Proto-DISFNet: A Prototype-Guided Dual-Feature Transfer Learning Method for Cross-Condition Fault Diagnosis of Cotton Harvester Picking-Head Drivetrains

Cross-condition fault diagnosis of cotton harvester picking-head drivetrains remains challenging due to significant distribution discrepancies in vibration signals under different operating conditions. Existing transfer learning approaches predominantly focus on domain-invariant features while failing to sufficiently exploit domain-specific information and the structural constraints embedded in target-domain normal samples, which often leads to unstable diagnostic performance across conditions. To address this issue, this paper proposes a prototype-guided dual-feature transfer learning method termed Proto-DISFNet (Prototype-guided Domain-Invariant and Domain-Specific Feature Network). The proposed method explicitly disentangles domain-invariant and domain-specific features to alleviate the impact of operating condition variations. High-confidence pseudo-labeled samples, selected through a filtering strategy, are utilized to construct class prototypes in the target domain, thereby enhancing semantic consistency and structural awareness in the feature space. In addition, a stage-wise training strategy is introduced to coordinate multi-task optimization, which improves training stability and overall adaptability under representative complex engineering operating conditions. Experiments conducted on three vibration datasets, JNU, THU, and CHPH-FETB, demonstrate that Proto-DISFNet achieves stable and competitive cross-condition diagnostic performance under varying degrees of domain shift and operating conditions. These results indicate the engineering relevance and potential applicability of the proposed method for fault diagnosis of cotton harvester picking-head drivetrains.