and high-frequency noise were used to train separate DAE models, and statistical features (mean, RMS, standard deviation, kurtosis, skewness) were extracted from the denoised signals. The fault diagnosis rates were calculated using One-Class Support Vector Machines (OC-SVM) for performance comparison. As a result, the model trained with high-frequency noise achieved a 0.0293 higher average F1-score than the Gaussian-based model. Notably, the fault detection accuracy using the kurtosis feature improved significantly from 26.22% to 99.5%. Furthermore, the proposed method outperformed the conventional denoising technique based on the Wavelet Transform, demonstrating superior noise reduction capability. These findings demonstrate that incorporating real high-frequency components from vibration data into the DAE training process is effective in enhancing both noise removal and fault diagnosis performance.  
D 202  
the operational state of equipment, its precise measurement and interpretation are crucial. However, in real industrial environments, vibration data inevitably includes various forms of noise due to environmental interference and sensor limitations. This noise degrades data quality and impedes accurate analysis and prediction, highlighting the need for effective noise reduction techniques.  
Traditionally, signal processing methods such as filtering have been employed to reduce noise. However, these techniques have limitations when the frequency components of noise and signals overlap, and they often struggle to manage complex noise patterns. Recently, advances in deep learning have attracted attention as they offer new approaches capable of learning intricate data patterns and effectively removing noise. In particular, deep learning-based denoising methods such as Denoising Autoencoders (DAEs) have demonstrated excellent performance in restoring original signals by removing noise from the input data. Therefore, applying deep learning techniques to denoise vibration time series data is expected to play a vital role in enhancing the accuracy of predictive maintenance systems.  
Nevertheless, the performance of DAE-based noise reduction methods varies depending on how the model is trained. DAEs typically learn to reconstruct original signals by intentionally adding random noise to input data and then learning to remove it. This noise is often configured as Gaussian random noise, designed to resemble the statistical properties of vibration signals. However, even with such Gaussian noise, it is difficult to fully reflect the actual characteristics of real-world vibration data. To address this limitation, this study proposes a method in which only the high-frequency noise components extracted from real vibration time series data are used as the random noise input during DAE training. Accordingly, this study proposes a novel approach in which only the high-frequency noise components extracted from real-world vibration time-series data are utilized as random noise during the training process of the Denoising Autoencoder (DAE). The effectiveness of this method is validated by comparing its fault diagnosis accuracy with that of data denoised using Gaussian random noise and the conventional Wavelet Transform technique.