TY - GEN
T1 - A fault detection and diagnosis scheme for discrete nonlinear system using output probability density estimation
AU - Zhang, Yumin
AU - Wang, Qing Guo
AU - Lum, Kai Yew
PY - 2008
Y1 - 2008
N2 - In this paper, a fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighted average function is given as an integral form of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear .lter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new adaptive fault diagnosis algorithm is further investigated to estimate the fault. The simulation example given demonstrates the effectiveness of the proposed approaches.
AB - In this paper, a fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighted average function is given as an integral form of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear .lter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new adaptive fault diagnosis algorithm is further investigated to estimate the fault. The simulation example given demonstrates the effectiveness of the proposed approaches.
KW - Fault detection
KW - Fault diagnosis
KW - Probability density function
UR - http://www.scopus.com/inward/record.url?scp=56449107675&partnerID=8YFLogxK
U2 - 10.1109/ICAL.2008.4636117
DO - 10.1109/ICAL.2008.4636117
M3 - Conference contribution
AN - SCOPUS:56449107675
SN - 9781424425020
T3 - Proceedings of the IEEE International Conference on Automation and Logistics, ICAL 2008
SP - 45
EP - 49
BT - Proceedings of the IEEE International Conference on Automation and Logistics, ICAL 2008
T2 - IEEE International Conference on Automation and Logistics, ICAL 2008
Y2 - 1 September 2008 through 3 September 2008
ER -