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

Autor:	Kenneth A. Marko, Jianbo Liu, Dragan Djurdjanovic, Jun Ni
Rok vydání:	2010
Předmět:	Automotive engine business.industry Computer science Reliability (computer networking) media_common.quotation_subject Automotive industry Maintainability Fault (power engineering) Fault detection and isolation Reliability engineering Anomaly detection Quality (business) business media_common
Zdroj:	Studies in Computational Intelligence ISBN: 9783642106897 Applications of Neural Networks in High Assurance Systems
DOI:	10.1007/978-3-642-10690-3_7
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5f9ec9f8a56c9f775c20f25ce0e2d8a9 https://doi.org/10.1007/978-3-642-10690-3_7 Zobrazit plný text záznamu