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Model Based Diagnosis with Application to Automotive Engines

Model based diagnosis has shown to be a promising approach to increase the performance of diagnosis systems. One important application is automotive engines for which legislative regulations specify hard requirements. Here a model based approach is suggested for the diagnosis of the air intake system of the SI-engine. The diagnosis system is experimentally validated and it is shown that it performs well.

In the residual structure, i.e. the information of what residuals that are sensitive to what faults, a \emph{don't care} option is introduced. It is shown that \emph{don't care} improves the robustness of the diagnosis system.

Generally there exists a large number of possible residuals that can be included in the diagnosis system. Therefore a problem in the design of diagnosis systems is the selection of residuals. Another problem is how to optimally introduce \emph{don't care} in the residual structure. Both described problems and also the selection of thresholds, are solved by a systematic procedure. The procedure is based on the optimization of a new probability based performance index. The optimization is performed by using real process measurement data that are provided as an input to the procedure.

The procedure is successfully applied to the previously described problem of diagnosing the air-intake system of the SI-engine. Compared to the first approach, which involved many ad-hoc solutions, better performance is obtained in the sense that faults of less than half the size can be detected.

Central for model based diagnosis is the generation of residuals. Here a method for linear residual generator design based on parity equations is developed. It is an extension to the Chow-Willsky scheme and includes both the continuous and discrete case. Also, it is universal in the sense that all decoupling linear residual generators can be obtained. It is shown that this is not the case for previous extensions to the Chow-Willsky scheme.

Fault detectability and strong fault detectability have earlier been defined as properties of a residual. Here it is shown that both detectability and strong detectability are properties of the system. Two conditions on the system for detectability and strong detectability analysis are provided.

Mattias Nyberg


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Senast uppdaterad: 2021-11-10