Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise

In time-series studies involving bearing sensor data, Gaussian noise and white noise techniques are commonly employed to evaluate model robustness. However, these conventional noise techniques are limited in their applicability to real-world industrial environments. This paper proposes three novel noise techniques—electrical interference noise, harmonic noise, and random shock noise—that more accurately reflect the complex noise encountered in industrial settings. Additionally, a new deep learning model, MultiPatchTST, is introduced, demonstrating robust performance under various noise conditions. Experimental results reveal that Gaussian noise has minimal impact on model performance, whereas the proposed noise techniques significantly affect performance, providing a more realistic evaluation of noise robustness. The proposed MultiPatchTST model achieves superior performance across all metrics in the presence of all four noise types, confirming its robustness and reliability.