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Automotive engine FDI by application of an automated model-based and data-driven design methodology
Scania CV AB, Södertälje, Sweden.
Scania CV AB, Södertälje, Sweden.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
2013 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 21, no 4, 455-472 p.Article in journal (Refereed) Published
Abstract [en]

Fault detection and isolation (FDI) in automotive diesel engines is important in order to achieve and guarantee low exhaust emissions, high vehicle uptime, and efficient repair and maintenance. This paper illustrates how a set of general methods for model-based sequential residual generation and data-driven statistical residual evaluation can be combined into an automated design methodology. The automated design methodology is then utilized to create a complete FDI-system for an automotive diesel engine. The performance of the obtained FDI-system is evaluated using measurements from road drives and engine test-bed experiments. The overall performance of the FDI-system is good in relation to the required design effort. In particular no specific tuning of the FDI-system, or any adaption of the design methodology, was needed. It is illustrated how estimations of the statistical powers of the fault detection tests in the FDI-system can be used to further increase the performance, specifically in terms of fault isolability.

Place, publisher, year, edition, pages
Elsevier , 2013. Vol. 21, no 4, 455-472 p.
Keyword [en]
Fault diagnosis, Fault detection, Fault detection and isolation, Automotive diesel engine
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-77189DOI: 10.1016/j.conengprac.2012.12.006ISI: 000316036500011OAI: oai:DiVA.org:liu-77189DiVA: diva2:525421
Note

Funding Agencies|Scania||VINNOVA (Swedish Governmental Agency for Innovation Systems)||

Available from: 2012-05-08 Created: 2012-05-08 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Methods for Automated Design of Fault Detection and Isolation Systems with Automotive Applications
Open this publication in new window or tab >>Methods for Automated Design of Fault Detection and Isolation Systems with Automotive Applications
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Fault detection and isolation (FDI) is essential for dependability of complex technical systems. One important application area is automotive systems, where precise and robust FDI is necessary in order to maintain low exhaust emissions, high vehicle up-time, high vehicle safety, and efficent repair. To achieve good performance, and at the same time minimize the need for expensive redundant hardware, model-based FDI is necessary. A model-based FDI-system typically comprises fault detection by means of residual generation and residual evaluation, and finally fault isolation.

The overall objective of this thesis is to develop generic and theoretically sound methods for design of model-based FDI-systems. The developed methods are aimed at supporting an automated design methodology. To this end, the methods require a minimum of human interaction. By means of an automated design methodology the overall design process becomes more efficient and systematic, which also contributes to higher quality. These aspects are of particular importance in an industrial context.

Design of a model-based FDI-system for a complex real-world system is an intricate task that poses several difficulties and challenges that must be handled by the involved design methods. For instance, modeling of these systems often result in large-scale, non-linear, differential-algebraic models. Furthermore, despite substantial modeling work, models are typically not able to capture the behaviors of systems in all operating modes. This results in model-errors of time-varying nature and magnitude. This thesis develops a set of methods able to handle these issues in a systematic manner.

Two methods for model-based residual generation are developed. The two methods handle different stages of the design of residual generators. The first method considers the actual residual generator realization by means of sequential residual generation with mixed causality. The second method considers the problem of how to select an optimal set of residual generators from all possible residual generators that can be created with the first method. Together the two methods enable systematic design of a set of residual generators that fulfills a stated fault isolation requirement. Moreover, the methods are applicable to complex, large-scale, and non-linear differential-algebraic models.

Furthermore, a data-driven method for statistical residual evaluation is developed. The method relies on a comparison of the probability distributions of residuals and exploits no-fault data from the system in order to learn the behavior of no-fault residuals. The method can be used to design residual evaluators capable of handling residuals subject to stochastic uncertainties and disturbances caused by for instance time-varying model errors.

The developed methods, as well as the potential of an automated design methodology, are evaluated through extensive application studies. To verify their generality, the methods are applied to different automotive systems, as well as a wind turbine system. The performances of the obtained FDI-systems are good in relation to the required engineering effort. Particularly, no specific adaption or no tuning of the methods, or the design methodology, were made.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 35 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1448
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-77073 (URN)978-91-7519-894-1 (ISBN)
Public defence
2012-06-15, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2012-05-08 Created: 2012-05-04 Last updated: 2012-05-08Bibliographically approved

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