{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T10:32:58Z","timestamp":1721385178103},"reference-count":6,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2003,9]]},"abstract":"<jats:p> Although the Space Shuttle is a high reliability system, the health of the Space Shuttle must be accurately diagnosed in real-time. Two problems current plague the system, false alarms that may be costly, and missed alarms which may be not only expensive, but also dangerous to the crew. This paper describes the results of a feasibility study where a multivariate state estimation technique is coupled with a Bayesian Belief Network to provide both fault detection and fault diagnostic capabilities for the Space Shuttle Main Engines (SSME). Five component failure modes and several single sensor failures are simulated in our study and correctly diagnosed. The results indicate that this is a feasible fault detection and diagnosis technique and fault detection and diagnosis can be made earlier than standard redline methods allow. <\/jats:p>","DOI":"10.1142\/s0218213003001277","type":"journal-article","created":{"date-parts":[[2003,10,15]],"date-time":"2003-10-15T20:35:19Z","timestamp":1066250119000},"page":"355-374","source":"Crossref","is-referenced-by-count":4,"title":["Diagnosis of Component Failures in the Space Shuttlemain Engines  Using Bayesian Belief Network: A Feasibility Study"],"prefix":"10.1142","volume":"12","author":[{"given":"Edwina","family":"Liu","sequence":"first","affiliation":[{"name":"Expert Microsystems, Inc.,  7932 Country Trail Drive, Suite 1, Orangevale, CA 95662-2018, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Du","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science,  California State University, Sacramento, CA 95819-6021, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060401154053"},{"key":"rf4","first-page":"50","author":"Charniak E.","journal-title":"AI Magazine"},{"key":"rf5","series-title":"Bayesian Failure Detection Models","volume-title":"Intra-Laboratory Memo","author":"Herzog J. P.","year":"1998"},{"key":"rf12","volume-title":"Probablistic Reasoning: in Intelligent Systems: Networks of Plausible Inference","author":"Pearl J.","year":"1998"},{"key":"rf13","volume-title":"Discrete Mathematics and its Applications","author":"Rosen K.","year":"1991"},{"key":"rf14","volume-title":"Artificial Intelligence, A Modern Approach","author":"Russell S.","year":"1995"}],"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213003001277","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T23:14:56Z","timestamp":1565133296000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213003001277"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2003,9]]},"references-count":6,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2003,9]]}},"alternative-id":["10.1142\/S0218213003001277"],"URL":"https:\/\/doi.org\/10.1142\/s0218213003001277","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2003,9]]}}}