A Fault Diagnosis Method for Marine Engine Cross Working Conditions Based on Transfer Learning

Abstract

Marine engines confront challenges of varying working conditions and intricate failures. Existing studies have primarily concentrated on fault diagnosis in a single condition, overlooking the adaptability of these methods in diverse working condition. To address the aforementioned issues, we propose a cross working condition fault diagnosis method named the Balanced Adaptation Domain Weighted Adversarial Network (BADWAN). This method combines transfer learning to tackle the challenges of cross working condition diagnosis with limited labels. Specifically tailored for scenarios with incomplete labeling in the target working conditions, we designed an Enhanced Centroid Balance scheme to balance the label space, thereby enhancing the model’s transfer capabilities. Additionally, we designed an Instance Affinity Weighting scheme on the foundation of Class-level Weighting, refining the model to the instance level for effective information interaction. Furthermore, we incorporated the Adaptive Uncertainty Suppression strategy to further boost the model’s classification prowess. Two experimental scenarios were designed to demonstrate the effectiveness of the proposed model using a Wärtsilä9L34DF dual-fuel engine as an experimental subject. The results demonstrate an over 90% diagnostic accuracy in scenarios with complete target working condition labels and 86% accuracy in scenarios with incomplete labels, outperforming other transfer learning models. The BADWAN model excels in cross-condition fault diagnosis tasks for marine engines with incomplete target working condition labels, offering a novel solution to this field.