A model-based probabilistic approach for fault detection and identification with application to the diagnosis of automotive engines

被引：19
|
作者：
Dinca, L [1 ]
Aldemir, T [1 ]
Rizzoni, G [1 ]
机构：
[1] Ohio State Univ, Dept Mech Engn, Columbus, OH 43210 USA
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 1999年 / 44卷 / 11期
关键词：
automotive engines; dynamic systems; fault diagnosis; Markov models; parameter and state estimation;
D O I：
10.1109/9.802945
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A model-based parameter and state estimation technique is presented toward fault diagnosis in dynamic systems, The methodology is based on the representation of the system dynamics in terms of transition probabilities between user-specified sets of magnitude intervals of system parameters and state variables during user-specified time intervals. These intervals may reflect noise in the monitored data, random changes in the parameters, or modeling uncertainties in general. The transition probabilities are obtained from a given system model that yields the current values of the state variables in discrete time from their values at the previous time step and the values of the system parameters at the previous time Step. Implementation of the methodology on a simplified model of the air, inertial, fuel, and exhaust dynamics of the powertrain of a vehicle shows that the methodology is capable of estimating the system parameters and tracking the unmonitored dynamic variables within the user-specified magnitude intervals.
引用
收藏
页码：2200 / 2205
页数：6
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