A Fault Detection and Health Monitoring Scheme for Ship Propulsion Systems Using SVM Technique
A Fault Detection and Health Monitoring Scheme for Ship Propulsion Systems Using SVM Technique
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采用SVM技术的船舶推进系统故障检测与健康监测方案
DOI：
10.1109/access.2018.2812207
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发表时间：
2018-03
期刊：
影响因子：
3.9
通讯作者：
Wei Muheng
中科院分区：
文献类型：
--
作者：
Zhou Jing;Yang Ying;Ding Steven X.;Zi Yanyang;Wei Muheng
基于模型和数据驱动的故障检测技术各有优缺点。在实践中，故障检测系统通常与健康监测系统分开布置。在本文中，建立基于残差发生器的故障检测支持向量机（SVM）的分类功能，制定了多个评价函数。将基于模型和数据驱动的方法联合收割机相结合，可以作为提高故障检测性能的一种尝试。对标准支持向量机进行改进，使其在虚警率和故障检测率之间达到定量的折衷。此外，本文还提供了一个统一的框架，故障检测和健康监测的支持向量机。对船舶推进系统的仿真表明了该方法的有效性。
Both the model-based and data-driven techniques for fault detection have their merits and drawbacks. The fault detection systems are usually laid out separately with the health monitoring systems in practice. In this paper, the well-established observer-based residual generator is formulated to construct multiple evaluation functions which are employed as the classification features of the support vector machine (SVM) for fault detection. It can be regarded as a tentative approach to combine the model-based and data-driven methods to enhance the fault detection performance. The standard SVM is modified for fault detection to achieve the quantitative tradeoff between false alarm rate and fault detection rate. In Addition, this paper also provides a unified framework for fault detection and health monitoring based on the SVM. Simulations on the ship propulsion system show the effectiveness of the proposed method.
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