Fault diagnosis of nonlinear systems based on hybrid PSOSA optimization algorithm

被引:1
作者
Li L.-L. [1 ]
Zhou D.-H. [1 ]
Wang L. [1 ]
机构
[1] Department of Automation, Tsinghua University
关键词
Fault diagnosis; Moving horizon estimation; Nonlinear systems; Particle swarm optimization (PSO);
D O I
10.1007/s11633-007-0183-4
中图分类号
学科分类号
摘要
Fault diagnosis of nonlinear systems is of great importance in theory and practice, and the parameter estimation method is an effective strategy. Based on the framework of moving horizon estimation, fault parameters are identified by a proposed intelligent optimization algorithm called PSOSA, which could avoid premature convergence of standard particle swarm optimization (PSO) by introducing the probabilistic jumping property of simulated annealing (SA). Simulations on a three-tank system show the effectiveness of this optimization based fault diagnosis strategy. © 2007 Institute of Engineering Mechanics, China Earthquake Administration.
引用
收藏
页码:183 / 188
页数:5
相关论文
共 14 条
  • [1] Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N., A Review of Process Fault Detection and Diagnosis Part I: Quantitative Model-based Methods, Computers and Chemical Engineering, 27, 3, pp. 293-311, (2003)
  • [2] Frank P.M., Ding S.X., Survey of Robust Residual Generation and Evaluation Methods in Observer-based Fault Detection Systems, Journal of Process Control, 7, 6, pp. 403-424, (1997)
  • [3] Chen J., Patton R.J., Robust Model-Based Fault Diagnosis for Dynamic Systems, (1999)
  • [4] Garcia E.A., Frank P.M., Deterministic Nonlinear Observer-based Approaches to Fault Diagnosis: A Survey, Control Engineering Practice, 5, 5, pp. 663-670, (1997)
  • [5] Isermann R., Process Fault Detection Based on Modeling and Estimation Methods - A Survey, Automatica, 20, 4, pp. 387-404, (1984)
  • [6] Zhou D.H., Ye Y.Z., Modern Fault Diagnosis and Fault Tolerance Control, (2000)
  • [7] Fang C.Z., Xiao D.Y., Process Indentification, (1988)
  • [8] Fleming P.J., Purshouse R.C., Evolutionary Algorithms in Control Systems Engineering: A Survey, Control Engineering Practice, 10, 11, pp. 1223-1241, (2002)
  • [9] Kennedy J., Eberhart R.C., Particle Swarm Optimization, Proceedings of IEEE International Conference on Neural Networks, pp. 1942-1948, (1995)
  • [10] Jiang B., Wang B.W., Parameter Estimation of Nonlinear System Based on Genetic Algorithms, Control Theory and Applications, 17, 1, pp. 150-152, (2000)