Gearbox Fault Diagnosis under Varying Operating Conditions through Semi-Supervised Masked Contrastive Learning and Domain Adaptation

To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis, this paper proposes a semi-supervised masked contrastive learning and domain adaptation (SSMCL-DA) method for gearbox fault diagnosis under variable conditions. Initially, during the unsupervised pre-training phase, a dual signal augmentation strategy is devised, which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples, thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations. Subsequently, a ConvNeXt-Transformer hybrid architecture is employed, integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios. Thereafter, a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample, enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data. In the final supervised fine-tuning phase, a multi-scale attention mechanism is incorporated for feature rectification, and a domain adaptation module combining Local Maximum Mean Discrepancy (LMMD) with adversarial learning is proposed. This module embodies a dual mechanism: LMMD facilitates fine-grained class-conditional alignment, compelling features of identical fault classes to converge across varying conditions, while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features. Working in concert, they markedly diminish feature distribution discrepancies induced by changes in load, rotational speed, and other factors, thereby boosting the model’s adaptability to cross-condition scenarios. Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University (CWRU) bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes, with diagnostic performance substantially surpassing that of conventional methods. Under cross-condition scenarios, the model attains fault diagnosis accuracies of 99.21% for the WT planetary gearbox and 99.86% for the bearings, respectively. Furthermore, the model exhibits stable generalization capability in cross-device settings.