2011 IEEE AUTOTESTCON最新文献_第4页

V-22 aircraft flight data mining V-22飞机飞行数据挖掘

2011 IEEE AUTOTESTCON Pub Date : 2011-10-24 DOI: 10.1109/AUTEST.2011.6058773

Michael Burger, C. Jaworowski, R. Meseroll

{"title":"V-22 aircraft flight data mining","authors":"Michael Burger, C. Jaworowski, R. Meseroll","doi":"10.1109/AUTEST.2011.6058773","DOIUrl":"https://doi.org/10.1109/AUTEST.2011.6058773","url":null,"abstract":"The Naval Air Systems Command (NAVAIR) produces and supports highly complex aircraft weapons systems which provide advanced capabilities required to defend U.S. freedoms. Supporting said complex systems such as the MV-22/CV-22 aircraft requires being able to troubleshoot and mitigate complex failure modes in dynamic operational environments. Since an aircraft is comprised of multiple systems designed by specialty sub-vendors and subsequently brought together by an aircraft integrator, diagnostics at the aircraft level are usually “good enough” but not capable of 100% fault isolation to a single component. Today's system components must be highly integrated and are required to communicate via high speed data-bus conduits which require precise synchronization between systems. Failure modes of aircraft are identified via design, analysis and test prior to fielding of the weapon system. However, not all failure modes are typically known at the time of system Initial Operational Capability, but rather are found in the field by maintainers/pilots and then subsequently mitigated with aircraft engineering changes or system replacements. Also, the requirement for increased capabilities can drive the need for new systems to be integrated into an aircraft system that may not have been considered in the initial design and support concept. There is a plethora of maintenance action detail collected by pilots, maintenance officers (MO) and engineers that can and should be used to identify failure mode trends that come to light during the operational phase of an aircraft. New troubleshooting techniques can be developed to address underlying failure modes to increase efficiency of future maintenance actions thus reducing the logistics trail required to support the aircraft. The elements available for analysis are maintenance results input by the MO/pilot, (including free form comments regarding problems and resulting actions), Built-In-Test (BIT) fault codes recorded during a flight, and off-aircraft test equipment (such as Consolidated Automated Support System CASS) historical test results. The Integrated Support Environment (ISE) is collecting the data required to perform analysis of underlying maintenance trends that can be identified using some specialized software data mining tools such as text mining of corrective action and maintainer comments data fields from maintenance results. The findings or knowledge extracted from text mining can be correlated back to fault codes recorded during flight and historical maintenance results to help mitigate issues with broken troubleshooting procedures causing headaches to the our Sailors and Marines in the field. By tagging key phrases from the maintainer's/pilot's remarks, knowledge can be gleaned into how the aircraft fails in vigorous environments. The premise of this research is to first choose an apparent high failure avionics system on the V-22 aircraft that is experiencing a high removal rate from the aircraft b","PeriodicalId":110721,"journal":{"name":"2011 IEEE AUTOTESTCON","volume":"603 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132353185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}