Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

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

The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study

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

Modeling 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

Fault diagnosis based on relevance vector machine for fuel regulator of aircraft engine

Zhou, J., Liu, Y., & Zhang, T. (2018) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-018-0855-0 · Cited by 12

Modeling, Diagnostics and Prognostics of a Two-Spool Turbofan Engine

Yedavalli, R. K., Shankar, P., Siddiqi, M., & Behbahani, A. (2005) · AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit (JPC) · doi:10.2514/6.2005-4344 · Cited by 12

On-Line Transient Engine Diagnostics in a Kalman Filtering Framework

Borguet, S., Dewallef, P., & ́onard, O. L. (2005) · doi:10.1115/gt2005-68013 · Cited by 11

Integrated In-Flight Fault Detection and Accommodation: A Model-Based Study

Rausch, R. T., Goebel, K., Eklund, N. H. W., & Brunell, B. J. (2005) · doi:10.1115/gt2005-68300 · Cited by 11

Rapid detection of faults for safety critical aircraft operation

Goebel, K., Eklund, N. H. W., & Brunell, B. J. (2005) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2004.1368144 · Cited by 11

ISHM-oriented adaptive fault diagnostics for avionics based on a distributed intelligent agent system

Xu, J., Zhong, Z., & Xu, L. (2014) · International Journal of Systems Science · doi:10.1080/00207721.2014.971090 · Cited by 10

Fault diagnosis of gas turbine engines by using dynamic neural networks

Mohammadi, R., Naderi, E., Khorasani, K., & Hashtrudi-Zad, S. (2011) · IEEE International Conference on Quality and Reliability · doi:10.1109/icqr.2011.6031675 · Cited by 10

A fault-tolerant control approach for aircraft engine using a bank of LMI-based UIO filters

He, A., Tan, D., & Wang, X. (2011) · International Conference on Electronics, Communications and Control · doi:10.1109/icecc.2011.6066717 · Cited by 10

A Hybrid Multimodel-Based Condition Monitoring and Sensor Fault Detection Method for Aero Gas Turbine

Chen, X., Tong, Z., Liu, Y., Wang, Y., & Qing, X. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3450859 · Cited by 9

Particle filter based self sensing Shape Memory Alloy wire actuator under external cooling

Mohan, S., & Banerjee, A. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109779 · Cited by 9

An actuator fault detection, isolation and estimation system for an UAV using input observers

Bateman, F., Noura, H. N., & Ouladsine, M. (2007) · European Control Conference (ECC) · doi:10.23919/ecc.2007.7068717 · Cited by 9

Diagnosis of gas path health states on a real flight data by convolutional neural network and dual-tree complex wavelet

Han, Z., Zhu, J., Xiong, Y., & Wang, Y. (2024) · Measurement · doi:10.1016/j.measurement.2024.115792 · Cited by 8

Active Fault Diagnosis and Major Actuator Failure Accommodation: Application to a UAV

Bateman, F., Noura, H. N., & Ouladsine, M. (2011) · InTech eBooks · doi:10.5772/15207 · Cited by 8

Fault diagnosis of gas turbine engines by using dynamic neural networks

Mohammadi, R., Naderi, E., Khorasani, K., & Hashtrudi-Zad, S. (2011) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas.2011.6026604 · Cited by 8

Distributed Fault Diagnosis of Nonlinear Stochastic Systems With Monitoring Networks

Raptis, I. A., & Noursadeghi, E. (2020) · IEEE Access · doi:10.1109/access.2020.3011725 · Cited by 8

FieldMAP: A Spatiotemporal Field Monitoring Application Prototyping Framework

Brusey, J., Gaura, E. I., Goldsmith, D., & Shuttleworth, J. (2009) · IEEE Sensors Journal · doi:10.1109/jsen.2009.2021799 · Cited by 8

Diagnosis of engine sensor, actuator and component faults using a bank of adaptive nonlinear estimators

Tang, L., Zhang, X., & DeCastro, J. (2011) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2011.5747565 · Cited by 7

Diagnosis and Supervision of Industrial Gas Turbines

Larsson, E. (2012) · Cited by 7

Decision fusion software system for turbine engine fault diagnostics

Al-Salah, T. H., Zein-Sabatto, S., & Bodruzzaman, M. (2012) · IEEE SoutheastCon · doi:10.1109/secon.2012.6197000 · Cited by 7

SR-30 turbojet engine real-time sensor health monitoring using neural networks, and Bayesian belief networks

Nott, C. R., Olcmen, S. M., Karr, C. L., & Trevino, L. C. (2006) · Applied Intelligence · doi:10.1007/s10489-006-0017-z · Cited by 7

Hybrid Online Multi-Sensor Error Detection and Functional Redundancy for Artificial Pancreas Control Systems

Feng, J., Hajizadeh, I., Samadi, S., Sevil, M., Hobbs, N., Brandt, R., Lazaro, C., Maloney, Z., Yu, X., Littlejohn, E., et al. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.09.289 · Cited by 7

Optimizing coupled fault diagnosis in liquid rocket engines via distributed robust multi-model Kalman filter

Zhang, Z., Zhang, X., Chen, H., Li, Y., & Chen, Z. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad98ad · Cited by 6

Virtual Sensor Development for Actual Sensor Fault Detection and Flight Parameter Estimation in Real Time

Zahed, M. J. H., Alabsi, M. I., Fields, T. D., & Hetrick, D. (2020) · AIAA SciTech Forum · doi:10.2514/6.2020-0510 · Cited by 6

The Test Verification Design Method Based on Rapid Prototyping Technology of Aero-engine

Qiu, X., Zheng, W.-H., Tang, Y., & Lü, F. (2015) · Procedia Engineering · doi:10.1016/j.proeng.2014.12.631 · Cited by 6

Fault Sensor Isolation Method for Liquid Rocket Engines Based on Multi-Model

Sun, R.-B., Su, Y., Chen, H., Zhang, H., Chen, Z., & Chen, X. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.112278 · Cited by 5

A first approach on fault detection and isolation for cardiovascular anomalies detection

Ledezma, F. D., & Laleg‐Kirati, T. (2015) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2015.7172246 · Cited by 5

In-the-Loop Simulation Experiment of Aero-Engine Fault-Tolerant Control Technology

Zhang, M., Huang, X., Wang, S., & Luo, L. (2022) · Applied Sciences · doi:10.3390/app12031716 · Cited by 5

Sensor and actuator bias estimation using multi model approach

Manimozhi, M., & Saravanakumar, R. (2016) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2016.11.003 · Cited by 5

Health Monitoring Framework for Aircraft Engine System using Deep Neural Network

Lee, H., Li, G., Rai, A., & Chattopadhyay, A. (2019) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2019.v11i1.869 · Cited by 5

Imbalanced Kernel Extreme Learning Machines for Fault Detection of Aircraft Engine

Zhao, Y.-P., Chen, Y.-B., Hao, Z., Wang, H., Yang, Z., & Tan, J. (2020) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4047117 · Cited by 6

Aeroengine Actuator Fault Detection and Estimation Via Combined Model Observers

Seiari, S. A., Cen, Z., Youssef, H., & Tsoutsanis, E. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.07.204 · Cited by 4

Application of Input and State Multiple Model Adaptive Estimator for Aircraft Airspeed Approximation

Bauer, P., Baár, T., Péni, T., Vanek, B., & Bokor, J. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.09.014 · Cited by 4

Multiple-Model Adaptive Estimation for Measurements with Unknown Time Delay

Lee, K., & Johnson, E. N. (2017) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2017-1260 · Cited by 4

Aircraft Turbine Engine Automatic Control Based on Adaptive Dynamic Models

Yepifanov, S. V. (2020) · Transactions of the Institute of Aviation · doi:10.2478/tar-2020-0021 · Cited by 4

Kalman-Filtering Based Algorithm for Sensor's Channel Fault Detection and Isolation

Кузнецова, Т. А. (2018) · International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) · doi:10.1109/fareastcon.2018.8602944 · Cited by 4

Multi-Sensor Data Fusion for Smart Home Reliable Pedestrian Localization

Achaji, L., Daher, M., Najjar, M. E. B. E., & Charpillet, F. (2021) · IEEE International Multidisciplinary Conference on Engineering Technology (IMCET) · doi:10.1109/imcet53404.2021.9665594 · Cited by 4

Onboard engine FDI in autonomous aircrafts using compact stochastic nonlinear modelling of flight signal dependencies

Dimogianopoulos, D. G., Hios, J. D., & Fassois, S. D. (2007) · European Control Conference (ECC) · doi:10.23919/ecc.2007.7068618 · Cited by 4

Development of a Detection Scheme for Aircraft Engine Failures Based on the Artificial Immune System Paradigm

Perhinschi, M. G., Porter, J., Moncayo, H., Davis, J. K., & Wayne, W. S. (2010) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2010-7740 · Cited by 4

Multiple Sensors Soft-Failure Diagnosis Based on Kalman Filter

Xu, X., & Wang, X. (2010) · Signal & Image Processing An International Journal · doi:10.5121/sipij.2010.1211 · Cited by 4

Robust Sensor Fault Reconstruction via a Bank of Second-Order Sliding Mode Observers for Aircraft Engines

Qiang, Z., Huang, J., Lü, F., & Chang, X. (2019) · Energies · doi:10.3390/en12142831 · Cited by 4

INS/Odometer Land Navigation by Accurate Measurement Modeling

Ou, Y., Wu, Y., & Chen, H. (2020) · Saint Petersburg International Conference on Integrated Navigation Systems · doi:10.23919/icins43215.2020.9133996 · Cited by 4

Aircraft Propulsion Health Management

Volponi, A., & Wood, B. (2011) · doi:10.1002/9781119994053.ch24 · Cited by 4

Optimal Robust Matching of Engine Models to Test Data

Mavris, D. N., & Denney, R. K. (2009) · doi:10.21236/ada498300 · Cited by 4

A novel approach to self-adaptive digital twin performance model of turbofan engines for in-flight application

Kim, S. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.110620 · Cited by 3

Interpretable Unsupervised Feature Extraction and Learning of Abnormal System State Transitions in Aircraft Sensor Data

Sundareswara, R., Betz, F. D., & Lu, T.-C. (2018) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2018.v10i1.463 · Cited by 3

A Reduced-Order Modeling Framework for Simulating Signatures of Faults in a Bladed Disk

Singh, D. S., Agrawal, A. K., & Mahapatra, D. R. (2022) · SAE International Journal of Aerospace · doi:10.4271/01-16-01-0006 · Cited by 3

Fault Diagnosis of Gas Turbine Engines by Using MultipleModel Approach

Abbasfard, Z. Z. (2013) · Cited by 3

Multiple Model-Based Hybrid Kalman Filter for Fault Diagnosis of Jet Engines

Fang, X.-J., Ren, X., Zhou, X., & Xing, X. (2020) · International Conference on Mechanical and Aerospace Engineering · doi:10.1109/icmae50897.2020.9178872 · Cited by 3

Efficient Sensor Fault Detection Using Group TestingPreprint

Lo, C., Bai, Y., Liu, M., & Lynch, J. P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.04152 · Cited by 3

Hard-Failure Diagnosis Using Self-tuning Kalman Filter

Xu, X., & Wang, X. (2010) · Communications in computer and information science · doi:10.1007/978-3-642-18129-0_52 · Cited by 2

Toward High Integrity Personal Localization System Based on Informational Formalism

Daher, M., Hage, J. A., Najjar, M. E. B. E., Diab, A., Khalil, M. A., & Charpillet, F. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2886976 · Cited by 2

Comparing residual evaluation methods for leak detection on an aircraft fuel system test-rig

Bennett, P., Dixon, R., & Pearson, J. P. (2008) · University of Birmingham Research Portal (University of Birmingham) · Cited by 2

Fault Tolerant Supervision of an Industrial Gas Turbine

Larsson, E., Åslund, J., Frisk, E., & Eriksson, L. (2013) · doi:10.1115/gt2013-95727 · Cited by 2

A quadratic programming framework for constrained and robust jet engine health monitoring

Borguet, S., & Léonard, O. (2009) · Progress in Propulsion Physics · doi:10.1051/eucass/200901669 · Cited by 2

A multiple model approach to fault detection and localization in multi-terminal HVDC systems

Hage, A. A. A., Piepenbreier, B., Buchstaller, D., & Engel, M. (2015) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2015.7392442 · Cited by 2

Smoothed multiple model adaptive estimation

Baár, T., Beke, B., Bauer, P., Vanek, B., & Bokor, J. (2016) · European Control Conference (ECC) · doi:10.1109/ecc.2016.7810442 · Cited by 2

A Survey on the Health Management Technology for Aircraft Gas Turbine Engine

Park, I., Kim, J., & Min, S. (2017) · Journal of the Korean Society of Propulsion Engineers · doi:10.6108/kspe.2017.21.5.108 · Cited by 2

Fault Diagnostics

Xu, J., & Xu, L. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812207-5.00006-7 · Cited by 2

Concurrent Fault Diagnosis Based on an Extended Kalman Filter

Lizarraga, A., Begovich, O., & Ramírez‐Treviño, A. (2021) · International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) · doi:10.1109/cce53527.2021.9633051 · Cited by 2

Dynamic Threshold Design Based on Kalman Filter in Multiple Fault Diagnosis

Gou, L., Zhou, Z., Liang, A., Wang, L., & Liu, Z. (2018) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2018.8483301 · Cited by 2

Security Engineering Project

Horowitz, B. (2012) · doi:10.21236/ada582703 · Cited by 2

A Particle-Filtering Based Approach for Distributed Fault Diagnosis of Large-Scale Interconnected Nonlinear SystemsPreprint

Noursadeghi, E., & Raptis, I. A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.01130 · Cited by 2

Application of Sparse Theory in Turbofan Engine Gas Path Abrupt Fault Diagnosis

Jie-tang, Z., Yu, H., Wei, Z., Gan, T., & Chen, J. (2018) · International Conference on Intelligent Computation Technology and Automation · doi:10.1109/icicta.2018.00093 · Cited by 1

Sensor Fusion for Power Line Sensitive Monitoring and Load State Estimation

Schimmack, M., Belda, K., & Mercorelli, P. (2023) · Sensors · doi:10.3390/s23167173 · Cited by 1

Design of Fault Tolerant Control Laws for Jet Engines

Sneha, R. F., & Mija, S. J. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.367.96 · Cited by 1

Propulsion performance diagnostics and optimization based on the estimation of measurement biases and hybrid optimization

Zhang, J., Tang, H., Shen, G. Z., & Yang, L. (2011) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1177/0954410010396278 · Cited by 1

Evaluation of multiple model adaptive estimation of aircraft airspeed in close to real conditions

Baár, T., Bauer, P., Szabó, Z., Vanek, B., & Bokor, J. (2017) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med.2017.7984130 · Cited by 1

On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine

Singh, R., Maity, A., Somani, B., & Nataraj, P. S. V. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.03.026 · Cited by 1

A Method for Comprehensive Diagnosis of Sensors of Flight Control System Using Analytical Redundancy

Chi, C., Zhang, W., Jiang-le, Z., & Liu, X. (2010) · International Conference on Electrical and Control Engineering · doi:10.1109/icece.2010.1183 · Cited by 1

A negative selection algorithm applied to helicopter actuator fault detection

Rakotomamonjy, T., & Boulanger, M. L. (2014) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit.2014.6996912 · Cited by 1

Алгоритми діагностики та парирування відмов каналів вимірювання системи автоматичного управління авіаційного двигуна ТВ3-117 у польотних режимах на основі нейромережевих технологій

Ігорович, В. С. (2020) · Proceedings of National Aviation University · doi:10.18372/2306-1472.84.14950 · Cited by 1

Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics

Stenfelt, M., & Kyprianidis, K. (2022) · Machines · doi:10.3390/machines10010036 · Cited by 1

Monitoring of Aircraft Engines

Kadirkamanathan, V., & Fleming, P. J. (2008) · Encyclopedia of Structural Health Monitoring · doi:10.1002/9780470061626.shm139 · Cited by 1

Untersuchungen zum Einfluss der Modellbildung auf das Trend Monitoring von Fluggasturbinen

Köhli, R. (2016) · OPUS Publication Server of the University of Stuttgart (University of Stuttgart) · doi:10.18419/opus-8726 · Cited by 1

High integrity personal tracking using fault tolerant multi-sensor data fusion

Daher, M. (2017) · doi:10.70675/88b6cb51z1d13z42dfz9f6bz760f23cf6a16 · Cited by 1

Integrated Design of Feedback Controller and UIO: An LMI Solution

Liu, Y., He, A., Wang, B., & Wang, X. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.302.723

Sensors Hard-Failure Diagnosis of Engine Control System Based Accommodation Kalman Filter

Xu, X., & Wang, X. (2010) · International Conference on E-Product E-Service and E-Entertainment · doi:10.1109/iceee.2010.5660978

Gas Turbine Engine Prognostics Using Time-Series Based Approaches

Gholamhossein, M. (2014) · Spectrum Research Repository (Concordia University)

Simulation-based Filtering Approach for In-Flight Monitoring of Control Surfaces

Kappenberger, C., & Christmann, J. (2010) · elib (German Aerospace Center)

Network-based relevant feature identification and early detection of impending faults

Sundareswara, R., Lu, T.-C., & Zhang, Y. (2016) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2016.7500901

Research Article Diagnostic Methods for an Aircraft Engine Performance

Ntantis, E. L., & Botsaris, P. N. (2015)

Efficient Sensor Fault Diagnosis in Wireless Sensor Networks

Liu, M., Kim, Y., Lee, S., Chen, S.-Y., Park, S., Kim, N., Tsang, F., Liu, D. S. H., Chan, R. W., & Ng, M. (2014)

The research on real-time fault diagnostic method of engine embedded by GA optimization and BP neural networks

Jing, Z., Xin, F., & Hongbo, F. (2017) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2017.7978170

Research on optimizing scheme of gas path sensors for turbofan engine

Lu-you, S., Yu, H., Jie-tang, Z., Qing, L., & Shi-ying, Z. (2017) · International Conference on Frontiers of Sensors Technologies (ICFST) · doi:10.1109/icfst.2017.8210486

Відмовостійкий алгоритм ідентифікації бортової математичної моделі авіаційного двигуна ТВ3- 117 в складі його системи автоматичного управління

Ігорович, В. С., Ковальський, В. С., Дятловська, В. Л., Яніцький, А. А., & Вакуленко, Р. А. (2020) · Вісник Херсонського національного технічного університету · doi:10.35546/kntu2078-4481.2020.4.1

In-the-loop Simulation Experiment of Aero-engine Fault-tolerant Control Technology

MengTian, Z., XiangHua, H., ShengChao, W., & Luo, L. (2021) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-nanjing52125.2021.9612829

Application of ROM-based methods to aeroengines health monitoring, performance, and control

Ramirez, J. R. (2022) · doi:10.20868/upm.thesis.70197

Application of Digital Twin Technology on Simulation and Optimization of Prime Movers in Energy Systems

Panov, V., & Cruz-Manzo, S. (2022) · Springer eBooks · doi:10.1007/978-3-030-72322-4_153-1

Aeroengine Multi-Fault Diagnosis Based on Hierarchical Multi-mode Filtering

Jiang, H., Ren, X., & Fang, X. (2022) · Proceedings · doi:10.33737/gpps21-tc-136

Application of Digital Twin Technology on Simulation and Optimization of Prime Movers in Energy Systems

Panov, V., & Cruz-Manzo, S. (2023) · Springer eBooks · doi:10.1007/978-3-030-97940-9_153

Conservativeness-Reduced Fault Diagnosis of Aeroengine Sensor Fault Considered Multi-Source Uncertainty

Sun, R., Han, X., Lin-Feng, G., & Chen, Y. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10239977

Generalized TSKF-Based Aeroengine Actuator Fault Estimation under Multi-source Uncertainty

Sun, R., Han, X., Chen, Y., & Lin-Feng, G. (2023) · International Conference on Mechanical and Aerospace Engineering · doi:10.1109/icmae59650.2023.10424551

DA&AI supporting tools for gas turbine’s efficiency improvement: maintenance, operation modes and performance enhancement

de Castro-Cros, M. (2023) · doi:10.5821/dissertation-2117-405571

Research on Analytical Redundancy Reconstruction of Flight Control Sensor Signals for Carrier-Based Aircrafts

Ma, J., Ma, Y., Zhou, D., & Xu, M. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-2252-8_31

Enhancing Fault Detection and Isolation in All-Electric Auxiliary Power Unit (APU) Gas Generator by Utilizing Starter/Generator Signal

Mao, H., Khorasani, K., & Guo, Y. (2025) · Aerospace · doi:10.3390/aerospace12070607

Analysis and Detection of Four Typical Arm Current Measurement Faults in MMC

Wen, Q., Song, S., Lei, J., Du, Q., & Ma, W. (2025) · Energies · doi:10.3390/en18143727

The Online Fault Diagnosis, Isolation, and Reconstruction Algorithm for Aero-Engine Sensors Based on Onboard Adaptive Model

Hou, P., Du, X., Zhang, Y., Jia, Q., & Liu, K. (2025) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc65474.2025.11090561

Particle Filter-Based Fault Diagnosis With Hidden Markov Models for Nonlinear and Uncertain Dynamical Systems

Kwao, V., & Raptis, I. A. (2026) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2026.3682345

A Bank of Kalman Filters and a Robust Kalman Filter Applied in Fault Diagnosis of Aircraft Engine Sensor/Actuator

Xue, W., Guo, Y., & Zhang, X. (2007) · International Conference on Innovative Computing, Information and Control · doi:10.1109/icicic.2007.3 · Cited by 26

Aircraft Engine Sensor/Actuator/Component Fault Diagnosis Using a Bank of Kalman Filters

Kobayashi, T., & Simon, D. L. (2003) · NASA Technical Reports Server (NASA) · Cited by 33

Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics

Kobayashi, T., & Simon, D. L. (2005) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.1850505 · Cited by 88

Enhanced Bank of Kalman Filters Developed and Demonstrated for In-Flight Aircraft Engine Sensor Fault Diagnostics

Simon, D. L., & Kobayashi, T. (n.d.) · NASA Technical Reports Server (NASA)

Sensor Soft Failure Diagnostics Based on Aero-Engine On-Board Adaptive Model

Xue, W., Guo, Y., & Zhang, X. (2008) · International Conference on Innovative Computing, Information and Control · doi:10.1109/icicic.2008.487 · Cited by 2