摘要本文提出了一种基于动态主成分分析方法(dynamic principal component analysis,DPCA)分析船舶主机控制系统冗余传感器数据并进而实现传感器偶发性故障诊断的方法。通过实船数据中主机排气温度多位点数据对上述方法进行验证,证明方法可用于实际故障检测并表现出较好的检测效果。
3Isermann R, Balle E Trends in the application of model based fault detection and diagnosis of technical processes[J]. Control Engineering Practice, 1997, 5(5): 709-719.
4Parthasarathy K, Jay H L. Diagnostic tools for multivariable model-based control system[J]. Industrial and Engineering Chemistry Research, 1997, 36(7): 2725- 2738.
5Anne Raich, Ali Cinar. Statistical process monitoring and disturbance diagnosis in multivariable continuous processes [J]. AIChE J, 1996, 42(4): 995-1009.
6Jie Chen, Ron J. Patton. Robust model-based fault diagnosis for dynamic systems[M]. Boston: Kluwer Academic Publishers, 1999.
7Bagheri F, Khaloozaded H, Abbaszadeh K. Stator fault detection in induction machines by parameter estimation using adaptive Kalman filter[C]. Proc of 2007 Mediterranean Conf on Control and Automation. Piscataway: IEEE, 2007: 1-6.
8Li L L, Zhou D H. Fast and robust fault diagnosis for a class of nonlinear system: Detectability analysis[J]. Computers and Chemical Engineering, 2004, 28(12): 2635-2646.
9Janos Gertler. Analytical redundancy methods in fault detection and isolation[C]. Proc of IFAC/ IMACS Symposium on Fault Detection, Supervision and Safety for Technical Processes. Baden-Baden: Pergamon Press, 1991.
10Iri M, Aoki K, O'Shima E, et al. An algorithm for diagnosis of system failures in the chemical process[J]. Computers and Chemical Engineering, 1979, 3(1/2/3/4): 489-493.
