Fault frequency band segmentation and domain adaptation with fault simulated signal for fault diagnosis of rolling element bearings

Rolling element bearings are vital components in rotating machinery, and ensuring their reliable operation through robust fault diagnosis is crucial in industrial settings. Deep-learning-based methods have shown promise due to their high accuracy, but they often face challenges in data acquisition and domain shifts between training and inference datasets. Existing approaches have attempted to address these issues through signal generation using simulation models, deep learning techniques, and domain adaptation under partial label scenarios. However, generated signals often lack plausibility or physical fidelity, and partial domain adaptation approaches frequently fail to incorporate fault-related knowledge. This paper proposes a novel method combining fault frequency band segmentation domain adaptation (FBSDA) with fault-added and uncertainty-aware signal simulation. To address the scarcity of fault-labeled signals, the proposed simulation method accounts for uncertainties in the signal acquisition environment by leveraging statistical cyclo-stationary modeling of fault bearings. By adding simulated fault signals to normal signals that contain system characteristic information, the generated signals more accurately reflect real-site environments and physical principles. Additionally, the FBSDA method, a domain adaptation approach focusing on segmenting fault-related information within the fault frequency band, is introduced. To enhance the focus on the fault frequency band, FBSDA employs a fault frequency segmentation module and a loss function inspired by image segmentation techniques. This method effectively reduces the domain gap between source and target domains and simultaneously captures fault information common to both simulated and real signals. The proposed method is validated through two case studies using different testbed datasets under various operating conditions. The results demonstrate the superior performance of our approach in handling domain shifts and different levels of partial labels, outperforming existing signal generation and domain adaptation methods. The proposed method also has a practical value in that the target bearing system can be diagnosed using physical knowledge even in the absence of fault signals that are difficult to obtain.