{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T10:27:49Z","timestamp":1777544869722,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2012,8,9]],"date-time":"2012-08-09T00:00:00Z","timestamp":1344470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can\u2019t be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.<\/jats:p>","DOI":"10.3390\/s120811061","type":"journal-article","created":{"date-parts":[[2012,8,9]],"date-time":"2012-08-09T11:22:32Z","timestamp":1344511352000},"page":"11061-11076","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Fault Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6620-2399","authenticated-orcid":false,"given":"Feng","family":"Lu","sequence":"first","affiliation":[{"name":"College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinquan","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaodong","family":"Xing","sequence":"additional","affiliation":[{"name":"College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2012,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Garg, S., Schadow, K., and Horn, W. (2010). Sensor and Actuator Needs for More Intelligent Gas Turbine Engines, NASA. NASA\/TM-2010-216746.","DOI":"10.1115\/GT2010-22685"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Martucci, A. (1998). Fault Detection and Accommodation in Real Time Embedded Full Authority Digital Electronic Engine Controls, ASME 98-GT-155.","DOI":"10.1115\/98-GT-155"},{"key":"ref_3","unstructured":"Sanjay, G. (2002). Propulsion Controls and Health Management Research at NASA Glenn Research Center, NASA. NASA\/TM-2002-211590."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jaw, L.C. (2005, January 6\u20139). Recent Advancements in Aircraft Engine Health Management (EHM) Technologies and Recommendations for the Next Step. Reno-Tahoe, NV, USA.","DOI":"10.1115\/GT2005-68625"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Simon, D.L. (2000, January 16\u201319). An Overview of the NASA Aviation Safety Program Propulsion Health Monitoring Element. Huntsville, AL, USA. AIAA-2000-3624.","DOI":"10.2514\/6.2000-3624"},{"key":"ref_6","unstructured":"Wallhagen, R.E., and Arpasi, D.J. (1974). Self-Teaching Digital Computer Program for Fail Operational Control of a Turbojet Engine in a Sea-Level Test Stand., NASA. NASA\/TM-X-3043."},{"key":"ref_7","unstructured":"Spang, H.A., and Corley, R.C. (1977, January 22\u201324). Failure detection and correction for turbofan engine. San Francisco, CA, USA. No.77CRD159."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/TAC.1976.1101146","article-title":"A generalized likelihood ration approach to the detection and estimation of jumps in linear systems","volume":"21","author":"Willsky","year":"1976","journal-title":"IEEE Trans. Autom. Control."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Brown, H., and Vizzini, R.W. (1986). Analytical redundancy technology for engine reliability engine reliability improvement. SAE-861725.","DOI":"10.4271\/861725"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Swan, J.A. (1988). Analytical Redundancy Design for Improved Engine Control Reliability Final Review., NASA. AIAA-88-3776.","DOI":"10.2514\/6.1988-3176"},{"key":"ref_11","unstructured":"Simon, D.L. (2011). An Integrated Architecture for On-Board Aircraft Engine Performance Trend Monitoring and Gas Path Fault Diagnostics, NASA. NASA\/TM-2010-216358."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.conengprac.2011.02.004","article-title":"Model-based on-board turbofan thrust estimation","volume":"19","author":"Henriksson","year":"2011","journal-title":"Control Eng. Pract."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mattern, D.L., Jaw, L.C., and Guo, T.H. (1998). Using Neural Networks for Sensor Validation, AIAA-98-35351.","DOI":"10.2514\/6.1998-3547"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.ast.2011.03.002","article-title":"Aircraft engine health management via stochastic modeling of flight data interrelations","volume":"16","author":"Dimogianopoulos","year":"2012","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2005","DOI":"10.3390\/s120202005","article-title":"A method based on multi-sensor data fusion for fault detection of planetary gearboxes","volume":"12","author":"Lei","year":"2012","journal-title":"Sensors"},{"key":"ref_16","unstructured":"Randal, R., Daniel, E.V., and Aditya, K. (2004, January 20\u201322). Towards In-Flight Detection and Accommodation of Faults in Aircraft Engines. Chicago, IL, USA. AIAA-2004-6463."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/S0306-2619(02)00015-6","article-title":"Multiple-sensor fault-diagnosis for a 2-shaft stationary gas-turbine","volume":"71","author":"Ogaji","year":"2002","journal-title":"Appl. Energy."},{"key":"ref_18","unstructured":"Zhernakov, S.V. (2000, January 15). Diagnostics and Checking of Gas Turbine Engines Parameters with Hybrid Expert Systems. Ufa, Russia."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.conengprac.2003.09.011","article-title":"Identification of sensor fault on turbofan engines using pattern recognition techniques","volume":"12","author":"Aretakis","year":"2004","journal-title":"Control Eng. Pract."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"517","DOI":"10.2514\/3.20348","article-title":"Advanced detection, isolation, and accommodation of sensor failures\u2014Real-time evaluation","volume":"11","author":"Merrill","year":"1988","journal-title":"J. Guid. Control Dyn."},{"key":"ref_21","unstructured":"Delaat, J.C., and Merrill, W.C. (1990). Advanced Detection Isolation and Accommodation of Sensor Failure in Turbofan Engine, Real-Time Microcomputer Implementation, NASA. NASA-90-2925."},{"key":"ref_22","unstructured":"Kobayashi, T. (2003). Aircraft Engine Sensor\/Actuator\/Component Fault Diagnosis Using a Bank of Kalman Filters, NASA. NASA\/CR-2003-212298."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kobayashi, T., and Simon, D.L. (2004, January 14\u201317). Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics. Vienna, Austria. NASA\/TM-2004-213203.","DOI":"10.1115\/GT2004-53640"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kobayashi, T., and Simon, D.L. (2008, January 9\u201313). Aircraft Engine On-Line Diagnostics through Dual-Channel Sensor Measurements: Development of a Baseline System. Berlin, Germany. NASA\/TM-2008-215228.","DOI":"10.1115\/GT2008-50345"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kobayashi, T., and Simon, D.L. (2008, January 9\u201313). Aircraft Engine On-Line Diagnostics through Dual-Channel Sensor Measurements: Development of an Enhanced System. Berlin, Germany. NASA\/TM-2008-215229.","DOI":"10.1115\/GT2008-50346"},{"key":"ref_26","first-page":"1856","article-title":"Research on sensor fault diagnosis of aero-engine based on data fusion of SPSO-SVR","volume":"24","author":"Lu","year":"2009","journal-title":"J. Aerosp. Power."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tang, L., Decastro, J.A., Zhang, X., Ramos, L.F., and Simon, D.L. (2010, January 14\u201318). A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults. Glasgow, UK. NASA\/TM-2010-216360.","DOI":"10.1115\/GT2010-22642"},{"key":"ref_28","first-page":"2592","article-title":"Application of relevance vector machine and survival probability to machine degradation assessment","volume":"28","author":"Widodo","year":"2010","journal-title":"Expert Syst. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/8\/11061\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:51:42Z","timestamp":1760219502000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/8\/11061"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,8,9]]},"references-count":28,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2012,8]]}},"alternative-id":["s120811061"],"URL":"https:\/\/doi.org\/10.3390\/s120811061","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,8,9]]}}}