# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 2 Related Work
### Raw Data-Based Anomaly Classification Research  
[PERSON] et al. highlighted the critical role of bearing fault diagnosis in wind farms while addressing the high computational costs associated with signal processing-based methods [6]. To overcome these challenges, they proposed a Boosted CNN algorithm that directly utilizes raw data for efficient classification of bearing faults without the need for extensive signal preprocessing. The dataset was collected using a PLC controller and categorized into healthy bearings, inner ring defects, and outer ring defects. For comparison, the study evaluated a spectrogram-based model leveraging the MobileNet-v2 network structure alongside the Boosted CNN and MLP models, both utilizing raw data as the input. Experiments conducted on a Raspberry Pi 4B minicomputer demonstrated that the Boosted CNN model achieved a 5.7% higher accuracy and a 42% reduction in execution time compared to the spectrogram-based model. These results underline the efficiency and practicality of the Boosted CNN model for real-time processing in low-power, low-memory environments, showcasing its potential applicability in industrial fault diagnosis systems. [PERSON] et al. proposed a hybrid DCNN-LSTM model that combines the strengths of a Deep Convolutional Neural Network (DCNN) and a Long Short-Term Memory (LSTM) network to diagnose bearing faults directly from raw vibration signals [7]. The study employed the IMS variable speed dataset and the CWRU bearing dataset, segmenting the signals into 1000-unit windows using a sliding-window technique. Experimental results indicated that the DCNN-LSTM model achieved a high accuracy exceeding 99% on both datasets, demonstrating its robustness and effectiveness for raw signal-based fault diagnosis. These findings highlight the potential of the DCNN-LSTM model for application in real-world industrial fault diagnosis systems. [PERSON] et al. proposed the Reparameterised Residual Denoising Diffusion Probability Model (ReF-DDPM), a novel data augmentation method to handle unbalanced rotating bearing fault data and improve diagnostic accuracy [8]. The ReF-DDPM improves the DDPM structure to enhance the quality and diversity of data generation and addressesthe problems of mode collapse and learning instability. The proposed model combines multi-scale residual network (RepRFN) and multi-head recurrent layer attention mechanism (MRLA) to enhance local and global features and effectively solve the problem of imbalanced data. The ReF-DDPM faithfully reproduces the characteristics of the original data in the time-frequency domain and outperforms the existing WGAN-GP and DDPM in evaluation metrics such as JSD, MMD, and COSINE. In particular, experiments on various datasets such as CWRU, MFPT, and MFS demonstrated high data generation quality and excellent diagnostic performance even in imbalanced data and achieved a high accuracy of 99.5% in balanced data. However, due to the slow sampling process of DDPM-based models, there are limitations in large-scale data processing and real-time application, and the complexity of the model structure increases, which may limit its application to real industrial datasets. In addition, the evaluation metrics limited to JSD, MMD, COSINE, etc., do not elaborately describe the impact of the generated data on the actual diagnostic performance, and the initial diagnostic performance is lower than that of the GAN-based model in situations with extreme data imbalance ratios. To overcome these limitations, future research should optimize sampling time, improve real-time processing capability, validate application in industrial environments, and develop new evaluation metrics. Table 1 summarizes the Related Work. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Methods** & **Results** & **Limitations** & **Reference** \\ \hline \multirow{4}{*}{CWRU} & \multirow{4}{*}{TDA, WPD, CWT, SIC, SVM, CNN} & \multirow{4}{*}{ \begin{tabular}{c} CWT most effective; SIC \\ (image-based) performs poorly. \\ \end{tabular}  } & \multirow{4}{*}{