Analysis of Built-In Self-Tests for Electronic Control Units
This paper addresses the problem of system design for diagnosability. Specifically, it focuses on design of built-in self-tests (BISTs) for subsystems based on electronic control units (ECUs). The BISTs play a major role in diagnosis of the systems and in particular in determining if the failure is in the ECU or externally in the sensors, detectors, or actuators. The design of BISTs involves a tradeoff between the diagnostic benefit gained by the presence of a BIST versus cost of providing it in the system.We describe a systematic methodology and software tools for quantitative tradeoff analysis of BISTs. The methodology utilizes graphical probabilistic models (Bayesian networks) to represent the diagnostic properties of the system and structured equation models to perform cost- benefit analysis. The models are developed from the knowledge of the systems (i.e. documentation and/or subject matter experts) and from data. The methodology is suitable for design of BIST for a broad range of systems. We illustrate the use of it on an example of a ECU- based subsystem for control of agricultural
How to Cite
Bayesian reasoning, diagnosis
J.M. Lu and C.W. Wu (2000). Cost and Benefit Model for Logic and Memory Test, in Proceedings of Design, Automation and Test in Europe, Paris, France.
T.C. Lu, M.J. Druzdzel, and T.Y. Leong (2000). Causal Mechanism-based Model Construction, in Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI), San Francisco, CA.
J. Pearl (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., San Mateo, California.
K.W. Przytula, D. Dash, and D. Thompson (2003). Evaluation of Bayesian Networks under Diagnostics, in Proceedings of IEEE Aerospace Conference, Big Sky, MO.
K.W. Przytula and S. Smith (2004). Diagnostic Assistant Based on Graphical Probabilistic Models, in 2004 SAE World Congress, Detroit, MI.
K.W. Przytula, G.B. Isdale, and T.C. Lu (2006). Collaborative Development of Large Bayesian Networks, in Proceedings of IEEE AUTOTESTCON, Anaheim, CA.
L.Y. Ungar and T. Ambler (2001). Economics of Built-in Self-Test, IEEE Design and Test, 15(5), pp. 70-79.
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