Evaluating the Performance of Generative Adversarial Network, Synthetic Minority Oversampling Technique, and Adaptive Synthetic Sampling in Marine Diesel Engine Fault Diagnosis using Vibration Data

Machine learning-driven fault diagnosis of marine diesel engines is a major research focus, challenged by scarce, insufficient fault data. This study explores vibration data under five conditions: normal operation, single-cylinder ignition failure, piston ring wear, nozzle blockage, and exhaust valv...

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التفاصيل البيبلوغرافية
المؤلفون الرئيسيون:	Zhijun Chen, Ziyu Xiao, Zhongjun Wang, Pengcheng Wang, Jie Wang, Yijie Qu, Xiong Bao, Xiaofeng Guo
التنسيق:	Artigo
اللغة:	Inglês
منشور في:	Engineered Science Publisher LLC 2025-08-01
سلاسل:	Engineered Science
الموضوعات:	fault diagnosis marine diesel engine data augmentation gan smote adasyn
الوصول للمادة أونلاين:	https://www.espublisher.com/journals/articlehtml/engineered-science/10.30919-es1696
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

Evaluating the Performance of Generative Adversarial Network, Synthetic Minority Oversampling Technique, and Adaptive Synthetic Sampling in Marine Diesel Engine Fault Diagnosis using Vibration Data

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