Title :
A probabilistic approach to fault diagnosis of industrial systems
Author :
Barigozzi, Alberto ; Magni, Lalo ; Scattolini, Riccardo
Author_Institution :
Dipt. di Informatica e Sistemistica, Univ. of Pavia, Italy
Abstract :
A method for fault diagnosis of industrial systems is presented. Plant devices, sensors, actuators and diagnostic tests are described as stochastic finite-state machines. A formal composition rule of these models is given to obtain: 1) the set of admissible fault signatures; 2) their conditional probability given any fault; and 3) the conditional probability of a fault given a prescribed signature. The modularity and flexibility of this method make it suitable to deal with complex systems made by a large number of components. The method is used in an industrial automotive application, specifically the diagnosis of the throttle body and of the angular sensors measuring the throttle plate angle is described in detail.
Keywords :
fault diagnosis; finite state machines; large-scale systems; probability; complex systems; fault diagnosis; industrial automotive applications; industrial systems; probabilistic models; stochastic finite-state machines; throttle plate angle; Actuators; Automotive applications; Automotive engineering; Data mining; Fault diagnosis; Information analysis; Sensor phenomena and characterization; Signal analysis; Stochastic processes; System testing; Automata; automotive; fault diagnosis; finite-state machines; probabilistic models;
Journal_Title :
Control Systems Technology, IEEE Transactions on
DOI :
10.1109/TCST.2004.833606
