Data model-based sensor fault diagnosis algorithm for closed-loop control systems
•A linear model is dynamically established based on the gradient change of the control variables from the sensor data, and a baseline model tracker is designed.•The engine baseline model is trained and updated using historical data from healthy sensors.•Reasonable diagnostic thresholds are calculate...
Saved in:
| Published in: | Measurement : journal of the International Measurement Confederation Vol. 246; p. 116715 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
31.03.2025
|
| Subjects: | |
| ISSN: | 0263-2241 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •A linear model is dynamically established based on the gradient change of the control variables from the sensor data, and a baseline model tracker is designed.•The engine baseline model is trained and updated using historical data from healthy sensors.•Reasonable diagnostic thresholds are calculated using the characteristics of historical data from healthy sensors.•The performance of four sensor fault threshold-setting methods is compared under various sensor fault modes.
The varying operational parameters and random noise make it difficult to determine the fault diagnosis thresholds for engine sensors under different working conditions. Therefore, an adaptive threshold-based fault diagnosis method for aeroengine sensors is proposed. A multivariable control system based on the MFAC method is established for the aeroengine. The OS-ELM algorithm employs historical sensor data to train and update the engine baseline model. MFAC dynamically establishes a linear model based on the pseudo-gradient change of control variables from the current sensor data and designs a baseline model tracker to calculate reasonable diagnostic thresholds based on historical sensor data characteristics, thereby improving the efficiency of threshold calculation and diagnostic accuracy. The experimental results validate that this method improves the fault detection rate by at least 30% while ensuring a low false alarm rate, reduces the minimum detectable fault magnitude by 39%, and keeps the fault detection time within 0.2 s. |
|---|---|
| AbstractList | •A linear model is dynamically established based on the gradient change of the control variables from the sensor data, and a baseline model tracker is designed.•The engine baseline model is trained and updated using historical data from healthy sensors.•Reasonable diagnostic thresholds are calculated using the characteristics of historical data from healthy sensors.•The performance of four sensor fault threshold-setting methods is compared under various sensor fault modes.
The varying operational parameters and random noise make it difficult to determine the fault diagnosis thresholds for engine sensors under different working conditions. Therefore, an adaptive threshold-based fault diagnosis method for aeroengine sensors is proposed. A multivariable control system based on the MFAC method is established for the aeroengine. The OS-ELM algorithm employs historical sensor data to train and update the engine baseline model. MFAC dynamically establishes a linear model based on the pseudo-gradient change of control variables from the current sensor data and designs a baseline model tracker to calculate reasonable diagnostic thresholds based on historical sensor data characteristics, thereby improving the efficiency of threshold calculation and diagnostic accuracy. The experimental results validate that this method improves the fault detection rate by at least 30% while ensuring a low false alarm rate, reduces the minimum detectable fault magnitude by 39%, and keeps the fault detection time within 0.2 s. |
| ArticleNumber | 116715 |
| Author | Zhou, Xin Han, Xinhao Huang, Jinquan Lu, Feng |
| Author_xml | – sequence: 1 givenname: Xinhao orcidid: 0009-0000-3857-2600 surname: Han fullname: Han, Xinhao email: xinhaoh@nuaa.edu.cn – sequence: 2 givenname: Xin surname: Zhou fullname: Zhou, Xin email: zhouxin1018@126.com – sequence: 3 givenname: Feng surname: Lu fullname: Lu, Feng email: lufengnuaa@126.com – sequence: 4 givenname: Jinquan surname: Huang fullname: Huang, Jinquan email: jhuang@nuaa.edu.cn |
| BookMark | eNqNkMtqwzAQRbVIoUnaf1A_wK4etmyvSkmfECiFdi1kaZQqyFaQlEL-vg7ponSV1VyYuQfmLNBsDCMgdENJSQkVt9tyAJX2EQYYc8kIq0tKRUPrGZoTJnjBWEUv0SKlLSFE8E7M0fuDygoPwYAvepXA4ARjChFbtfcZG6c2Y0guYeU3Ibr8NWA7bbUP023hQ9hhHcYcg8fpkDIM6QpdWOUTXP_OJfp8evxYvRTrt-fX1f260JzRXChbV62piNFEiL7tBK-saQnvemEr0Tet6bveTpFXwBpim4aRpudT1kSLpuNL1J24OoaUIli5i25Q8SApkUcfciv_-JBHH_LkY-re_etql1V2x0-U82cRVicCTC9-O4gyaQejBuMi6CxNcGdQfgBbq4pv |
| CitedBy_id | crossref_primary_10_1016_j_ijepes_2025_110921 |
| Cites_doi | 10.1016/j.ins.2020.05.090 10.1109/ACC.2005.1469978 10.1109/TAC.1977.1101598 10.1109/TVT.2022.3182017 10.1016/j.isatra.2024.08.029 10.1016/j.ast.2019.105649 10.1016/j.energy.2018.06.202 10.3390/s17040835 10.3390/act12100391 10.1007/s42835-024-01830-x 10.3390/su14105905 10.1016/j.jprocont.2018.02.002 10.1109/TPEL.2024.3432163 10.1109/TIA.2019.2902532 10.1109/TR.2019.2930195 10.1016/j.chemolab.2017.01.013 10.1109/TNN.2011.2176141 10.1016/j.ymssp.2013.11.011 10.1016/j.ins.2021.02.064 10.1016/j.isatra.2019.11.035 10.2514/6.1990-1920 10.1109/TMECH.2022.3215545 10.1109/TCST.2010.2093136 10.1016/j.automatica.2024.111845 10.1109/TR.2018.2822702 10.1109/TCST.2012.2187057 10.1109/TGRS.2023.3295932 10.1016/j.compind.2019.02.001 10.1016/0005-1098(87)90087-2 10.1016/j.engappai.2024.108904 10.1109/PHM.2008.4711414 10.1109/TPEL.2024.3484469 10.1016/j.dsp.2015.04.008 10.1016/j.measurement.2022.111037 10.1016/j.conengprac.2017.06.003 10.1016/j.neucom.2005.12.126 10.1016/j.ymssp.2023.110208 10.1007/s11071-023-08561-0 10.1016/j.energy.2018.06.220 10.1109/CDC.2003.1272606 10.1016/j.ast.2022.107871 10.1109/TNN.2006.880583 10.1109/TIE.2014.2308161 10.1016/j.ins.2024.120817 10.1016/j.ymssp.2021.108668 |
| ContentType | Journal Article |
| Copyright | 2025 Elsevier Ltd |
| Copyright_xml | – notice: 2025 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.measurement.2025.116715 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Physics |
| ExternalDocumentID | 10_1016_j_measurement_2025_116715 S0263224125000740 |
| GroupedDBID | --K --M .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AATTM AAXKI AAXUO AAYWO ABFRF ABJNI ABMAC ABNEU ACDAQ ACFVG ACGFO ACGFS ACIWK ACLOT ACRLP ACVFH ADBBV ADCNI ADEZE ADTZH AEBSH AECPX AEFWE AEGXH AEIPS AEKER AENEX AEUPX AFJKZ AFPUW AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIGII AIIUN AIKHN AITUG AIVDX AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU APXCP AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFKBS EFLBG EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA GS5 IHE J1W JJJVA KOM LY7 M41 MO0 N9A O-L O9- OAUVE OGIMB OZT P-8 P-9 P2P PC. Q38 RNS ROL RPZ SDF SDG SES SEW SPC SPCBC SPD SSQ SST SSZ T5K ZMT ~G- ~HD 29M 9DU AAYXX ABFNM ABXDB ACNNM ASPBG AVWKF AZFZN CITATION EJD FEDTE FGOYB G-2 HVGLF HZ~ R2- SET WUQ XPP |
| ID | FETCH-LOGICAL-c321t-af548d40dc066b89634fd8039b6f46b78db9bff4634e270f77207b3e27c0c6793 |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001405234000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0263-2241 |
| IngestDate | Tue Nov 18 20:41:43 EST 2025 Sat Nov 29 07:28:26 EST 2025 Sun Oct 19 02:02:35 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Nonlinear tracker Data model Turboprop engine Neural network Real-time sensor fault diagnosis Adaptive threshold setting |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c321t-af548d40dc066b89634fd8039b6f46b78db9bff4634e270f77207b3e27c0c6793 |
| ORCID | 0009-0000-3857-2600 |
| ParticipantIDs | crossref_primary_10_1016_j_measurement_2025_116715 crossref_citationtrail_10_1016_j_measurement_2025_116715 elsevier_sciencedirect_doi_10_1016_j_measurement_2025_116715 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-03-31 |
| PublicationDateYYYYMMDD | 2025-03-31 |
| PublicationDate_xml | – month: 03 year: 2025 text: 2025-03-31 day: 31 |
| PublicationDecade | 2020 |
| PublicationTitle | Measurement : journal of the International Measurement Confederation |
| PublicationYear | 2025 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Xu, Li (b0135) 2024; 170 Hang, Wang, Li, Ding (b0250) 2025; 40 Hang, Qiu, Hao, Ding (b0105) 2024; 39 T. Kobayashi, D. L. Simon., Aircraft engine sensor/actuator/ component fault diagnosis using a bank of Kalman filters, NASA Technical Rep. No. NASA/CR—2003-212298. Mar. 2003, Cleveland, OH: National Aero-nautics and Space Administration, Glenn Research Center. Wang, Tianyang, et al., Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis, Mech. Syst. Signal Process. 45 (1) (2014) pp.139-153, doi:10.1016/j.ymssp.2013.11.011. Meng, Zhang, Shi, Chen, Hu, Lu (b0060) 2023; 61 Hou, Jin (b0150) 2011; 22 Swets (b0275) 2014 He, Zhang, Lu (b0170) 2020; 98 A. Saxena, K. Goebel, D. Simon and N. Eklund, Damage propagation modeling for aircraft engine run-to-failure simulation, International Conference on Prognostics and Health Management, Denver, CO, USA, 06-09 Oct 2008, doi: 10.1109/PHM.2008.4711414. Bakdi, Kouadri (b0085) 2017; 162 Tan, Mu, Fu (b0185) 2022; 194 Pan, Lü, Wang (b0225) 2018; 160 Liang, Huang, Saratchandran (b0215) 2006; 17 Yang (b0195) 2020; 540 Nakajima, West (b0095) 2015; 47 Alag G, Gilyard G., A proposed kalman filter algorithm for estimation of unmeasured output variables for an F100 turbofan engine, In Proceedings of the 26th Joint Propulsion Conference, Orlando, FL, USA, 16–18 July 1990. Hou, Jin (b0145) 2011; 19 Chang, Huang, Lu (b0165) 2017; 17 Cartocci, Napolitano, Costante (b0205) 2022; 170 D. Tolani, M. Yasar, Shin Chin and A. Ray, Anomaly detection for health management of aircraft gas turbine engines, Proceedings of the 2005, American Control Conference, Portland, OR, USA, 08-10 June 2005. doi:10.1109/ACC.2005.1469978. Hou, Jin (b0235) 2013 Zhao, Cui, Liu (b0040) 2022; 28 Zhao, Shao, Cui (b0050) 2024; 154 Qiu, Chang, Chen (b0015) 2022; 14 Bakdi, Kouadri, Bens (b0090) 2017; 66 Xu, Jiang, Shi (b0155) 2014; 61 E. Lughofer, H. Efendic, L. D. Re, and E. P. Klement., Filtering of dynamic measurements in intelligent sensors for fault detection based on data-driven models, In Proc., 42nd IEEE Int. Conf. on Decision and Control, Maui, HI, USA, 2003, pp. 463–468, doi:10.1109/CDC.2003.1272606. A. Kumar, D.E. Viassolo., Model-based fault tolerant control, NASA Technical Rep. No. NASA/CR 2008-215273. 2008, New York: General Electric Global Research. Zhang, Tang, Decastro (b0100) 2013; 21 Zhou (b0245) 2006 Hu, Tang, Tan, Zhang (b0035) 2023; 12 Miaofen, Youmin, Tianyang (b0045) 2023; 191 Huang, Zhu, Siew (b0210) 2006; 70 Yang (b0200) 2021; 566 Jin, Zhou, Lu (b0005) 2023; 111 Hou, Jin (b0240) 2010; 19 Nyulaszi, Andoga, Butka (b0180) 2018; 15 Zhao (b0190) 2019; 107 Beard (b0160) 1971 Xu, Guo (b0260) 2022; 71 Tan, Zhang, Li, Wu (b0125) 2023; 59 Clarke, Mohtadi, Tuffs (b0270) 1987; 23 Amirkhani, Chaibakhsh, Ghaffari (b0115) 2020; 100 Wang (b0140) 2024; 677 Hanachi, Mechefske, Liu, Banerjee, Chen (b0030) 2018; 67 Pérez-Cruz (b0280) 2008 Kordestani, Saif, Orchard, Razavi-Far, Khorasani (b0025) 2021; 70 Ni (b0120) 2024; 136 Navi, Meskin, Davoodi (b0110) 2018; 64 Huang, Chen, Chai (b0175) 2022; 130 J.T. Csank, D. L. Simon., Sensor data qualification technique applied to gas turbine engines, NASA Technical Rep. No. NASA/ CR—2013-216609. Dec.2013, Cleveland, OH: National Aeronautics and Space Admin-istration, Glenn Research Center. Lan, Zhao (b0130) 2024; 19 Deckert, Desai, Deyst (b0010) 1977; 22 Lipu, Hannan, Hussain (b0230) 2019; 55 Silitonga, Masjuki, Ong (b0220) 2018; 159 10.1016/j.measurement.2025.116715_b0055 Pan (10.1016/j.measurement.2025.116715_b0225) 2018; 160 10.1016/j.measurement.2025.116715_b0255 Lan (10.1016/j.measurement.2025.116715_b0130) 2024; 19 Xu (10.1016/j.measurement.2025.116715_b0260) 2022; 71 Huang (10.1016/j.measurement.2025.116715_b0175) 2022; 130 Meng (10.1016/j.measurement.2025.116715_b0060) 2023; 61 Deckert (10.1016/j.measurement.2025.116715_b0010) 1977; 22 10.1016/j.measurement.2025.116715_b0065 10.1016/j.measurement.2025.116715_b0020 10.1016/j.measurement.2025.116715_b0265 Hou (10.1016/j.measurement.2025.116715_b0240) 2010; 19 Ni (10.1016/j.measurement.2025.116715_b0120) 2024; 136 Xu (10.1016/j.measurement.2025.116715_b0135) 2024; 170 Navi (10.1016/j.measurement.2025.116715_b0110) 2018; 64 Nyulaszi (10.1016/j.measurement.2025.116715_b0180) 2018; 15 Qiu (10.1016/j.measurement.2025.116715_b0015) 2022; 14 Hu (10.1016/j.measurement.2025.116715_b0035) 2023; 12 Tan (10.1016/j.measurement.2025.116715_b0185) 2022; 194 Hanachi (10.1016/j.measurement.2025.116715_b0030) 2018; 67 Wang (10.1016/j.measurement.2025.116715_b0140) 2024; 677 Lipu (10.1016/j.measurement.2025.116715_b0230) 2019; 55 10.1016/j.measurement.2025.116715_b0070 Tan (10.1016/j.measurement.2025.116715_b0125) 2023; 59 10.1016/j.measurement.2025.116715_b0075 Swets (10.1016/j.measurement.2025.116715_b0275) 2014 Amirkhani (10.1016/j.measurement.2025.116715_b0115) 2020; 100 Hang (10.1016/j.measurement.2025.116715_b0105) 2024; 39 Liang (10.1016/j.measurement.2025.116715_b0215) 2006; 17 Pérez-Cruz (10.1016/j.measurement.2025.116715_b0280) 2008 Zhang (10.1016/j.measurement.2025.116715_b0100) 2013; 21 Zhao (10.1016/j.measurement.2025.116715_b0190) 2019; 107 Chang (10.1016/j.measurement.2025.116715_b0165) 2017; 17 Hou (10.1016/j.measurement.2025.116715_b0235) 2013 Clarke (10.1016/j.measurement.2025.116715_b0270) 1987; 23 Zhou (10.1016/j.measurement.2025.116715_b0245) 2006 Hang (10.1016/j.measurement.2025.116715_b0250) 2025; 40 He (10.1016/j.measurement.2025.116715_b0170) 2020; 98 Bakdi (10.1016/j.measurement.2025.116715_b0085) 2017; 162 10.1016/j.measurement.2025.116715_b0080 Yang (10.1016/j.measurement.2025.116715_b0195) 2020; 540 Miaofen (10.1016/j.measurement.2025.116715_b0045) 2023; 191 Nakajima (10.1016/j.measurement.2025.116715_b0095) 2015; 47 Cartocci (10.1016/j.measurement.2025.116715_b0205) 2022; 170 Bakdi (10.1016/j.measurement.2025.116715_b0090) 2017; 66 Hou (10.1016/j.measurement.2025.116715_b0150) 2011; 22 Silitonga (10.1016/j.measurement.2025.116715_b0220) 2018; 159 Huang (10.1016/j.measurement.2025.116715_b0210) 2006; 70 Beard (10.1016/j.measurement.2025.116715_b0160) 1971 Kordestani (10.1016/j.measurement.2025.116715_b0025) 2021; 70 Zhao (10.1016/j.measurement.2025.116715_b0040) 2022; 28 Jin (10.1016/j.measurement.2025.116715_b0005) 2023; 111 Hou (10.1016/j.measurement.2025.116715_b0145) 2011; 19 Yang (10.1016/j.measurement.2025.116715_b0200) 2021; 566 Zhao (10.1016/j.measurement.2025.116715_b0050) 2024; 154 Xu (10.1016/j.measurement.2025.116715_b0155) 2014; 61 |
| References_xml | – reference: Wang, Tianyang, et al., Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis, Mech. Syst. Signal Process. 45 (1) (2014) pp.139-153, doi:10.1016/j.ymssp.2013.11.011. – volume: 154 start-page: 335 year: 2024 end-page: 351 ident: b0050 article-title: CTNet: A data-driven time-frequency technique for wind turbines fault diagnosis under time-varying speeds publication-title: ISA Trans. – volume: 194 year: 2022 ident: b0185 article-title: A new sensor fault diagnosis method for gas leakage monitoring based on the naive Bayes and probabilistic neural network classifier publication-title: Measurement – volume: 159 start-page: 1075 year: 2018 end-page: 1087 ident: b0220 article-title: Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine publication-title: Energy – volume: 47 start-page: 5 year: 2015 end-page: 16 ident: b0095 article-title: Dynamic network signal processing using latent threshold models publication-title: Digital Sig-Nal Process – year: 2013 ident: b0235 article-title: Model Free Adaptive Control: Theory and Applications, Boca Raton, FL – volume: 130 year: 2022 ident: b0175 article-title: A unified framework of fault detection and diagnosis based on frac-tional-order chaos system publication-title: Aerosp. Sci. Technol. – volume: 160 start-page: 466 year: 2018 end-page: 477 ident: b0225 article-title: Novel battery state-of-health online estimation method using multi-ple health indicators and an extreme learning machine publication-title: Energy – volume: 39 start-page: 13808 year: 2024 end-page: 13817 ident: b0105 article-title: Improved fault diagnosis method for permanent magnet synchronous machine system based on lightweight multisource information data layer fusion publication-title: IEEE Trans. Power Electron. – volume: 61 year: 2023 ident: b0060 article-title: A robust infrared small target detection method jointing multiple information and noise prediction: Algorithm and Benchmark publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 12 start-page: 391 year: 2023 ident: b0035 article-title: October. Fault detection for point machines: A review, challenges, and perspectives publication-title: Actuators – volume: 17 start-page: 835 year: 2017 ident: b0165 article-title: Robust in-flight sensor fault diagnostics for aircraft engine based on sliding mode observers publication-title: Sensors – volume: 98 year: 2020 ident: b0170 article-title: Performance comparison of representative model-based fault recon-struction algorithms for aircraft sensor fault detection and diagnosis publication-title: Aerosp. Sci. Technol. – volume: 64 start-page: 37 year: 2018 end-page: 48 ident: b0110 article-title: Sensor fault detection and isolation of an industrial gas turbine us-ing partial adaptive KPCA publication-title: J. Process Control. – volume: 17 start-page: 1411 year: 2006 end-page: 1423 ident: b0215 article-title: A fast and accurate online sequential learning algorithm for feedforward networks publication-title: IEEE Trans. Neural Netw. – volume: 170 year: 2024 ident: b0135 article-title: PDE-based observation and predictor-based control for linear systems with distributed infinite input and output delays publication-title: Automatica – volume: 14 start-page: 5905 year: 2022 ident: b0015 article-title: Research on the analytical redundancy method for the control system of variable cycle engine publication-title: Sustainability – reference: J.T. Csank, D. L. Simon., Sensor data qualification technique applied to gas turbine engines, NASA Technical Rep. No. NASA/ CR—2013-216609. Dec.2013, Cleveland, OH: National Aeronautics and Space Admin-istration, Glenn Research Center. – reference: Alag G, Gilyard G., A proposed kalman filter algorithm for estimation of unmeasured output variables for an F100 turbofan engine, In Proceedings of the 26th Joint Propulsion Conference, Orlando, FL, USA, 16–18 July 1990. – volume: 170 year: 2022 ident: b0205 article-title: Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria publication-title: Mech. Syst. Signal Process. – volume: 107 start-page: 59 year: 2019 end-page: 66 ident: b0190 article-title: Tianyang Wang, and Fulei Chu, Deep convolutional neural network based planet bearing fault classification publication-title: Comput. Ind. – volume: 70 start-page: 728 year: 2021 end-page: 748 ident: b0025 article-title: Failure prognosis and applica-tions—A survey of recent literature publication-title: IEEE Trans. Reliab – reference: E. Lughofer, H. Efendic, L. D. Re, and E. P. Klement., Filtering of dynamic measurements in intelligent sensors for fault detection based on data-driven models, In Proc., 42nd IEEE Int. Conf. on Decision and Control, Maui, HI, USA, 2003, pp. 463–468, doi:10.1109/CDC.2003.1272606. – volume: 66 start-page: 64 year: 2017 end-page: 75 ident: b0090 article-title: Fault detection and diagnosis in a cement rotary kiln using PCA with EWMA-based adaptive threshold monitoring scheme publication-title: Control Eng. Pract. – volume: 19 start-page: 3781 year: 2024 end-page: 3794 ident: b0130 article-title: Improving track performance by combining padé-approximation-based preview repetitive control and equivalent-input-disturbance publication-title: J. Electr. Eng. Technol – start-page: 1666 year: 2008 end-page: 1670 ident: b0280 article-title: Kullback-Leibler divergence estimation of continuous distributions publication-title: Proc. IEEE Int. Symp. Inf. Theory – reference: T. Kobayashi, D. L. Simon., Aircraft engine sensor/actuator/ component fault diagnosis using a bank of Kalman filters, NASA Technical Rep. No. NASA/CR—2003-212298. Mar. 2003, Cleveland, OH: National Aero-nautics and Space Administration, Glenn Research Center. – volume: 21 start-page: 861 year: 2013 end-page: 868 ident: b0100 article-title: Robust fault diagnosis of aircraft engines: A nonlinear adaptive esti-mation-based approach publication-title: IEEE Trans. Control Syst. Technol. – volume: 55 start-page: 4225 year: 2019 end-page: 4234 ident: b0230 article-title: Extreme learning machine model for state-of-charge estima-tion of lithium-ion battery using gravitational search algorithm publication-title: IEEE Trans. Ind. Appl. – volume: 677 year: 2024 ident: b0140 article-title: Adaptive PI event-triggered control for MIMO nonlinear systems with input delay publication-title: Inf. Sci. – volume: 22 start-page: 2173 year: 2011 end-page: 2188 ident: b0150 article-title: Data driven model free adaptive control for a class of MIMO nonlinear discrete-time systems publication-title: IEEE Trans. Neural Netw. – volume: 71 start-page: 9422 year: 2022 end-page: 9434 ident: b0260 article-title: A Novel DVL Calibration Method Based on Robust Invariant Extended Kalman Filter publication-title: IEEE Trans. Veh. Technol. – volume: 162 start-page: 83 year: 2017 end-page: 93 ident: b0085 article-title: A new adaptive PCA based thresholding scheme for fault detection in complex systems publication-title: Chemom. Intell. Lab. Syst. – volume: 540 start-page: 117 year: 2020 end-page: 130 ident: b0195 article-title: A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection publication-title: Inf. Sci. – year: 2014 ident: b0275 article-title: Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers, New York, NY – reference: A. Kumar, D.E. Viassolo., Model-based fault tolerant control, NASA Technical Rep. No. NASA/CR 2008-215273. 2008, New York: General Electric Global Research. – volume: 61 start-page: 6391 year: 2014 end-page: 6398 ident: b0155 article-title: A novel model-free adaptive control design for multivariable industrial processes publication-title: IEEE Trans. Ind. Electron. – volume: 19 start-page: 1549 year: 2010 end-page: 1558 ident: b0240 article-title: A novel data-driven control approach for a class of discrete-time non-linear systems publication-title: IEEE Trans. Control Syst. Technol. – volume: 191 year: 2023 ident: b0045 article-title: Adaptive synchronous demodulation transform with application to analyzing multicomponent signals for machinery fault diagnostics publication-title: Mech. Syst. Sig. Process. – volume: 15 start-page: 189 year: 2018 end-page: 209 ident: b0180 article-title: Fault detection and isolation of an aircraft turbojet engine using a multi-sensor network and multiple model approach publication-title: Acta Polytech. Hung. – volume: 19 start-page: 1549 year: 2011 end-page: 1558 ident: b0145 article-title: A novel data-driven control approach for a class of discrete-time nonlinear systems publication-title: IEEE Trans. Control Syst. Technol. – volume: 22 start-page: 795 year: 1977 end-page: 803 ident: b0010 article-title: F-8 DFBW sensor failure identification using analytic redundancy publication-title: IEEE Trans. Autom. Control – volume: 40 start-page: 3395 year: 2025 end-page: 3404 ident: b0250 article-title: Interturn Short-Circuit Fault Diagnosis and Fault-Tolerant Control of DTP-PMSM Based on Subspace Current Residuals publication-title: IEEE Trans. Power Electron. – reference: A. Saxena, K. Goebel, D. Simon and N. Eklund, Damage propagation modeling for aircraft engine run-to-failure simulation, International Conference on Prognostics and Health Management, Denver, CO, USA, 06-09 Oct 2008, doi: 10.1109/PHM.2008.4711414. – volume: 100 start-page: 171 year: 2020 end-page: 184 ident: b0115 article-title: Nonlinear robust fault diagnosis of power plant gas turbine using Monte Carlo-based adaptive threshold approach publication-title: ISA Trans. – volume: 28 start-page: 1627 year: 2022 end-page: 1637 ident: b0040 article-title: Bearing weak fault feature extraction under time-varying speed conditions based on frequency matching demodulation transform publication-title: IEEE/ASME Trans. Mechatron. – year: 1971 ident: b0160 article-title: Failure Accommodation in Linear Systems Through Self Reorganization publication-title: Massachusetts Insti-tute Technol. – volume: 70 start-page: 489 year: 2006 end-page: 501 ident: b0210 article-title: Extreme learning machine: theory and applications publication-title: Neurocomputing – volume: 136 year: 2024 ident: b0120 article-title: An explainable neural network integrating Jiles-Atherton and nonlinear auto-regressive exogenous models for modeling universal hysteresis publication-title: Eng. Appl. Artificial Intelligence – volume: 566 start-page: 103 year: 2021 end-page: 117 ident: b0200 article-title: A data-driven structural damage detection framework based on parallel convolutional neural network and bidirectional gated recurrent unit publication-title: Inf. Sci. – volume: 23 start-page: 137 year: 1987 end-page: 148 ident: b0270 article-title: Generalized predictive control—Part I. The Basic Algorithm publication-title: Automatica – year: 2006 ident: b0245 article-title: Research on Object-Oriented Modeling and Simulation for Aeroengine and Control System – volume: 59 start-page: 6031 year: 2023 end-page: 6043 ident: b0125 article-title: Event-triggered sliding mode control for spacecraft reorientation with multiple attitude constraints publication-title: IEEE Trans. Aerosp. Electron. Syst. – volume: 67 start-page: 1340 year: 2018 end-page: 1363 ident: b0030 article-title: Performance-based gas turbine health monitoring, diagnostics, and prognostics: A survey publication-title: IEEE Trans. Reliab – reference: D. Tolani, M. Yasar, Shin Chin and A. Ray, Anomaly detection for health management of aircraft gas turbine engines, Proceedings of the 2005, American Control Conference, Portland, OR, USA, 08-10 June 2005. doi:10.1109/ACC.2005.1469978. – volume: 111 start-page: 13215 year: 2023 end-page: 13234 ident: b0005 article-title: A novel analytical redundancy method based on decision-level fusion for aero-engine sensors publication-title: Nonlinear Dyn. – volume: 540 start-page: 117 year: 2020 ident: 10.1016/j.measurement.2025.116715_b0195 article-title: A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection publication-title: Inf. Sci. doi: 10.1016/j.ins.2020.05.090 – ident: 10.1016/j.measurement.2025.116715_b0255 doi: 10.1109/ACC.2005.1469978 – volume: 22 start-page: 795 issue: 5 year: 1977 ident: 10.1016/j.measurement.2025.116715_b0010 article-title: F-8 DFBW sensor failure identification using analytic redundancy publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.1977.1101598 – volume: 71 start-page: 9422 issue: 9 year: 2022 ident: 10.1016/j.measurement.2025.116715_b0260 article-title: A Novel DVL Calibration Method Based on Robust Invariant Extended Kalman Filter publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2022.3182017 – volume: 154 start-page: 335 year: 2024 ident: 10.1016/j.measurement.2025.116715_b0050 article-title: CTNet: A data-driven time-frequency technique for wind turbines fault diagnosis under time-varying speeds publication-title: ISA Trans. doi: 10.1016/j.isatra.2024.08.029 – volume: 98 year: 2020 ident: 10.1016/j.measurement.2025.116715_b0170 article-title: Performance comparison of representative model-based fault recon-struction algorithms for aircraft sensor fault detection and diagnosis publication-title: Aerosp. Sci. Technol. doi: 10.1016/j.ast.2019.105649 – volume: 159 start-page: 1075 year: 2018 ident: 10.1016/j.measurement.2025.116715_b0220 article-title: Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine publication-title: Energy doi: 10.1016/j.energy.2018.06.202 – volume: 17 start-page: 835 issue: 4 year: 2017 ident: 10.1016/j.measurement.2025.116715_b0165 article-title: Robust in-flight sensor fault diagnostics for aircraft engine based on sliding mode observers publication-title: Sensors doi: 10.3390/s17040835 – ident: 10.1016/j.measurement.2025.116715_b0075 – volume: 12 start-page: 391 issue: 10 year: 2023 ident: 10.1016/j.measurement.2025.116715_b0035 article-title: October. Fault detection for point machines: A review, challenges, and perspectives publication-title: Actuators doi: 10.3390/act12100391 – volume: 19 start-page: 3781 year: 2024 ident: 10.1016/j.measurement.2025.116715_b0130 article-title: Improving track performance by combining padé-approximation-based preview repetitive control and equivalent-input-disturbance publication-title: J. Electr. Eng. Technol doi: 10.1007/s42835-024-01830-x – volume: 14 start-page: 5905 issue: 10 year: 2022 ident: 10.1016/j.measurement.2025.116715_b0015 article-title: Research on the analytical redundancy method for the control system of variable cycle engine publication-title: Sustainability doi: 10.3390/su14105905 – volume: 64 start-page: 37 year: 2018 ident: 10.1016/j.measurement.2025.116715_b0110 article-title: Sensor fault detection and isolation of an industrial gas turbine us-ing partial adaptive KPCA publication-title: J. Process Control. doi: 10.1016/j.jprocont.2018.02.002 – year: 2014 ident: 10.1016/j.measurement.2025.116715_b0275 – volume: 39 start-page: 13808 issue: 10 year: 2024 ident: 10.1016/j.measurement.2025.116715_b0105 article-title: Improved fault diagnosis method for permanent magnet synchronous machine system based on lightweight multisource information data layer fusion publication-title: IEEE Trans. Power Electron. doi: 10.1109/TPEL.2024.3432163 – volume: 55 start-page: 4225 issue: 4 year: 2019 ident: 10.1016/j.measurement.2025.116715_b0230 article-title: Extreme learning machine model for state-of-charge estima-tion of lithium-ion battery using gravitational search algorithm publication-title: IEEE Trans. Ind. Appl. doi: 10.1109/TIA.2019.2902532 – volume: 70 start-page: 728 issue: 2 year: 2021 ident: 10.1016/j.measurement.2025.116715_b0025 article-title: Failure prognosis and applica-tions—A survey of recent literature publication-title: IEEE Trans. Reliab doi: 10.1109/TR.2019.2930195 – volume: 162 start-page: 83 year: 2017 ident: 10.1016/j.measurement.2025.116715_b0085 article-title: A new adaptive PCA based thresholding scheme for fault detection in complex systems publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2017.01.013 – volume: 22 start-page: 2173 issue: 12 year: 2011 ident: 10.1016/j.measurement.2025.116715_b0150 article-title: Data driven model free adaptive control for a class of MIMO nonlinear discrete-time systems publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2011.2176141 – ident: 10.1016/j.measurement.2025.116715_b0055 doi: 10.1016/j.ymssp.2013.11.011 – ident: 10.1016/j.measurement.2025.116715_b0080 – volume: 566 start-page: 103 year: 2021 ident: 10.1016/j.measurement.2025.116715_b0200 article-title: A data-driven structural damage detection framework based on parallel convolutional neural network and bidirectional gated recurrent unit publication-title: Inf. Sci. doi: 10.1016/j.ins.2021.02.064 – volume: 100 start-page: 171 year: 2020 ident: 10.1016/j.measurement.2025.116715_b0115 article-title: Nonlinear robust fault diagnosis of power plant gas turbine using Monte Carlo-based adaptive threshold approach publication-title: ISA Trans. doi: 10.1016/j.isatra.2019.11.035 – volume: 59 start-page: 6031 issue: 5 year: 2023 ident: 10.1016/j.measurement.2025.116715_b0125 article-title: Event-triggered sliding mode control for spacecraft reorientation with multiple attitude constraints publication-title: IEEE Trans. Aerosp. Electron. Syst. – ident: 10.1016/j.measurement.2025.116715_b0020 doi: 10.2514/6.1990-1920 – volume: 28 start-page: 1627 issue: 3 year: 2022 ident: 10.1016/j.measurement.2025.116715_b0040 article-title: Bearing weak fault feature extraction under time-varying speed conditions based on frequency matching demodulation transform publication-title: IEEE/ASME Trans. Mechatron. doi: 10.1109/TMECH.2022.3215545 – ident: 10.1016/j.measurement.2025.116715_b0070 – volume: 19 start-page: 1549 issue: 6 year: 2010 ident: 10.1016/j.measurement.2025.116715_b0240 article-title: A novel data-driven control approach for a class of discrete-time non-linear systems publication-title: IEEE Trans. Control Syst. Technol. doi: 10.1109/TCST.2010.2093136 – volume: 170 year: 2024 ident: 10.1016/j.measurement.2025.116715_b0135 article-title: PDE-based observation and predictor-based control for linear systems with distributed infinite input and output delays publication-title: Automatica doi: 10.1016/j.automatica.2024.111845 – year: 1971 ident: 10.1016/j.measurement.2025.116715_b0160 article-title: Failure Accommodation in Linear Systems Through Self Reorganization publication-title: Massachusetts Insti-tute Technol. – volume: 67 start-page: 1340 issue: 3 year: 2018 ident: 10.1016/j.measurement.2025.116715_b0030 article-title: Performance-based gas turbine health monitoring, diagnostics, and prognostics: A survey publication-title: IEEE Trans. Reliab doi: 10.1109/TR.2018.2822702 – volume: 21 start-page: 861 issue: 3 year: 2013 ident: 10.1016/j.measurement.2025.116715_b0100 article-title: Robust fault diagnosis of aircraft engines: A nonlinear adaptive esti-mation-based approach publication-title: IEEE Trans. Control Syst. Technol. doi: 10.1109/TCST.2012.2187057 – volume: 61 year: 2023 ident: 10.1016/j.measurement.2025.116715_b0060 article-title: A robust infrared small target detection method jointing multiple information and noise prediction: Algorithm and Benchmark publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2023.3295932 – volume: 107 start-page: 59 year: 2019 ident: 10.1016/j.measurement.2025.116715_b0190 article-title: Tianyang Wang, and Fulei Chu, Deep convolutional neural network based planet bearing fault classification publication-title: Comput. Ind. doi: 10.1016/j.compind.2019.02.001 – volume: 23 start-page: 137 issue: 2 year: 1987 ident: 10.1016/j.measurement.2025.116715_b0270 article-title: Generalized predictive control—Part I. The Basic Algorithm publication-title: Automatica doi: 10.1016/0005-1098(87)90087-2 – volume: 136 year: 2024 ident: 10.1016/j.measurement.2025.116715_b0120 article-title: An explainable neural network integrating Jiles-Atherton and nonlinear auto-regressive exogenous models for modeling universal hysteresis publication-title: Eng. Appl. Artificial Intelligence doi: 10.1016/j.engappai.2024.108904 – ident: 10.1016/j.measurement.2025.116715_b0265 doi: 10.1109/PHM.2008.4711414 – volume: 19 start-page: 1549 issue: 6 year: 2011 ident: 10.1016/j.measurement.2025.116715_b0145 article-title: A novel data-driven control approach for a class of discrete-time nonlinear systems publication-title: IEEE Trans. Control Syst. Technol. doi: 10.1109/TCST.2010.2093136 – volume: 40 start-page: 3395 issue: 2 year: 2025 ident: 10.1016/j.measurement.2025.116715_b0250 article-title: Interturn Short-Circuit Fault Diagnosis and Fault-Tolerant Control of DTP-PMSM Based on Subspace Current Residuals publication-title: IEEE Trans. Power Electron. doi: 10.1109/TPEL.2024.3484469 – volume: 47 start-page: 5 year: 2015 ident: 10.1016/j.measurement.2025.116715_b0095 article-title: Dynamic network signal processing using latent threshold models publication-title: Digital Sig-Nal Process doi: 10.1016/j.dsp.2015.04.008 – volume: 194 year: 2022 ident: 10.1016/j.measurement.2025.116715_b0185 article-title: A new sensor fault diagnosis method for gas leakage monitoring based on the naive Bayes and probabilistic neural network classifier publication-title: Measurement doi: 10.1016/j.measurement.2022.111037 – year: 2006 ident: 10.1016/j.measurement.2025.116715_b0245 – volume: 66 start-page: 64 year: 2017 ident: 10.1016/j.measurement.2025.116715_b0090 article-title: Fault detection and diagnosis in a cement rotary kiln using PCA with EWMA-based adaptive threshold monitoring scheme publication-title: Control Eng. Pract. doi: 10.1016/j.conengprac.2017.06.003 – volume: 70 start-page: 489 issue: 1 year: 2006 ident: 10.1016/j.measurement.2025.116715_b0210 article-title: Extreme learning machine: theory and applications publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.12.126 – volume: 191 year: 2023 ident: 10.1016/j.measurement.2025.116715_b0045 article-title: Adaptive synchronous demodulation transform with application to analyzing multicomponent signals for machinery fault diagnostics publication-title: Mech. Syst. Sig. Process. doi: 10.1016/j.ymssp.2023.110208 – volume: 111 start-page: 13215 issue: 14 year: 2023 ident: 10.1016/j.measurement.2025.116715_b0005 article-title: A novel analytical redundancy method based on decision-level fusion for aero-engine sensors publication-title: Nonlinear Dyn. doi: 10.1007/s11071-023-08561-0 – volume: 160 start-page: 466 year: 2018 ident: 10.1016/j.measurement.2025.116715_b0225 article-title: Novel battery state-of-health online estimation method using multi-ple health indicators and an extreme learning machine publication-title: Energy doi: 10.1016/j.energy.2018.06.220 – volume: 15 start-page: 189 issue: 2 year: 2018 ident: 10.1016/j.measurement.2025.116715_b0180 article-title: Fault detection and isolation of an aircraft turbojet engine using a multi-sensor network and multiple model approach publication-title: Acta Polytech. Hung. – ident: 10.1016/j.measurement.2025.116715_b0065 doi: 10.1109/CDC.2003.1272606 – volume: 130 year: 2022 ident: 10.1016/j.measurement.2025.116715_b0175 article-title: A unified framework of fault detection and diagnosis based on frac-tional-order chaos system publication-title: Aerosp. Sci. Technol. doi: 10.1016/j.ast.2022.107871 – volume: 17 start-page: 1411 issue: 6 year: 2006 ident: 10.1016/j.measurement.2025.116715_b0215 article-title: A fast and accurate online sequential learning algorithm for feedforward networks publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2006.880583 – volume: 61 start-page: 6391 issue: 11 year: 2014 ident: 10.1016/j.measurement.2025.116715_b0155 article-title: A novel model-free adaptive control design for multivariable industrial processes publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2014.2308161 – start-page: 1666 year: 2008 ident: 10.1016/j.measurement.2025.116715_b0280 article-title: Kullback-Leibler divergence estimation of continuous distributions publication-title: Proc. IEEE Int. Symp. Inf. Theory – volume: 677 year: 2024 ident: 10.1016/j.measurement.2025.116715_b0140 article-title: Adaptive PI event-triggered control for MIMO nonlinear systems with input delay publication-title: Inf. Sci. doi: 10.1016/j.ins.2024.120817 – volume: 170 year: 2022 ident: 10.1016/j.measurement.2025.116715_b0205 article-title: Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2021.108668 – year: 2013 ident: 10.1016/j.measurement.2025.116715_b0235 |
| SSID | ssj0006396 |
| Score | 2.4009776 |
| Snippet | •A linear model is dynamically established based on the gradient change of the control variables from the sensor data, and a baseline model tracker is... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 116715 |
| SubjectTerms | Adaptive threshold setting Data model Neural network Nonlinear tracker Real-time sensor fault diagnosis Turboprop engine |
| Title | Data model-based sensor fault diagnosis algorithm for closed-loop control systems |
| URI | https://dx.doi.org/10.1016/j.measurement.2025.116715 |
| Volume | 246 |
| WOSCitedRecordID | wos001405234000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: ScienceDirect database issn: 0263-2241 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0006396 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3fa9QwHA_npjIfRKeyuSkRfDt6tGmuScGXMSdzD0Nxwr2VtE22jq49r72xB_94v2nSNkzFifhSQmjS3vf7ueSbT78_EHoLu4hSLAzgj8SkR0kQeTz2pcelL2lI8phz0RWbYKenfLGIP00m3_tYmOuSVRW_uYmX_1XV0AfK1qGzf6HuYVLogDYoHa6gdrjeSfHvRStMgRtPb1H5tIGTar2aKrEuW821ate6opmK8rxeFe3FVedpmJU13OuVdb0c3NcbJ5t5X_RppBQ7MsFJPDG6Yfb8ontzF1mos3y6H_6PDfu6KKoLUY8Udr22vYO3UNcBUj4fUWh57pOi-ra2CLfkBZn30Xw9o9ZH1YwuTE2XDDb0tGnhrtLEMJU_rfiGfLicXY0_aaafNNMfmEyk6K2E2l_0_Hp6Mu8sKP8e2iRsHsOauHnw8WhxMuzkYL1FhqMz7_MQvRn9A3_zwF_bN47NcvYEPbaHDXxgQPIUTWS1jR45KSi30YPOBThrnqHPGjjYAQ42wMEdcPAAHDwABwNwsAMcbIGDLXCeo68fjs4Ojz1bcMPLQhK0nlBwfs2pn2dgiKYc1maqcu6HcRopGqWM52mcKmiGVBLmKziZ-SwNoZ35WQQr_Qu0UdWV3EHYVylTaZgFci5pzIjIaRAoSsOcikiQeBfxXkxJZrPR66IoZdK7HV4mjoQTLeHESHgXkWHo0qRkucugd70uEmtbGpsxASD9efjLfxu-h7ZG9O-jjXa1lq_Q_ey6LZrVawu7H264qQs |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Data+model-based+sensor+fault+diagnosis+algorithm+for+closed-loop+control+systems&rft.jtitle=Measurement+%3A+journal+of+the+International+Measurement+Confederation&rft.au=Han%2C+Xinhao&rft.au=Zhou%2C+Xin&rft.au=Lu%2C+Feng&rft.au=Huang%2C+Jinquan&rft.date=2025-03-31&rft.pub=Elsevier+Ltd&rft.issn=0263-2241&rft.volume=246&rft_id=info:doi/10.1016%2Fj.measurement.2025.116715&rft.externalDocID=S0263224125000740 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0263-2241&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0263-2241&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0263-2241&client=summon |