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

doi:10.16183/j.cnki.jsjtu.2020.05.002

摘要/Abstract

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.

关键词: 舵机；电液伺服；基于模型；数据驱动；早期故障检测

Abstract: 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.

Key words: rudder; electro-hydraulic; model-based; data-driven; early fault detection

中图分类号:

TH 137

徐巧宁，艾青林，杜学文，刘毅. 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[J]. 上海交通大学学报, 2020, 54(5): 451-464.

XU Qiaoning,AI Qinglin,DU Xuewen,LIU Yi. An Integrated Model-Based and Data-Driven Method for Early Fault Detection of a Ship Rudder Electro-Hydraulic Servo System[J]. Journal of Shanghai Jiaotong University, 2020, 54(5): 451-464.

参考文献

［1］STAFFORD B, OSBORNE N. Technology development for steering and stabilizers［J］. Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment, 2008, 222(2): 41-52. ［2］费千. 船舶辅机［M］. 大连: 大连海事大学出版社, 2010. FEI Qian. Marine auxiliary machinery［M］. Dalian: Dalian Maritime University Press, 2010. ［3］ZHOU J, Yang Y, Ding S, et al. A fault detection scheme for ship propulsion systems using randomized algorithm techniques［J］. Control Engineering Practice, 2018, 81(12): 65-72. ［4］王少萍. 液压系统故障诊断与健康管理技术［M］. 北京: 机械工业出版社, 2014. WANG Shaoping. Fault diagnosis and health ma-nagement technologies for hydraulic system［M］. Beijing: China Machine Press, 2014. ［5］GAO Z, CECATI C, DING S. A survey of fault diagnosis and fault-tolerant techniques-Part I: Fault diagnosis with model-based and signal-based approaches［J］. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3757-3767. ［6］鄢镕易, 何潇, 周东华. 线性离散系统间歇故障的鲁棒检测方法［J］. 上海交通大学学报, 2015, 49(6): 812-818. YAN Rongyi, HE Xiao, ZHOU Donghua. Robust detection of intermittent faults of linear discrete-time stochastic systems［J］. Journal of Shanghai Jiao Tong University, 2015, 49(6): 812-818. ［7］GAO Z, LIU X, CHEN M. Unknown input obser-ver-based robust fault estimation for systems corrupted by partially decoupled disturbances［J］. IEEE Tran-sactions on Industrial Electronics, 2016, 63(4): 2537-2547. ［8］SEPASI M, SASSANI F. On-line fault diagnosis of hydraulic systems using Unscented Kalman Filter［J］. International Journal of Control Automation and Systems, 2010, 8(1): 149-156. ［9］WU X, LI Y, LI F, et al. Adaptive estimation-based leakage detection for a wind turbine hydraulic pitching system［J］. IEEE-ASME Transactions on Mechatro-nics, 2012, 17(5): 907-914. ［10］KHAN H, ABOU S C, SEPEHRI N. Nonlinear observer-based fault detection technique for electro-hydraulic servo-positioning systems［J］. Mechatro-nics, 2005, 15(9): 1037-1059. ［11］SHI Z, GU F, LENNOX B, et al. The development of an adaptive threshold for model-based fault detection of a nonlinear electro-hydraulic system［J］. Control Engineering Practice, 2005, 13(11): 1357-1367. ［12］BAHRAMI M, NARAGHI M, ZAREINEJAD M. Adaptive super-twisting observer for fault reconstruction in electro-hydraulic systems［J］. ISA Transactions, 2018, 76(5): 235-245. ［13］PALLI G, STRANO S, TERZO M. Sliding-mode observers for state and disturbance estimation in electro-hydraulic systems［J］. Control Engineering Practice, 2018, 74(5): 58-70. ［14］FALUGI P, MAYNE D Q. Getting robustness against unstructured uncertainty: a tube-based mpc approach［J］. IEEE Transactions on Automatic Control, 2014, 59(5): 1290-1295. ［15］WEN L, LI X, GAO L, et al. A new convolutional neural network-based data-driven fault diagnosis method［J］. IEEE Transactions on Industrial Electro-nics, 2018, 65(7): 5990-5998. ［16］纪洪泉, 何潇, 周东华. 基于多元统计分析的故障检测方法［J］. 上海交通大学学报, 2015, 49(6): 842-848. JI Hongquan, HE Xiao, ZHOU Donghua. Fault detection techniques based on multivariate statistical analysis［J］. Journal of Shanghai Jiao Tong University, 2015, 49(6): 842-848. ［17］SHARIFI S, TIVAY A, REZAEI S, et al. Leakage fault detection in electro-hydraulic servo systems using a nonlinear representation learning approach［J］. Isa Transactions, 2018, 73(2): 154-164. ［18］HE X. Fault diagnosis approach of hydraulic system using FARX model［J］. Procedia Engineering, 2011, 15(1): 949-953. ［19］FU X B, LIU B, ZHANG Y C, et al. Fault diagnosis of hydraulic system in large forging hydraulic press［J］. Measurement, 2014, 49(1): 390-396. ［20］DAI X, GAO Z. From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis［J］. IEEE Transactions on Industrial Informatics, 2013, 9(4): 2226-2238. ［21］王春行. 液压控制系统［M］. 北京: 机械工业出版社, 2011. WANG Chunxing. Hydraulic control system ［M］. Beijing: China Machine Press, 2011. ［22］XU W, CHEN W, LIANG Y. Feasibility study on the least square method for fitting non-Gaussian noise data［J］. Physica A Statistical Mechanics and Its Applications, 2018, 492: 1917-1930. ［23］魏彤, 田双彪. 基于RLS-DE算法的多变量径向磁轴承系统辨识［J］. 机械工程学报, 2016, 52(3): 143-150. WEI Tong, TIAN Shuangbiao. The identification of multivariable radial magnetic bearing system based on RLS-DE algorithm［J］. Journal of Mechanical Engineering, 2016, 52(3): 143-150. ［24］CHEN M, CHEN C. Robust nonlinear observer for lipschitz nonlinear systems subject to disturbances［J］. IEEE Tran-sactions on Automatic Control, 2007, 52(12): 2365-2369. ［25］DING S X. Model-based fault diagnosis techniques: design schemes, algorithms, and tools［M］. Berlin Heidelberg: Springer-Verlag, 2008. ［26］GAO Z, LIU X, CHEN M. Unknown input observer based robust fault estimation for systems corrupted by partially-decoupled disturbances［J］. IEEE Tran-sactions on Industrial Electronics, 2015, 63(4): 1-1. ［27］ZEMOUCHE A, BOUTAYEB M. On LMI conditions to design observers for Lipschitz nonlinear systems［J］. Automatica, 2013, 49(2): 585-591. ［28］HAN H, YANG Y, LI L, et al. Observer-based fault detection for uncertain nonlinear systems［J］. Journal of the Franklin Institute, 2018, 355(3): 1278-1295. ［29］WITCZAK P, PATAN K, WITCZAK M, et al. A neural network approach to simultaneous state and actuator fault estimation under unknown input decoup-ling［J］. Neurocomputing, 2017, 250: 65-75. ［30］ABBASPOUR A, ABOUTALEBI P, YEN K, et al. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV［J］. ISA Transactions, 2017, 67: 317-329.

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