Diesel engines are critical power sources widely used in marine,transportation,and industrial applications,where reliable operation is essential for safety and economic efficiency.However,traditional signal processing...Diesel engines are critical power sources widely used in marine,transportation,and industrial applications,where reliable operation is essential for safety and economic efficiency.However,traditional signal processing and many machine learning methods face challenges in extracting generalized fault features and accurately diagnosing misfires under complex,noisy operating conditions.To address these challenges,this paper proposes a novel multi-scale bottleneck attention and mixupbased domain adaptation network(MBA-MDAN)for reliable misfire detection across varying noise levels and working conditions.The approach integrates a denoising convolutional neural network(DnCNN)to suppress noise unrelated to fault characteristics,enabling clearer fault signal extraction.A parallel multi-scale convolution module captures fault features at different time scales,while a bottleneck attention module(BAM)selectively emphasizes critical features for deep fault representation.To preserve important information,time-domain statistical features are also incorporated.During training,metric learning minimizes feature discrepancies between source and target domains,and adversarial training between a domain discriminator and the fault classifier enhances domain adaptation.Additionally,domain mixup is applied to augment discriminator samples,further improving diagnostic performance.On the real-world datasets,compared to several state-of-the-art methods—including ShuffleNetV2,DenseNet,ANMCNN,MCBACNN,DANN,and WDAN—MBA-MDAN improves average diagnostic accuracy by 30.372%,16.407%,15.410%,16.483%,5.200%,and 4.725%,respectively.These results confirm the effectiveness of MBA-MDAN and indicate its strong potential for integration into intelligent operation and maintenance(O&M)systems for diesel engines,facilitating early fault detection and maintenance decision-making in complex industrial environments.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52375255)。
文摘Diesel engines are critical power sources widely used in marine,transportation,and industrial applications,where reliable operation is essential for safety and economic efficiency.However,traditional signal processing and many machine learning methods face challenges in extracting generalized fault features and accurately diagnosing misfires under complex,noisy operating conditions.To address these challenges,this paper proposes a novel multi-scale bottleneck attention and mixupbased domain adaptation network(MBA-MDAN)for reliable misfire detection across varying noise levels and working conditions.The approach integrates a denoising convolutional neural network(DnCNN)to suppress noise unrelated to fault characteristics,enabling clearer fault signal extraction.A parallel multi-scale convolution module captures fault features at different time scales,while a bottleneck attention module(BAM)selectively emphasizes critical features for deep fault representation.To preserve important information,time-domain statistical features are also incorporated.During training,metric learning minimizes feature discrepancies between source and target domains,and adversarial training between a domain discriminator and the fault classifier enhances domain adaptation.Additionally,domain mixup is applied to augment discriminator samples,further improving diagnostic performance.On the real-world datasets,compared to several state-of-the-art methods—including ShuffleNetV2,DenseNet,ANMCNN,MCBACNN,DANN,and WDAN—MBA-MDAN improves average diagnostic accuracy by 30.372%,16.407%,15.410%,16.483%,5.200%,and 4.725%,respectively.These results confirm the effectiveness of MBA-MDAN and indicate its strong potential for integration into intelligent operation and maintenance(O&M)systems for diesel engines,facilitating early fault detection and maintenance decision-making in complex industrial environments.
