A Combined Data-Driven and Model-Based Residual Selection Algorithm for Fault Detection and Isolation

Selecting residual generators for detecting and isolating faults in a system is an important step when designing model-based diagnosis systems. However, finding a suitable set of residual generators to fulfill performance requirements is complicated by model uncertainties and measurement noise that...

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Bibliographic Details
Published in:IEEE transactions on control systems technology Vol. 27; no. 2; pp. 616 - 630
Main Authors: Jung, Daniel, Sundstrom, Christofer
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Automotive applications
change detection algorithms
Computational modeling
Data models
Engines
Fault detection
Fault diagnosis
Generators
Internal combustion engines
machine learning
Mathematical model
Noise measurement
Systems design
ISSN:1063-6536, 1558-0865, 1558-0865
Online Access:Get full text
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Summary:Selecting residual generators for detecting and isolating faults in a system is an important step when designing model-based diagnosis systems. However, finding a suitable set of residual generators to fulfill performance requirements is complicated by model uncertainties and measurement noise that have negative impact on fault detection performance. The main contribution is an algorithm for residual selection that combines model-based and data-driven methods to find a set of residual generators that maximizes fault detection and isolation performance. Based on the solution from the residual selection algorithm, a generalized diagnosis system design is proposed where test quantities are designed using multivariate residual information to improve detection performance. To illustrate the usefulness of the proposed residual selection algorithm, it is applied to find a set of residual generators to monitor the air path through an internal combustion engine.
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ISSN:1063-6536
1558-0865
1558-0865
DOI:10.1109/TCST.2017.2773514