Bearing Fault Diagnosis for Cross-Condition Scenarios Under Data Scarcity Based on Transformer Transfer Learning Network

Motor-bearing fault diagnosis is critical for industrial equipment reliability, yet traditional data-driven methods require extensive labeled data, which are often scarce in real-world applications. To address this challenge, we propose a Transformer transfer learning network (TTLN) for accurate fault diagnosis under cross-condition scenarios, particularly when target domain data are limited. First, we develop a Transformer-based fault diagnosis model that captures long-range dependencies in sequential data through self-attention, achieving high accuracy under single operating conditions. Second, we introduce the TTLN framework, which integrates domain adaptation to align marginal and conditional distributions, enabling robust cross-condition fault diagnosis with minimal target domain samples. Finally, we validated the proposed method on the CWRU and PU datasets, demonstrating the TTLN’s superior performance in data-scarce scenarios. For example, the TTLN achieved over 95% accuracy with only 100 target samples, outperforming traditional methods and fine-tuning-based approaches. The results underscore the TTLN’s effectiveness in cross-condition fault diagnosis, offering a practical solution for industrial applications with limited labeled data.