Fault Diagnosis Method for Marine Engine under Variable Working Conditions Based on Adversarial Subdomain Adaptation

Marine engines have a high risk of failure during navigation and face issues such as varying operating conditions and complex faults. Existing research mainly focuses on fault diagnosis of marine engines under single operating conditions, neglecting the applicability of these methods under other conditions. To address this issue, this paper employs domain adaptation techniques to study cross-condition fault diagnosis of marine engines. Firstly, a self-calibrating convolutional neural network is used to extract health state features from engine operation data. Meanwhile, by integrating adversarial and subdomain adaptation techniques, the distribution differences in global domains and local subdomains under different operating conditions are reduced, thereby improving the fault diagnosis accuracy under variable conditions. Finally, the effectiveness of the diagnostic model is validated based on simulated marine engine fault data. For the designed one-to-one and one-to-many condition transfer tasks, the average fault diagnosis accuracy reaches approximately 90%, achieving higher recognition accuracy compared to other methods. The research results can provide theoretical reference for fault diagnosis of marine engines under variable operating conditions.