摘要
提出了一种模型-数据联合驱动的船舶舵机电液伺服系统早期故障检测方法.首先,建立了系统状态方程,对系统中的常见故障进行了模型解析,并对系统中的各类不确定因素进行了分类分析.其次,为减少各类不确定因素的影响,采用混合式处理方法进行逐层削减.利用系统正常运行状态下的输入输出数据先对系统中的不确定参数进行有效辨识,通过设计鲁棒故障检测观测器来对系统中的固有非线性和未知时变外负载力进行处理和解耦.为了能够对早期故障进行有效检测,利用实际及观测系统数据,构建了基于神经网络的补偿模型,可进一步削减剩余不确定因素对故障检测的影响,从而提高故障敏感性.最后,通过仿真和实验共同验证了这种模型-数据联合驱动故障检测方法的有效性,该方法可用于船舶舵机电液伺服系统及类似系统的在线早期故障检测.
This paper offers an integrated model-based and data-driven early fault detection scheme for a ship rudder electro-hydraulic servo system(RESS). First, the state equation of RESS is established, the common faults in the system are analyzed, and the uncertain factors in the system are classified. To reduce the influences of various uncertainties, a hybrid processing method with several steps is proposed next. Using the system input and output data in normal state, the uncertain model parameters can be identified, and then a robust fault detection observer is designed to eliminate the influences of system inherent nonlinearity and unknown external force. To deal with the remaining uncertainties and disturbances, a neural network based compensation model using actual and observed system data is constructed, which can further reduce the influences of uncertainties and to detect the early faults effectively. Both simulation and experimental results show that the combined method is efficient and can be used for on-line fault detection.
作者
徐巧宁
艾青林
杜学文
刘毅
XU Qiaoning;AI Qinglin;DU Xuewen;LIU Yi(Key Laboratory of Special Purpose Equipment and Advanced Manufacturing Technology,Ministry of Education,Zhejiang University of Technology,Hangzhou 310023,China;College of Mechanical and Energy Engineering,Zhejiang University Ningbo Institute of Technology,Ningbo 315100,Zhejiang,China)
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2020年第5期451-464,共14页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金资助项目(51705456,51605431)
浙江省教育厅一般科研项目(自然科学类)(Y201737901)。
