Fault detection and diagnosis of marine diesel engines: A systematic review

柴油 断层(地质) 故障检测与隔离 海洋工程 法律工程学 工程类 计算机科学 环境科学 汽车工程 地质学 人工智能 地震学 执行机构
作者
Yaqiong Lv,Xueting Yang,Yifan Li,Jialun Liu,Shijie Li
出处
期刊:Ocean Engineering [Elsevier BV]
卷期号:294: 116798-116798 被引量:27
标识
DOI:10.1016/j.oceaneng.2024.116798
摘要

Marine diesel engines play a pivotal role in ensuring the smooth operation of maritime vessels. However, given the rigorous operational conditions and the natural wear and tear of engine components, the likelihood of diesel engine failures during voyages is an inherent concern. Preserving the optimal functionality and performance of marine diesel engines is of paramount importance, as it averts the potential repercussions of in-service failures and accidents. Hence, timely and effective fault detection is imperative to minimize operational disruptions and uphold safety standards. Intelligent fault detection and diagnosis (FDD) holds substantial promise in both academic and industrial contexts. While the monitoring of marine diesel engines using various FDD techniques has been a subject of study for decades, various new techniques emerges and there is a notable absence of comprehensive analysis and summarization of the methodologies developed. This paper commences by introducing the operational principles of marine diesel engines and elucidating potential faults. Subsequently, it provides an overview of recent advancements in FDD methods tailored for marine diesel engines, categorizing them into four distinct sections. In alignment with the conventional FDD workflow, the paper delves into discussions concerning model-based, data-driven, knowledge-based, and hybrid approaches. Moreover, it offers insights into the potential future directions of this field.
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