Fault Prognosis of Marine Diesel Engine With Working State Transition Based on EIIKF

被引：0
作者：
Han M. [1 ]
Li J.-B. [1 ]
Xu M.-L. [1 ]
Han B. [2 ]
机构：
[1] Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian
[2] The State Key Laboratory of Navigation and Safety Technology, Shanghai Ship and Shipping Research Institute, Shanghai
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2019年 / 45卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Fault prognosis; Kalman filters; Marine diesel engine; Sequential probability ratio test;
D O I：
10.16383/j.aas.2018.c170457
中图分类号：
学科分类号：
摘要：
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. Copyright © 2019 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：920 / 926
页数：6
相关论文
共 23 条
  • [1] Cai C.T., Weng X.Y., Zhang C.B., A novel approach for marine diesel engine fault diagnosis, Cluster Computing, 20, 2, pp. 1691-1702, (2017)
  • [2] Zhou D.-H., Hu Y.-Y., Fault diagnosis techniques for dynamic systems, Acta Automatica Sinica, 35, 6, pp. 748-758, (2009)
  • [3] Basurko O.C., Uriondo Z., Condition-based maintenance for medium speed diesel engines used in vessels in operation, Applied Thermal Engineering, 80, pp. 404-412, (2015)
  • [4] Nahim H.M., Younes R., Shraim H., Ouladsine M., Oriented review to potential simulator for faults modeling in diesel engine, Journal of Marine Science and Technology, 21, 3, pp. 533-551, (2016)
  • [5] Ma J., Li G., Chen M., Nonlinear fault reconstruction based fault prognosis for rotating machinery, Acta Automatica Sinica, 40, 9, pp. 2045-2050, (2014)
  • [6] Moosavian A., Najafi G., Ghobadian B., Mirsalim M., Jafari S.M., Sharghi P., Piston scuffing fault and its identification in an IC engine by vibration analysis, Applied Acoustics, 102, pp. 40-48, (2016)
  • [7] Yang Y.S., Ming A.B., Zhang Y.Y., Zhu Y.S., Discriminative non-negative matrix factorization (DNMF) and its application to the fault diagnosis of diesel engine, Mechanical Systems and Signal Processing, 95, pp. 158-171, (2017)
  • [8] Morgan I., Liu H.H., Tormos B., Sala A., Detection and diagnosis of incipient faults in heavy-duty diesel engines, IEEE Transactions on Industrial Electronics, 57, 10, pp. 3522-3532, (2010)
  • [9] Porteiro J., Collazo J., Patino D., Miguez J.L., Diesel engine condition monitoring using a multi-net neural network system with nonintrusive sensors, Applied Thermal Engineering, 31, 17-18, pp. 4097-4105, (2011)
  • [10] Diez-Olivan A., Pagan J.A., Khoa N.L.D., Sanz R., Sierra B., Kernel-based support vector machines for automated health status assessment in monitoring sensor data, The International Journal of Advanced Manufacturing Technology, 95, 1-4, pp. 327-3401, (2018)
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