# Noise Reduction in CWRU Data Using DAE and Classification with ViT
## 1 Introduction  
### Background  
Predictive maintenance involves collecting and analyzing asset, equipment, or machinery data. With advancements in predictive maintenance technologies, numerous studies have focused on diagnosing and classifying mechanical device breakdowns. In particular, abnormalities or breakdowns in rotating machinery can lead to equipment downtime, and severe cases may result in safety incidents that threaten human lives. Therefore, monitoring the condition of machinery is essential to ensure proper operation [1]. However, measuring and analyzing operating machinery data in real time and classifying breakdowns are challenging. Noise often obscures the properties of failures when machinery breaks down, severely affecting the accuracy of status monitoring [1]. The effective reduction of noise interference and diagnostic accuracy improvements require noise removal processes. Removing noise from vibration signals is essential for AI models. As shown in Figure 1, according to CrowdFlower's data science report, many researchers spend most of their time on data processing and preparation to effectively use AI models [2]. Data noise removal is essential, and many studies are focusing on noise reduction methods to improve AI model classification accuracy. [PERSON] et al. (2021) proposed a noise reduction technique that involved dividing the data into 16 segments and subsequently removing the first and last segments, effectively reducing the noise component [3]. Similarly, [PERSON] et al. (2023) adopted a novel approach to noise reduction by transforming one-dimensional vibration signals into two-dimensional grayscale images [4]. [PERSON] et al. (2020) utilized cepstrum analysis to effectively remove noise from bearing data [5]. Furthermore, [PERSON] et al. (2021) introduced a hybrid method, combining Sparse Component Independent Component Analysis (SCICA) and Frequency Domain (FD) techniques to mitigate noise in the time-frequency analysis of vibration signals [6]. [PERSON] et al. (2025) developed a methodology for diagnosing cross-domain complex faults in bearings by leveraging domain enhancement features through the DA-DREA approach [7]. In a related study, [PERSON] et al. (2024) introduced an unsupervised recognition framework based on the RAOS algorithm, demonstrating its capability to accurately identify complex defects. These methodologies contribute significantly to advancements in the diagnosis of intricate fault patterns in bearing systems [8]. Wavelet transform effectively utilizes signal information across varying scales and has been widely developed for noise removal in signals [9]. [PERSON] et al. (2021) sought to flexibly adjust thresholds of wavelet coefficients, addressing limitations in the wavelet threshold function, and proposed an improved wavelet threshold function for this purpose [10]. [PERSON] et al. (2019) and [PERSON] et al. (2017) suggested a universal Bayesian wavelet noise removal approach and a wavelet packet noise removal method, respectively [11; 12]. However, selecting the optimal wavelet function remains a challenge. Considering the limitations of the wavelet transform, [PERSON] et al. (1998) recommended empirical mode decomposition (EMD) for processing abnormal signals [13]. Compared to wavelet transform, EMD is an adaptive signal decomposition method. The intrinsic mode function (IMF) components directly depend on underlying signals, eliminating the need for a preset basis function. However, EMD is susceptible to mode mixing and edge effects [14]. Improved versions of EMD, such as ensemble empirical mode decomposition (EEMD) and complete EEMD with adaptive noise (CEEMDAN), which includes adaptive noise, effectively overcome the mode mixing issue while maintaining EMD's advantages in processing abnormal signals [15]. Although CEEMDAN significantly improves EEMD, inevitable noise remains in the initial IMFs.