Fault diagnosis of gearbox driven by vibration response mechanism and enhanced unsupervised domain adaptation

Although data-driven model has achieved remarkable results in gearbox fault diagnosis, its diagnostic accuracy is still highly dependent on large amounts of high-quality labeled samples. Some data generation methods, such as generative adversarial network, are utilized to address this problem. However, the generated simulation samples not only lack fault mechanism features with clear physical meaning, but also have distribution differences with the real samples. Aiming at the above problems, an enhanced unsupervised domain adaption method combined with vibration response mechanism is proposed for gearbox fault diagnosis. Firstly, various fault types of labeled simulation signals with clear physical meaning are generated based on vibration response mechanism of gearbox, alleviating the lack of large amounts of high-quality labeled samples for data-driven models. Secondly, to narrow the inevitable domain discrepancy between simulation samples and experimental samples, a domain mapping method is raised to both transform their distributions to normal distribution by optimizing an alignment function, which also could effectively improve the diagnostic speed and accuracy of intelligent models. Finally, the mapped samples are directly fed into an arbitrary unsupervised domain adaptation model to achieve fault diagnosis in the absence of any label information of measured samples. Importantly, the proposed domain mapping method can be simply appended to any existing core network to enhance diagnostic accuracy without necessitating modifications to its structure or training procedure. Experiments on two gearbox datasets suggest that the proposed method can effectively boost the performance of diagnosis issues with only a small number of experimental samples and outperform existing diagnosis approaches.