摘要
船舶柴油机作为大多数船舶的动力源泉,具有十分重要的地位,其健康状态直接影响了船舶的稳定运行.由于船舶柴油机具有工作环境复杂且工况多变的特点,不利于传统故障预测方法的应用.本文提出了一种增强型间歇性未知输入卡尔曼滤波器,可以有效降低建模的复杂度,应对具有不同的工作状态的参数预测.最后本文提出并使用改进的序贯概率比检验进行残差处理,减小故障误报.仿真结果表明,该方法可以较好地对船舶柴油机系统故障进行预测.
The marine diesel engine serves as the power source of most vessels, which has a very important position.Its health status directly affect the ship’s stable operation. The traditional fault prognosis methods are difficult to apply to the marine diesel engine due to its different operating environments and work patterns. In this paper, we propose an enhanced intermittent unknown input Kalman filter which can effectively reduce the complexity of modeling and deal with the fault prognosis with different working modes. Also this paper uses the improved sequential probability ratio test for residual processing to reduce the probability of false alarm. According to the simulation results, the proposed method demonstrated superiority and feasibility in fault prognosis for the marine diesel engine.
作者
韩敏
李锦冰
许美玲
韩冰
HAN Min;LI Jin-Bing;XU Mei-Ling;HAN Bing(Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023;The State Key Laboratory of Navigation and Safety Technology,Shanghai Ship and Shipping Research Institute, Shanghai200135)
出处
《自动化学报》
EI
CSCD
北大核心
2019年第5期920-926,共7页
Acta Automatica Sinica
基金
国家自然科学基金(61773087)
中央高校基本科研业务费专项(DUT16RC(3)123)
上海启明星计划(15QB1400800)资助~~
关键词
船舶柴油机
卡尔曼滤波器
序贯概率比检验
故障预测
Marine diesel engine
Kalman filters
sequential probability ratio test
fault prognosis
