# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 6 Conclusions  
This paper addresses the limitations of Gaussian noise and white noise, which have been predominantly used in bearing sensor-based fault classification research, highlighting their inadequacy for representing real-world industrial noise. To overcome these limitations, three novel noise techniques--electrical interference noise, harmonic noise, and random shock noise--were proposed, reflecting noise characteristics observed in industrial environments. Additionally, the study compared the performance of RNN-based and CNN-based deep learning models, and introduced a new deep learning model, MultiPatchTST, for bearing sensor data anomaly classification. The experimental results demonstrate that all three proposed noise types significantly impact model performance. Among these, random shock noise causes the greatest performance degradation when applied independently. However, when mixed with original data, models perform best under random shock noise, debunking the notion that periodic noise inherently leads to overfitting due to the model learning its periodicity. The proposed MultiPatchTST model outperforms all other models across various noise environments, confirming its robustness to noise. The models used in this study can be categorized into three groups: sequential models (LSTM, GRU, LSTMAttention, GRU-Attention), CNN-based models (e.g., FCN, ResNet), and patch-based models (PatchTST, MultiPatchTST). The MultiPatchTST model consistently delivered superior performance, while LSTMAttention and GRUAttention performed the worst in some experiments, indicating that attention mechanisms alone are insufficient for optimal performance. Instead, patch-based learning emerged as a key factor for effective bearing fault classification in vibration sensor data. The main strength of the MultiPatchTST model is its ability to capture the temporal and frequency patterns of data in detail through a learning strategy that utilizes multiple patch sizes, which is a fundamental difference from traditional models that rely on a single patch size, enabling it to reliably learn the key characteristics of data, especially in complex and atypical industrial noise environments. By using a combination of different patch sizes to learn the multi-layered nature of the data, it effectively overcomes the noise sensitivity issues of traditional RNN and CNN-based models. In addition, the MultiPatchTST model demonstrated high information extraction capability even in noisy data through the combination of a self-attention mechanism and patch-based learning, which contributed to its excellent performance even in complex noise environments such as electrical interference noise, harmonic noise, and random impact noise. The experimental results showed that the MultiPatchTST model outperformed all other comparison models, with the largest performance advantage in the random impulse noise environment. The model's multi-patch ensemble learning approach has a practical advantage in that it can automatically learn the optimal patch size based on the characteristics of the data without requiring the user to manually set the patch size. The findings of this study provide valuable insights for developing robust fault classification models and are expected to contribute significantly to industrial field systems, improving their efficiency and reliability. In future research, more diverse data sets will be used to verify and improve the performance of the model in different environments. In addition, the noise technique proposed in this paper will be tested using datasets of other mechanical elements as noise that can affect not only bearings but also other mechanical equipment elements, so that it can be developed into a general-purpose model that shows good performance in various mechanical equipment rather than a bearing-specific model. Conceptualization, S.L. ; data curation, S.L. ; formal analysis, S.K. ; funding acquisition, S.L. ; investigation, S.L. ; methodology, S.L. ; project administration, S.L. ; resources, S.K. and S.L.