The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics
Open accessAbstract
In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The state covariance matrix estimated from each filter can be compared for FDI analysis of sensors [52,53].”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [52]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy
Frank, P. M. (1990) · Automatica · doi:10.1016/0005-1098(90)90018-d · Cited by 3,545
Volponi, A. J., DePold, H. R., Ganguli, R., & Daguang, C. (2003) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.1419016 · Cited by 202
Sensor/actuator failure detection in the Vista F-16 by multiple model adaptive estimation
Menke, T. E., & Maybeck, P. S. (1995) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/7.464346 · Cited by 182
Robust fault detection of jet engine sensor systems using eigenstructure assignment
Patton, R. J., & Chen, J. (1992) · Journal of Guidance Control and Dynamics · doi:10.2514/3.11413 · Cited by 172
Estimating in-flight engine performance variations using Kalman filter concepts
Luppold, R., ROMAN, J., Gallops, G. W., & Kerr, L. J. (1989) · AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit · doi:10.2514/6.1989-2584 · Cited by 156
Hybrid Neural-Network Genetic-Algorithm Technique for Aircraft Engine Performance Diagnostics
Kobayashi, T., & Simon, D. L. (2005) · Journal of Propulsion and Power · doi:10.2514/1.9881 · Cited by 109
Self-citationModeling and Analysis of Gas Turbine Performance Deterioration
Lakshminarasimha, A. N., Boyce, M. P., & Meher-Homji, C. B. (1994) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.2906808 · Cited by 105
An Evaluation of Engine Faults Diagnostics Using Artificial Neural Networks
Lu, P.-J., Zhang, M.-C., Hsu, T.-C., & Zhang, J. (2001) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.1362667 · Cited by 91
Advanced detection, isolation, and accommodation of sensor failures - Real-time evaluation
Merrill, W. C., DeLaat, J. C., & Bruton, W. M. (1988) · Journal of Guidance Control and Dynamics · doi:10.2514/3.20348 · Cited by 82
Fault Detection and Diagnosis in Gas Turbines
Merrington, G. L., Kwon, O.-K., Goodwin, G. C., & Carlsson, B. (1991) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.2906559 · Cited by 43
Cited by
Sorted by citations
Tahan, M., Tsoutsanis, E., Muhammad, M. B., & Karim, Z. A. A. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.04.048 · Cited by 485
Gas Turbine Engine Health Management: Past, Present, and Future Trends
Volponi, A. J. (2013) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.4026126 · Cited by 357
Pourbabaee, B., Meskin, N., & Khorasani, K. (2015) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2015.2480003 · Cited by 236
Dynamic neural network-based fault diagnosis of gas turbine engines
Tayarani-Bathaie, S. S., Vanini, Z. N. S., & Khorasani, K. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.06.050 · Cited by 226
Propulsion and power for 21st century aviation
Sehra, A. K., & Whitlow, W. (2004) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2004.06.003 · Cited by 188
A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems
Litt, J. S., Simon, D. L., Garg, S., Guo, T.-H., Mercer, C. R., Millar, R. C., Behbahani, A., Bajwa, A. R., & Jensen, D. T. (2004) · Journal of Aerospace Computing Information and Communication · doi:10.2514/1.13048 · Cited by 174
Self-citationA Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities
Fentaye, A. D., Baheta, A. T., Gilani, S. I. U.-H., & Kyprianidis, K. (2019) · Aerospace · doi:10.3390/aerospace6070083 · Cited by 176
Vanini, Z. N. S., Khorasani, K., & Meskin, N. (2013) · Information Sciences · doi:10.1016/j.ins.2013.05.032 · Cited by 155
Amozegar, M., & Khorasani, K. (2016) · Neural Networks · doi:10.1016/j.neunet.2016.01.003 · Cited by 150
Extreme learning machine based transfer learning for aero engine fault diagnosis
Zhao, Y.-P., & Chen, Y.-B. (2021) · Aerospace Science and Technology · doi:10.1016/j.ast.2021.107311 · Cited by 134
Fault detection and isolation of gas turbine engines using a bank of neural networks
Tayarani-Bathaie, S. S., & Khorasani, K. (2015) · Journal of Process Control · doi:10.1016/j.jprocont.2015.08.007 · Cited by 127
A Multiple Model-Based Approach for Fault Diagnosis of Jet Engines
Meskin, N., Naderi, E., & Khorasani, K. (2012) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2011.2177981 · Cited by 115
Fault Diagnosis and Fault-Tolerant Control Strategy for the Aerosonde UAV
Bateman, F., Noura, H. N., & Ouladsine, M. (2011) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2011.5937287 · Cited by 115
Lee, H., Li, G., Rai, A., & Chattopadhyay, A. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101071 · Cited by 109
Kiakojoori, S., & Khorasani, K. (2015) · Neural Computing and Applications · doi:10.1007/s00521-015-1990-0 · Cited by 101
Kobayashi, T., & Simon, D. L. (2005) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.1850505 · Cited by 88
Self-citationNonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach
Naderi, E., Meskin, N., & Khorasani, K. (2011) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.4004152 · Cited by 87
Smart Video Surveillance System Based on Edge Computing
Cob-Parro, A. C., Losada-Gutiérrez, C., Marrón-Romera, M., Gardel-Vicente, A., & Bravo-Muñoz, I. (2021) · Sensors · doi:10.3390/s21092958 · Cited by 84
INS/Odometer Land Navigation by Accurate Measurement Modeling and Multiple-Model Adaptive Estimation
Ouyang, W., Wu, Y., & Chen, H. (2020) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2020.3011998 · Cited by 77
Hybrid Kalman Filter Approach for Aircraft Engine In-Flight Diagnostics: Sensor Fault Detection Case
Kobayashi, T., & Simon, D. L. (2006) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.2718572 · Cited by 77
Self-citationA Fault Diagnosis Method for Industrial Gas Turbines Using Bayesian Data Analysis
Lee, Y. K., Mavris, D. N., Volovoi, V. V., Yuan, M., & Fisher, T. (2010) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.3204508 · Cited by 75
Zhao, Y.-P., Jian-jun, W., Li, X., Peng, G., & Yang, Z. (2019) · ISA Transactions · doi:10.1016/j.isatra.2019.08.036 · Cited by 72
Data-Driven Fault Detection in Aircraft Engines With Noisy Sensor Measurements
Sarkar, S., Jin, X., & Ray, A. (2011) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.4002877 · Cited by 71
Sequential Bayesian Prediction in the Presence of Changepoints and Faults
Garnett, R., Osborne, M. A., Reece, S., Rogers, A., & Roberts, S. (2010) · The Computer Journal · doi:10.1093/comjnl/bxq003 · Cited by 66
Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks
Lo, C., Lynch, J. P., & Liu, M. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.05.011 · Cited by 64
Xi, P.-P., Zhao, Y.-P., Wang, P., Li, Z., Pan, Y.-T., & Song, F.-Q. (2018) · Aerospace Science and Technology · doi:10.1016/j.ast.2018.08.042 · Cited by 66
Fuel usage data analysis for efficient shipping operations
Trodden, D., Murphy, A. J., Pazouki, K., & Sargeant, J. (2015) · Ocean Engineering · doi:10.1016/j.oceaneng.2015.09.028 · Cited by 62
An Integrated Fault Diagnostics Model Using Genetic Algorithm and Neural Networks
Sampath, S., & Singh, R. (2004) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.1995771 · Cited by 61
Diagnostic Methods for an Aircraft Engine Performance
Ntantis, E. L., & Botsaris, P. N. (2015) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.084.10 · Cited by 57
A new dynamic radius SVDD for fault detection of aircraft engine
Zhao, Y.-P., Xie, Y.-L., & Ye, Z. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104177 · Cited by 56
A Concurrent Diagnosis Method of IGBT Open-Circuit Faults in Modular Multilevel Converters
Jia, H., Deng, Y., Hu, X., Deng, Z., & He, X. (2022) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2022.3208647 · Cited by 50
Shi, C., Yang, G., & Li, X. (2017) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2017.2743261 · Cited by 50
Fault detection and isolation in aircraft gas turbine engines. Part 1: Underlying concept
Gupta, S. K., Ray, A., Sarkar, S., & Yasar, M. (2008) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1243/09544100jaero311 · Cited by 49
Li, X., Shi, C., & Yang, G. (2017) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2017.2688581 · Cited by 49
Soft extreme learning machine for fault detection of aircraft engine
Zhao, Y.-P., Huang, G., Hu, Q., Tan, J., Wang, J., & Yang, Z. (2019) · Aerospace Science and Technology · doi:10.1016/j.ast.2019.05.021 · Cited by 44
An Optimal Orthogonal Decomposition Method for Kalman Filter-Based Turbofan Engine Thrust Estimation
Litt, J. S. (2007) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.2747254 · Cited by 43
An Optimal Orthogonal Decomposition Method for Kalman Filter-Based Turbofan Engine Thrust Estimation
Litt, J. S. (2005) · doi:10.1115/gt2005-68808 · Cited by 43
Kobayashi, T., & Simon, D. L. (2004) · doi:10.1115/gt2004-53640 · Cited by 43
Self-citationRaouf, I., Khan, A., Khalid, S., Sohail, M., Azad, M. M., & Kim, H. S. (2022) · Mathematics · doi:10.3390/math10183233 · Cited by 46
Fusing unscented Kalman filter for performance monitoring and fault accommodation in gas turbine
Lü, F., Wang, Y., Huang, J., Huang, Y., & Qiu, X. (2016) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1177/0954410016682269 · Cited by 41
Distributed Reference-Free Fault Detection Method for Autonomous Wireless Sensor Networks
Lo, C., Lynch, J. P., & Liu, M. (2013) · IEEE Sensors Journal · doi:10.1109/jsen.2013.2244881 · Cited by 40
Tatsis, K., Agathos, K., Chatzi, E., & Dertimanis, V. K. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108558 · Cited by 41
Pourbabaee, B., Meskin, N., & Khorasani, K. (2013) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2013.6580567 · Cited by 39
Lai, Z., Ying Lei, A., Zhu, S., Xu, Y. L., Zhang, X., & Krishnaswamy, S. (2016) · Measurement · doi:10.1016/j.measurement.2016.04.016 · Cited by 39
Ding, B., & Fang, H. (2017) · ISA Transactions · doi:10.1016/j.isatra.2017.03.018 · Cited by 38
Hyperelliptic Kalman filter-based aeroengine sensor fault FDIA system under multi-source uncertainty
Sun, R., Han, X., Chen, Y., & Gou, L. (2022) · Aerospace Science and Technology · doi:10.1016/j.ast.2022.108058 · Cited by 37
Efficient Sensor Fault Detection Using Combinatorial Group Testing
Lo, C., Liu, M., Lynch, J. P., & Gilbert, A. C. (2013) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss.2013.57 · Cited by 37
Giorgi, M. G. D., Menga, N., & Ficarella, A. (2023) · Energies · doi:10.3390/en16062711 · Cited by 40
Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics
Dewallef, P., Romessis, C., Léonard, O., & Mathioudakis, K. (2004) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.2056507 · Cited by 36
Yang, Q., Li, S., & Cao, Y. (2019) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-019-0346-6 · Cited by 34
Empirical Tuning of an On-Board Gas Turbine Engine Model for Real-Time Module Performance Estimation
Volponi, A., Brotherton, T., & Luppold, R. (2008) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.2799527 · Cited by 34
Horowitz, B., & Pierce, K. M. (2013) · Systems Engineering · doi:10.1002/sys.21239 · Cited by 33
Aircraft engine health management via stochastic modelling of flight data interrelations
Dimogianopoulos, D. G., Hios, J. D., & Fassois, S. D. (2011) · Aerospace Science and Technology · doi:10.1016/j.ast.2011.03.002 · Cited by 33
Zhao, Y.-P., Song, F.-Q., Pan, Y.-T., & Li, B. (2017) · Aerospace Science and Technology · doi:10.1016/j.ast.2017.10.004 · Cited by 32
Gas Path Fault and Degradation Modelling in Twin-Shaft Gas Turbines
Cruz-Manzo, S., Panov, V., & Zhang, Y. (2018) · Machines · doi:10.3390/machines6040043 · Cited by 33
Litt, J. S., Turso, J. A., Shah, N., Sowers, T. S., & Owen, A. K. (2005) · AIAA Infotech@Aerospace Conference · doi:10.2514/6.2005-6905 · Cited by 30
Fault Tolerant Control and Fault Detection and Isolation
Alwi, H., Edwards, C., & Tan, C. P. (2011) · Advances in industrial control · doi:10.1007/978-0-85729-650-4_2 · Cited by 30
Auto-Tuning of Real-Time Dynamic Gas Turbine Models
Panov, V. (2014) · Volume 6: Ceramics; Controls, Diagnostics and Instrumentation; Education; Manufacturing Materials and Metallurgy · doi:10.1115/gt2014-25606 · Cited by 29
Actuators Fault Diagnosis and Tolerant Control for an Unmanned Aerial Vehicle
Bateman, F., Noura, H. N., & Ouladsine, M. (2007) · The proceedings of the IEEE Conference on Control Applications/The proceedings of the ... IEEE Conference on Control Applications · doi:10.1109/cca.2007.4389374 · Cited by 29
A strong tracking filter based multiple model approach for gas turbine fault diagnosis
Yang, Q., Li, S., & Cao, Y. (2018) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-017-1248-0 · Cited by 27
Xue, W., Guo, Y., & Zhang, X. (2007) · International Conference on Innovative Computing, Information and Control · doi:10.1109/icicic.2007.3 · Cited by 26
Alsuwian, T. M., Riaz, U., Amin, A. A., Qadir, M. B., Almasabi, S., & Jalalah, M. (2022) · Energies · doi:10.3390/en15197010 · Cited by 24
Adaptive Sensor Fault Detection and Isolation using Unscented Kalman Filter for Vehicle Positioning
Mori, D., Sugiura, H., & Hattori, Y. (2019) · 2019 IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2019.8917208 · Cited by 23
Active Data Selection for Sensor Networks with Faults and Changepoints
Osborne, M. A., Garnett, R., & Roberts, S. (2010) · International Conference on Advanced Information Networking and Applications (AINA) · doi:10.1109/aina.2010.36 · Cited by 23
Towards In-Flight Detection and Accommodation of Faults in Aircraft Engines
Rausch, R. T., Viassolo, D., Kumar, A., Goebel, K., Eklund, N. H. W., Brunell, B. J., & Bonanni, P. (2004) · AIAA Intelligent Systems Technical Conference · doi:10.2514/6.2004-6463 · Cited by 23
On-Line Aircraft Engine Diagnostic Using a Soft-Constrained Kalman Filter
Dewallef, P., ́onard, O. L., & Mathioudakis, K. (2004) · doi:10.1115/gt2004-53539 · Cited by 23
Artificial-Immune-System-Based Detection Scheme for Aircraft Engine Failures
Perhinschi, M. G., Porter, J., Moncayo, H., Davis, J. K., & Wayne, W. S. (2011) · Journal of Guidance Control and Dynamics · doi:10.2514/1.52746 · Cited by 22
Multi Kalman Filtering Approach for Estimation of Wheel-Rail Contact Conditions
Hussain, I., & Mei, T. X. (2010) · UKACC International Conference on Control · doi:10.1049/ic.2010.0326 · Cited by 20
Gas Turbine Modeling for Diagnosis and Control
Larsson, E., Åslund, J., Frisk, E., & Eriksson, L. (2014) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.4026598 · Cited by 20
Integrated in-Flight Fault Detection and Accommodation: A Model-Based Study
Rausch, R. T., Goebel, K., Eklund, N. H. W., & Brunell, B. J. (2007) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.2720517 · Cited by 20
Tu, W., & Dong, J. (2023) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2023.02.002 · Cited by 19
Performance Diagnosis for Turbojet Engines Based on Flight Data
Chang, R. C. (2012) · Journal of Aerospace Engineering · doi:10.1061/(asce)as.1943-5525.0000210 · Cited by 19
Han, D.-J. (2018) · International Journal of Aeronautical and Space Sciences · doi:10.1007/s42405-018-0032-4 · Cited by 19
A Sensor-Fault-Tolerant Diagnosis Tool Based on a Quadratic Programming Approach
Borguet, S., & Léonard, O. (2008) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.2772637 · Cited by 18
Zhao, Y.-P., & Cai, W. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107249 · Cited by 17
MODEL-BASED CONTROL AND DIAGNOSTIC TECHNIQUES FOR OPERATIONAL IMPROVEMENTS OF GAS TURBINE ENGINES
Panov, V. (2013) · Lincoln Repository (University of Lincoln) · doi:10.13140/rg.2.1.3041.3925 · Cited by 17
Shi, C., & Yang, G. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2845685 · Cited by 17
Fault detection of gas turbine engines using dynamic neural networks
Tayarani-Bathaie, S. S., Vanini, Z. N. S., & Khorasani, K. (2012) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2012.6334837 · Cited by 17
Hybrid Kalman Filter: A New Approach for Aircraft Engine In-Flight Diagnostics
Simon, D. L., & Kobayashi, T. (2013) · NASA STI Repository (National Aeronautics and Space Administration) · Cited by 17
Self-citationFDIA System for Sensors of the Aero-Engine Control System Based on the Immune Fusion Kalman Filter
Gou, L., Sun, R., & Han, X. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/6662425 · Cited by 16
Aircraft engine sensor fault diagnostics using an on-line OBEM update method
Liu, X., Xue, N., & Yuan, Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171037 · Cited by 16
Anomaly detection of hot components in gas turbine based on frequent pattern extraction
Liu, J., Zhu, L., Ma, Y., Liu, J., Zhou, W., & Yu, D. (2017) · Science China Technological Sciences · doi:10.1007/s11431-017-9165-7 · Cited by 16
Model Based Diagnosis and Supervision of Industrial Gas Turbines
Larsson, E. (2014) · Linköping University Electronic Press eBooks · doi:10.3384/diss.diva-106256 · Cited by 16
Bateman, F., Noura, H. N., & Ouladsine, M. (2008) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med.2008.4602147 · Cited by 16
Sequential Bayesian Inference for Uncertain Nonlinear Dynamic Systems: A Tutorial.
Tatsis, K., Dertimanis, V. K., & Chatzi, E. (2022) · Journal of Structural Dynamics · doi:10.25518/2684-6500.107 · Cited by 20
Using long-term condition monitoring data with non-Gaussian noise for online diagnostics
Shiri, H., Zimroz, P., Wodecki, J., Wyłomańska, A., Zimroz, R., & Szabat, K. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110472 · Cited by 15
A Foreign Object Damage Event Detector Data Fusion System for Turbofan Engines
Turso, J. A., & Litt, J. S. (2005) · Journal of Aerospace Computing Information and Communication · doi:10.2514/1.12348 · Cited by 15
Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics
Dewallef, P., Romessis, C., ́onard, O. L., & Mathioudakis, K. (2004) · doi:10.1115/gt2004-53541 · Cited by 15
Feng, J., Turksoy, K., Samadi, S., Hajizadeh, I., & Çınar, A. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.07.277 · Cited by 14
A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults
Tang, L., Zhang, X., DeCastro, J., Farfan-Ramos, L., & Simon, D. L. (2010) · Volume 3: Controls, Diagnostics and Instrumentation; Cycle Innovations; Marine · doi:10.1115/gt2010-22642 · Cited by 14
Self-citationAircraft Fault Detection Using Real-Time Frequency Response Estimation
Grauer, J. A. (2016) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2016-0372 · Cited by 14
Chen, Y.-Z., Zhang, W., Zhiwen, Z. H. A. O., Tsoutsanis, E., MALKOGIANNI, A., Ma, Y., & Gou, L. (2024) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2024.09.019 · Cited by 13
Model-based fault detection and diagnosis in ALMA subsystems
Ortiz, J. L., & Carrasco, R. A. (2016) · doi:10.1117/12.2233204 · Cited by 13
A fault tolerant model for multi-sensor measurement
Li, L., & Shi, W. (2015) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2015.04.021 · Cited by 13
Health Monitoring in an Industrial Gas Turbine Application by Using Model Based Diagnosis Techniques
Larsson, E., Åslund, J., Frisk, E., & Eriksson, L. (2011) · doi:10.1115/gt2011-46825 · Cited by 12
Calderano, P., Ribeiro, M. G. D. C., Amaral, R. P. F., Vellasco, M. M. B. R., Tanscheit, R., & de Aguiar, E. P. (2019) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-019-1567-4 · Cited by 12
Ng, K. Y., Frisk, E., Krysander, M., & Eriksson, L. (2020) · IEEE Control Systems · doi:10.1109/mcs.2019.2961793 · Cited by 12
Fault Isolation for an Industrial Gas Turbine With a Model-Based Diagnosis Approach
Larsson, E., Åslund, J., Frisk, E., & Eriksson, L. (2010) · Volume 3: Controls, Diagnostics and Instrumentation; Cycle Innovations; Marine · doi:10.1115/gt2010-22511 · Cited by 12
Robust Estimation and Fault Diagnostics for Aircraft Engines with Uncertain Model Data
Yedavalli, R. K. (2007) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2007.4283156 · Cited by 12
Huang, Q. Y., & Bhattacharyya, D. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.106985 · Cited by 12
Metadata
- Authors
- Kobayashi, T., & Simon, D. L.
- Affiliations
- QSS Group, Inc., Cleveland, OH
- DEVCOM Army Research Laboratory (US)
- Venue
- Volume 1: Turbo Expo 2003
- Publisher
- ASMEDC
- Published
- 2003-01-01
- Pages
- 461-470
- OpenAlex
- W2151462123
- Cited by
- 198
- References
- 17
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 5 · 2023: 9 · 2022: 13 · 2021: 8 · 2020: 12 · 2019: 9 · 2018: 11 · 2017: 14 · 2016: 12 · 2015: 12 · 2014: 7 · 2013: 13 · 2012: 6
- FWCI
- 8.87
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- NASA Technical Reports Server (NASA) (submittedVersion)
- Defense Technical Information Center (DTIC) (submittedVersion)
- Funders
- National Aeronautics and Space Administration
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Engineering and Test Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Aerospace Engineering and Applications
- Keywords
- Kalman filter bank, fault detection and isolation, aircraft gas turbine engines, sensor faults, actuator faults
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review