title: Monitoring and fault diagnosis of hybrid systems.
abstract: Many networked embedded sensing and control systems can be modeled as hybrid systems with interacting continuous and discrete dynamics. These systems present significant challenges for monitoring and diagnosis. Many existing model-based approaches focus on diagnostic reasoning assuming appropriate fault signatures have been generated. However, an important missing piece is the integration of model-based techniques with the acquisition and processing of sensor signals and the modeling of faults to support diagnostic reasoning. This paper addresses key modeling and computational problems at the interface between model-based diagnosis techniques and signature analysis to enable the efficient detection and isolation of incipient and abrupt faults in hybrid systems. A hybrid automata model that parameterizes abrupt and incipient faults is introduced. Based on this model, an approach for diagnoser design is presented. The paper also develops a novel mode estimation algorithm that uses model-based prediction to focus distributed processing signal algorithms. Finally, the paper describes a diagnostic system architecture that integrates the modeling, prediction, and diagnosis components. The implemented architecture is applied to fault diagnosis of a complex electro-mechanical machine, the Xerox DC265 printer, and the experimental results presented validate the approach. A number of design trade-offs that were made to support implementation of the algorithms for online applications are also described.
authors.affiliation: Microsoft Research, Redmond, WA 98052, USA. zhao@microsoft.com
authors.forename: Feng
authors.initials: F
authors.lastname: Zhao
authors.forename: Xenofon
authors.initials: X
authors.lastname: Koutsoukos
authors.forename: Horst
authors.initials: H
authors.lastname: Haussecker
authors.forename: Jim
authors.initials: J
authors.lastname: Reich
authors.forename: Patrick
authors.initials: P
authors.lastname: Cheung
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D004868:Equipment Failure; D019544:Equipment Failure Analysis / Q000379:methods*; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted; D018511:Systems Integration