Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer
Aircraft engine gas-path health monitoring (GPHM) plays a critical role in engine health management (EHM). Among model-based approaches, the Kalman filter (KF) has been widely employed in GPHM. The main shortcoming of KF-based scheme lies in the lack of robustness against uncertainties. To enhance r...
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| Veröffentlicht in: | Energies (Basel) 2016, Vol.9 (8), p.598-598 |
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| creator | Chang, Xiaodong Huang, Jinquan Lu, Feng Sun, Haobo |
| description | Aircraft engine gas-path health monitoring (GPHM) plays a critical role in engine health management (EHM). Among model-based approaches, the Kalman filter (KF) has been widely employed in GPHM. The main shortcoming of KF-based scheme lies in the lack of robustness against uncertainties. To enhance robustness, this paper describes a new GPHM architecture using a sliding mode observer (SMO). The convergence of the error system in uncertainty-existing circumstances is demonstrated, and the proposed method is developed to estimate components' performance degradations regardless of modeling uncertainties. Simulations using a nonlinear model of a turbofan engine are presented, in which health monitoring problems are handled by the KF and the SMO, respectively. Results indicate the proposed approach possesses better diagnostic performance compared to the KF-based scheme, and the SMO shows its strong robustness and great potential to be applied to GPHM. |
| doi_str_mv | 10.3390/en9080598 |
| format | Article |
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Among model-based approaches, the Kalman filter (KF) has been widely employed in GPHM. The main shortcoming of KF-based scheme lies in the lack of robustness against uncertainties. To enhance robustness, this paper describes a new GPHM architecture using a sliding mode observer (SMO). The convergence of the error system in uncertainty-existing circumstances is demonstrated, and the proposed method is developed to estimate components' performance degradations regardless of modeling uncertainties. Simulations using a nonlinear model of a turbofan engine are presented, in which health monitoring problems are handled by the KF and the SMO, respectively. Results indicate the proposed approach possesses better diagnostic performance compared to the KF-based scheme, and the SMO shows its strong robustness and great potential to be applied to GPHM.</description><identifier>ISSN: 1996-1073</identifier><identifier>EISSN: 1996-1073</identifier><identifier>DOI: 10.3390/en9080598</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Aerospace engines ; Aircraft ; Aircraft engines ; Airplane engines ; Computer simulation ; Diagnostic systems ; Gas turbine engines ; Mathematical models ; Monitoring systems ; Robustness ; Sensors ; Sliding mode ; Uncertainty</subject><ispartof>Energies (Basel), 2016, Vol.9 (8), p.598-598</ispartof><rights>Copyright MDPI AG 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c428t-4893c78704c32160668993244c2bea7a00600ced62230747047da13b977ac7983</citedby><cites>FETCH-LOGICAL-c428t-4893c78704c32160668993244c2bea7a00600ced62230747047da13b977ac7983</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,4024,27923,27924,27925</link.rule.ids></links><search><creatorcontrib>Chang, Xiaodong</creatorcontrib><creatorcontrib>Huang, Jinquan</creatorcontrib><creatorcontrib>Lu, Feng</creatorcontrib><creatorcontrib>Sun, Haobo</creatorcontrib><title>Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer</title><title>Energies (Basel)</title><description>Aircraft engine gas-path health monitoring (GPHM) plays a critical role in engine health management (EHM). Among model-based approaches, the Kalman filter (KF) has been widely employed in GPHM. The main shortcoming of KF-based scheme lies in the lack of robustness against uncertainties. To enhance robustness, this paper describes a new GPHM architecture using a sliding mode observer (SMO). The convergence of the error system in uncertainty-existing circumstances is demonstrated, and the proposed method is developed to estimate components' performance degradations regardless of modeling uncertainties. Simulations using a nonlinear model of a turbofan engine are presented, in which health monitoring problems are handled by the KF and the SMO, respectively. Results indicate the proposed approach possesses better diagnostic performance compared to the KF-based scheme, and the SMO shows its strong robustness and great potential to be applied to GPHM.</description><subject>Accuracy</subject><subject>Aerospace engines</subject><subject>Aircraft</subject><subject>Aircraft engines</subject><subject>Airplane engines</subject><subject>Computer simulation</subject><subject>Diagnostic systems</subject><subject>Gas turbine engines</subject><subject>Mathematical models</subject><subject>Monitoring systems</subject><subject>Robustness</subject><subject>Sensors</subject><subject>Sliding mode</subject><subject>Uncertainty</subject><issn>1996-1073</issn><issn>1996-1073</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNkU1LAzEQhoMoWGoP_oOAFz2sJpndfBxrqa1QqfhxXtJstqZsszXZFfz3plREPDmXd2AeZnjnReickmsARW6sV0SSQskjNKBK8YwSAce_-lM0inFDUgFQABigp5mO2aPu3vDc6ibJNHZuqzvXely3AWuPxy6YoOsOT_3aeYtvdbQVTnONnxtXOb_GD21l8XIVbfiw4Qyd1LqJdvStQ_R6N32ZzLPFcnY_GS8ykzPZZblUYIQUJDfAKCecS6WA5blhK6uFJoQTYmzFGQMi8sSJSlNYKSG0EUrCEF0e9u5C-97b2JVbF41tGu1t28eSSigKTkVe_AOlBVfpHk_oxR900_bBJyN7CqiSlLJEXR0oE9oYg63LXUhvC58lJeU-i_InC_gCn5h27g</recordid><startdate>2016</startdate><enddate>2016</enddate><creator>Chang, Xiaodong</creator><creator>Huang, Jinquan</creator><creator>Lu, Feng</creator><creator>Sun, Haobo</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>2016</creationdate><title>Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer</title><author>Chang, Xiaodong ; 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Among model-based approaches, the Kalman filter (KF) has been widely employed in GPHM. The main shortcoming of KF-based scheme lies in the lack of robustness against uncertainties. To enhance robustness, this paper describes a new GPHM architecture using a sliding mode observer (SMO). The convergence of the error system in uncertainty-existing circumstances is demonstrated, and the proposed method is developed to estimate components' performance degradations regardless of modeling uncertainties. Simulations using a nonlinear model of a turbofan engine are presented, in which health monitoring problems are handled by the KF and the SMO, respectively. Results indicate the proposed approach possesses better diagnostic performance compared to the KF-based scheme, and the SMO shows its strong robustness and great potential to be applied to GPHM.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/en9080598</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| issn | 1996-1073 1996-1073 |
| language | eng |
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| source | DOAJ Directory of Open Access Journals; MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
| subjects | Accuracy Aerospace engines Aircraft Aircraft engines Airplane engines Computer simulation Diagnostic systems Gas turbine engines Mathematical models Monitoring systems Robustness Sensors Sliding mode Uncertainty |
| title | Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer |
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