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Immune Systems Inspired Approach to Anomaly Detection, Fault Localization and Diagnosis in Automotive Engines

  • Chapter
Applications of Neural Networks in High Assurance Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 268))

Abstract

As more electronic devices are integrated into automobiles to improve the reliability, drivability and maintainability, automotive diagnosis becomes increasingly difficult. Unavoidable design defects, quality variations in the production process as well as different usage patterns make it is infeasible to foresee all possible faults that may occur to the vehicle. As a result, many systems rely on limited diagnostic coverage provided by a diagnostic strategy which tests only for a priori known or anticipated failures, and presumes the system is operating normally if the full set of tests is passed. To circumvent these difficulties and provide a more complete coverage for detection of any fault, a new paradigm for design of automotive diagnostic systems is needed. An approach inspired by the functionalities and characteristics of natural immune system is presented and discussed in the paper. The feasibility of the newly proposed paradigm is also demonstrated through application examples.

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Authors
  1. Dragan Djurdjanovic
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  2. Jianbo Liu
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  3. Kenneth A. Marko
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  4. Jun Ni
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Editor information

Editors and Affiliations

  1. RIACS/USRA, NASA Ames Research Center, M/S 269-3, 94035, Moffett Field, CA, USA

    Johann Schumann

  2. Motorola Autonomic Networking Labs, 1301 East Algonquin Road, 60193, Schaumburg, IL, USA

    Yan Liu

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Djurdjanovic, D., Liu, J., Marko, K.A., Ni, J. (2010). Immune Systems Inspired Approach to Anomaly Detection, Fault Localization and Diagnosis in Automotive Engines. In: Schumann, J., Liu, Y. (eds) Applications of Neural Networks in High Assurance Systems. Studies in Computational Intelligence, vol 268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10690-3_7

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  • DOI: https://doi.org/10.1007/978-3-642-10690-3_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10689-7

  • Online ISBN: 978-3-642-10690-3

  • eBook Packages: EngineeringEngineering (R0)

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