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Immune Systems Inspired Approach to Anomaly Detection and Fault Diagnosis for Engines

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4 Author(s)
Dragan Djurdjanovic ; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. phone: 734-763-9975; fax: 734-936-0363 email: ; Jianbo Liu ; Kenneth A. Marko ; Jun Ni

As more electronic devices are integrated into automobiles to improve the reliability, drivability and maintainability, automotive diagnosis becomes increasingly difficult to deal with. 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 partially demonstrated through application examples.

Published in:

2007 International Joint Conference on Neural Networks

Date of Conference:

12-17 Aug. 2007