Gearbox fault diagnosis method based on adaptive multi-sensor data level fusion and fine-grained domain adaptation

Gearboxes are critical in industrial machinery, and leveraging multi-sensor data is crucial for effective fault diagnosis. However, robust diagnosis is particularly challenging under varying operational conditions, especially with unlabeled data. This paper introduces an adaptive multi-sensor data level fusion and fine-grained domain adaptation approach (AMDFFDA) to improve gearbox fault diagnosis. The method enhances performance across different operational conditions by combining multi-sensor data fusion at the data level with domain adversarial techniques. Firstly, each single sensor data undergoes preprocessing using the eccentricity technique, followed by the determination of their optimal weighting factors for the effective convergence of data from multiple sensors, with the aim of minimizing the mean square error. Subsequently, an adversarial domain adaptation module is employed to extract features that remain invariant across domains through continuous adversarial training. Ultimately, a teacher model generates reliable pseudo-labels for the target domain, thereby reducing class-conditional shifts between the source and target domains. The findings on the MCC5 dataset demonstrate that AMDFFDA surpasses its competitors in the realm of unsupervised cross-domain fault diagnosis, achieving an average accuracy of 75%. Furthermore, ablation studies validated the contribution of each module within our method, and parameter sensitivity analyses highlighted their impact on the model’s effectiveness.