Methods and Systems for Hybrid Digital Twin Driven Health Predictions for Aircraft Sub-systems
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Published
Jan 13, 2026
Partha Pratim Adhikari
Deepu Vettimittathu Mathai
Avik Sadhu
Darren Macer
Abstract
In the aerospace industry, modern aircraft are increasingly equipped with a growing number of sensors, which enable the development of predictive maintenance solutions utilizing data-driven diagnostic and prognostic (D&P) techniques to enhance operational availability and reduce maintenance costs. However, constructing a purely data-driven D&P solution requires a substantial amount of run-to-fail sensor data, which is often unavailable for highly reliable and safety-critical aircraft systems. This limitation restricts the applicability of purely data-driven D&P solutions for aircraft subsystems. To address this limitation, we developed a novel Hybrid Digital Twin framework that integrates physics-based subsystem models with sensor data, enabling enhanced feature generation for improved fault diagnostics and prognostics. Our approach simultaneously estimates both design and health-related parameters, facilitating accurate model calibration even when some of design data is not available. Sensor features enhanced with estimated health-related parameters enable more accurate data-driven diagnostics and prognostics solutions of a sub-system or a component. The framework is demonstrated on key subsystems of the aircraft Environment Control System (ECS), including the Heat Exchanger and Centrifugal Compressor. Various parameter estimation techniques including nonlinear least squares, particle swarm optimization, and extended Kalman filter, Unscented Kalman filter, Physics-Informed Neural Networks, etc., are evaluated. This Hybrid Digital Twin approach offers a promising pathway for more accurate, robust and scalable health management of aircraft subsystems having limited operational data.
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Keywords
Physics based Model, Hybrid Digital Twin, Model Calibration, Parameter Estimation, Heat Exchanger, Diagnostics & Prognostics
References
Adhikari, P., Rao, H. G., & Buderath, M. (2018, October). Machine learning based data driven diagnostics & prognostics framework for aircraft predictive maintenance. In Proceedings of the 10th International Symposium on NDT in Aerospace, Dresden, Germany (pp. 24-26).
Bastida, H., Ugalde-Loo, C. E., Abeysekera, M., Xu, X., & Qadrdan, M. (2019, September). Dynamic modelling and control of counter-flow heat exchangers for heating and cooling systems. In 2019 54th international universities power engineering conference (UPEC) (pp. 1-6). IEEE. doi: 10.1109/UPEC.2019.8893634.
Brebenel, M. (2020). An analytical method for simulation of centrifugal compressors. INCAS Bulletin, 12(1), 35-49.doi: 10.13111/2066-8201.2020.12.1.4
Chu, F., Dai, B., Dai, W., Jia, R., Ma, X., & Wang, F. (2017). Rapid modeling method for performance prediction of centrifugal compressor based on model migration and SVM. IEEE Access, 5, 21488-21496. doi: 10.1109/ACCESS.2017.2753378.
Ezhilarasu, C. M., Skaf, Z., & Jennions, I. K. (2019). The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities. Progress in aerospace sciences, 105, 60-73.doi: 10.1016/J.PAEROSCI.2019.01.001
Ezhilarasu, C. M., Skaf, Z., & Jennions, I. K. (2019, October). Understanding the role of a digital twin in integrated vehicle health management (IVHM). In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 1484-1491). IEEE. doi: 10.1109/SMC.2019.8914244.
Friedenthal, S., Griego, R., & Sampson, M. (2007, June). INCOSE model based systems engineering (MBSE) initiative. In INCOSE 2007 symposium (Vol. 11). sn.
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: enabling technologies, challenges and open research. IEEE access, 8, 108952-108971. doi: 10.1109/ACCESS.2020.2998358
Gartner Inc, “Gartner Survey Reveals Digital Twins Are Entering Mainstream Use.” [Online]. Available: https://www.gartner.com/en/newsroom/press-releases/2019-02- 20-gartner-survey-reveals-digital-twins-are-entering-mai.[Accessed: 28-Mar-2019].
GE Digital Twin, (2016)
https://hi.dcsmodule.com/js/htmledit/kindeditor/attached/20210916/20210916172149_81906.pdf
Glaessgen, E., & Stargel, D. (2012, April). The digital twin paradigm for future NASA and US Air Force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA (p. 1818).doi: 10.2514/6.2012-1818
Goldenberg B. Driving innovation: Rolls Royce’s success with digital twins. Retrieved August 4, 2025, from ismguide’s website: https://ismguide.com/rolls-royce-use-of-digital-twin-technology-case-study/
Gravdahl, J. T., & Egeland, O. (2002). Centrifugal compressor surge and speed control. IEEE Transactions on control systems technology, 7(5), 567-579. doi: 10.1109/87.784420.
Grieves, M. (2016). Origins of the Digital Twin Concept. Florida Institute of Technology/NASA.
Grieves, M., & Vickers, J. (2016). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary perspectives on complex systems: New findings and approaches (pp. 85-113). Cham: Springer International Publishing. doi: 10.1007/978-3-319-38756-7_4
Guðmundsson, O. (2008). Detection of fouling in heat exchangers (Master's dissertation). University of Iceland.url: https://skemman.is/handle/1946/10192https://skemman.is/handle/1946/10192
Hafaifa, A., Rachid, B., & Mouloud, G. (2014). Modelling of surge phenomena in a centrifugal compressor: experimental analysis for control. Systems Science & Control Engineering: An Open Access Journal, 2(1), 632-641.doi: 10.1080/21642583.2014.956269
Iqbal, A. (2019). Applications of an Extended Kalman filter in nonlinear mechanics (Doctoral dissertation, PhD Thesis, University of Management and Technology. url: https://www. physlab. org/wp- content/uploads/2019/06/Thesis-compressed. pdf).
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), 1483-1510. doi:10.1016/J.YMSSP.2005.09.012
Jennions, I., Ali, F., Miguez, M. E., & Escobar, I. C. (2020). Simulation of an aircraft environmental control system. Applied Thermal Engineering, 172, 114925.doi: 10.1016/j.applthermaleng.2020.114925
Jia, W., Wang, W., & Zhang, Z. (2022). From simple digital twin to complex digital twin Part I: A novel modeling method for multi-scale and multi-scenario digital twin. Advanced Engineering Informatics, 53, 101706. doi: 10.1016/j.aei.2022.101706.
Jiang, H., Dong, S., Liu, Z., He, Y., & Ai, F. (2019). Performance prediction of the centrifugal compressor based on a limited number of sample data. Mathematical Problems in Engineering, 2019(1), 5954128.
Jonsson, G. R., Lalot, S., Palsson, O. P., & Desmet, B. (2007). Use of extended Kalman filtering in detecting fouling in heat exchangers. International journal of heat and mass transfer, 50(13-14), 2643-2655.doi: 10.1016/j.ijheatmasstransfer.2006.11.025.
Jonsson, G., & Palsson, O. P. (1994). An application of extended Kalman filtering to heat exchanger models. (Doctoral dissertation). University of Iceland, url: https://asmedigitalcollection.asme.org/dynamicsystems/article-abstract/116/2/257/399542/An-Application-of-Extended-Kalman-Filtering-to
Kurz, R., White, R., Brun, K., & Winklemann, B. (2015). Tutorial: Surge Control and Dynamic Behavior for Centrifugal Gas Compressors.
Le, H., Zach, C., Rosten, E., & Woodford, O. J. (2020). Progressive batching for efficient non-linear least squares. In Proceedings of the Asian Conference on Computer Vision. doi:10.48550/arXiv.2010.10968.
Lim, K. Y. H., Zheng, P., & Chen, C. H. (2020). A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of intelligent manufacturing, 31(6), 1313-1337.doi: 10.1007/s10845-019-01512-w
Liu, H., Xia, M., Williams, D., Sun, J., & Yan, H. (2022). Digital Twin‐Driven Machine Condition Monitoring: A Literature Review. Journal of Sensors, 2022(1), 6129995. doi: 10.1155/2022/6129995
Liu, H., Xia, M., Williams, D., Sun, J., & Yan, H. (2022). Digital Twin‐Driven Machine Condition Monitoring: A Literature Review. Journal of Sensors, 2022(1), 6129995.doi: 10.1155/2022/6129995
Liu, Z., Meyendorf, N., Blasch, E., Tsukada, K., Liao, M., & Mrad, N. (2025). The role of data fusion in predictive maintenance using digital twins. In Handbook of nondestructive evaluation 4.0 (pp. 429-451). Cham: Springer Nature Switzerland.doi: 10.1007/978-3-031-84477-5_65
Louise Bonnar (2019), “Twining: Digital Twins Show their Power”, https://aerospacetechreview.com/twinning-digital-twins-show-their-power-by-louise-bonnar/
Aerospace Tech Review
Ma, J., Lu, C., & Liu, H. (2015). Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter. PloS one, 10(3), e0122829.doi: 10.1371/journal.pone.0122829
Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging Digital Twin Technology in Model-Based Systems Engineering. Systems, 7(1), 7. doi:10.3390/systems7010007
Newman, D. (2023, September). A Method for Executing Digital System Models and Digital Twins at Scale to Enrich Fleet Health Management. In PHM Society Asia-Pacific Conference (Vol. 4, No. 1).
Nikula, R., Paavola, M., Ruusunen, M. & Keski-Rahkonen, J. (2020). Towards online adaptation of digital twins. Open Engineering, 10(1), 776-783. doi:10.1515/eng-2020-0088
Pecht, M., & Kumar, S. (2008, January). Data analysis approach for system reliability, diagnostics and prognostics. In Pan Pacific Microelectronics Symposium (Vol. 795, pp. 1-9). Hawaii, USA: Kauai.
Pujana, A., Esteras, M., Perea, E., Maqueda, E., & Calvez, P. (2023). Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation. Energies, 16(2), 861. doi:10.3390/en16020861
Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modeling perspective. IEEE access, 8, pp.21980-22012. doi: 10.1109/ACCESS.2020.2970143.
Ravi, A., Sznajder, L., & Bennett, I. (2015, July). Compressor map prediction tool. In IOP Conference Series: Materials Science and Engineering (Vol. 90, No. 1, p. 012042). IOP Publishing. doi:10.1088/1757-899X/90/1/012042
Redding, L. (2011). Integrated vehicle health management: perspectives on an emerging field, SAE International
Reid, J. B., & Rhodes, D. H. (2016, March). Digital system models: An investigation of the non-technical challenges and research needs. In Conference on systems engineering research (pp. 1-10).
Schleich, B., Anwer, N., Mathieu, L., & Wartzack, S. (2017). Shaping the digital twin for design and production engineering. CIRP annals, 66(1), 141-144.doi: 10.1016/j.cirp.2017.04.040
Shah, S., Liu, G., & Greatrix, D. R. (2009, August). On-line fouling detection of aircraft environmental control system cross flow heat exchanger. In 2009 International Conference on Mechatronics and Automation (pp. 2940-2945). IEEE.doi: 10.1109/ICMA.2009.5246062
Shigetomi, S., Imamura, M., Kaido, N., Taniguchi, M., Nishiwaki, M., & Kaya, J. (2023, September). Anomaly Detection in Airliner Centrifugal Compressor Using Sensor Data during the Climb Phase. In PHM Society Asia-Pacific Conference (Vol. 4, No. 1).
Sun, Y., Wang, Y., Bai, L., Hu, Y., Sidorov, D., & Panasetsky, D. (2018). Parameter estimation of electromechanical oscillation based on a constrained EKF with C&I-PSO. Energies, 11(8), 2059. doi:10.3390/en11082059
Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. (2018). Digital twin driven prognostics and health management for complex equipment. Cirp Annals, 67(1), 169-172.doi: 10.1016/j.cirp.2018.04.055
Tavakoli, S., Griffin, I., & Fleming, P. (2004). An overview of compressor instabilities: basic concepts and control. IFAC Proceedings Volumes, 37(6), 523-528.doi: 10.1016/S1474-6670(17)32228-0.
Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and health management: A review on data driven approaches. Mathematical Problems in Engineering, 2015(1), 793161.doi: 10.1155/2015/793161
Tuegel, E. (2012, April). The airframe digital twin: Some challenges to realization. In 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA (p. 1812). doi.:10.2514/6.2012-1812
Xu, B., Ren, Y., Zhu, P., & Lu, M. (2014, December). A PSO-based approach for multi-cell multi-parameter estimation. In The 2014 International Conference on Control, Automation and Information Sciences (ICCAIS 2014) (pp. 65-70). IEEE. doi: 10.1109/ICCAIS.2014.7020569
Yu, X., Wei, J., Dong, G., Chen, Z., & Zhang, C. (2019). State-of-charge estimation approach of lithium-ion batteries using an improved extended Kalman filter. Energy Procedia, 158, 5097-5102.doi: 10.1016/j.egypro.2019.01.691
Zakrajsek, A. J., & Mall, S. (2017). The development and use of a digital twin model for tire touchdown health monitoring. In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (p. 0863). doi:10.2514/6.2017-0863
Zhang, J., Zhang, L., Wang, R., & Hou, G. (2015, June). Fouling detection in heat exchanger using a bilinear model-based parameter estimation method. In 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA) (pp. 1831-1836). IEEE.doi: 10.1109/ICIEA.2015.7334409
Zhao, Z., Qi, P., & Liu, F. (2017, May). Iterative learning state estimation for batch process. In 2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP) (pp. 424-429). IEEE.doi: 10.1109/ADCONIP.2017.7983818.
Bastida, H., Ugalde-Loo, C. E., Abeysekera, M., Xu, X., & Qadrdan, M. (2019, September). Dynamic modelling and control of counter-flow heat exchangers for heating and cooling systems. In 2019 54th international universities power engineering conference (UPEC) (pp. 1-6). IEEE. doi: 10.1109/UPEC.2019.8893634.
Brebenel, M. (2020). An analytical method for simulation of centrifugal compressors. INCAS Bulletin, 12(1), 35-49.doi: 10.13111/2066-8201.2020.12.1.4
Chu, F., Dai, B., Dai, W., Jia, R., Ma, X., & Wang, F. (2017). Rapid modeling method for performance prediction of centrifugal compressor based on model migration and SVM. IEEE Access, 5, 21488-21496. doi: 10.1109/ACCESS.2017.2753378.
Ezhilarasu, C. M., Skaf, Z., & Jennions, I. K. (2019). The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities. Progress in aerospace sciences, 105, 60-73.doi: 10.1016/J.PAEROSCI.2019.01.001
Ezhilarasu, C. M., Skaf, Z., & Jennions, I. K. (2019, October). Understanding the role of a digital twin in integrated vehicle health management (IVHM). In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 1484-1491). IEEE. doi: 10.1109/SMC.2019.8914244.
Friedenthal, S., Griego, R., & Sampson, M. (2007, June). INCOSE model based systems engineering (MBSE) initiative. In INCOSE 2007 symposium (Vol. 11). sn.
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: enabling technologies, challenges and open research. IEEE access, 8, 108952-108971. doi: 10.1109/ACCESS.2020.2998358
Gartner Inc, “Gartner Survey Reveals Digital Twins Are Entering Mainstream Use.” [Online]. Available: https://www.gartner.com/en/newsroom/press-releases/2019-02- 20-gartner-survey-reveals-digital-twins-are-entering-mai.[Accessed: 28-Mar-2019].
GE Digital Twin, (2016)
https://hi.dcsmodule.com/js/htmledit/kindeditor/attached/20210916/20210916172149_81906.pdf
Glaessgen, E., & Stargel, D. (2012, April). The digital twin paradigm for future NASA and US Air Force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA (p. 1818).doi: 10.2514/6.2012-1818
Goldenberg B. Driving innovation: Rolls Royce’s success with digital twins. Retrieved August 4, 2025, from ismguide’s website: https://ismguide.com/rolls-royce-use-of-digital-twin-technology-case-study/
Gravdahl, J. T., & Egeland, O. (2002). Centrifugal compressor surge and speed control. IEEE Transactions on control systems technology, 7(5), 567-579. doi: 10.1109/87.784420.
Grieves, M. (2016). Origins of the Digital Twin Concept. Florida Institute of Technology/NASA.
Grieves, M., & Vickers, J. (2016). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary perspectives on complex systems: New findings and approaches (pp. 85-113). Cham: Springer International Publishing. doi: 10.1007/978-3-319-38756-7_4
Guðmundsson, O. (2008). Detection of fouling in heat exchangers (Master's dissertation). University of Iceland.url: https://skemman.is/handle/1946/10192https://skemman.is/handle/1946/10192
Hafaifa, A., Rachid, B., & Mouloud, G. (2014). Modelling of surge phenomena in a centrifugal compressor: experimental analysis for control. Systems Science & Control Engineering: An Open Access Journal, 2(1), 632-641.doi: 10.1080/21642583.2014.956269
Iqbal, A. (2019). Applications of an Extended Kalman filter in nonlinear mechanics (Doctoral dissertation, PhD Thesis, University of Management and Technology. url: https://www. physlab. org/wp- content/uploads/2019/06/Thesis-compressed. pdf).
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), 1483-1510. doi:10.1016/J.YMSSP.2005.09.012
Jennions, I., Ali, F., Miguez, M. E., & Escobar, I. C. (2020). Simulation of an aircraft environmental control system. Applied Thermal Engineering, 172, 114925.doi: 10.1016/j.applthermaleng.2020.114925
Jia, W., Wang, W., & Zhang, Z. (2022). From simple digital twin to complex digital twin Part I: A novel modeling method for multi-scale and multi-scenario digital twin. Advanced Engineering Informatics, 53, 101706. doi: 10.1016/j.aei.2022.101706.
Jiang, H., Dong, S., Liu, Z., He, Y., & Ai, F. (2019). Performance prediction of the centrifugal compressor based on a limited number of sample data. Mathematical Problems in Engineering, 2019(1), 5954128.
Jonsson, G. R., Lalot, S., Palsson, O. P., & Desmet, B. (2007). Use of extended Kalman filtering in detecting fouling in heat exchangers. International journal of heat and mass transfer, 50(13-14), 2643-2655.doi: 10.1016/j.ijheatmasstransfer.2006.11.025.
Jonsson, G., & Palsson, O. P. (1994). An application of extended Kalman filtering to heat exchanger models. (Doctoral dissertation). University of Iceland, url: https://asmedigitalcollection.asme.org/dynamicsystems/article-abstract/116/2/257/399542/An-Application-of-Extended-Kalman-Filtering-to
Kurz, R., White, R., Brun, K., & Winklemann, B. (2015). Tutorial: Surge Control and Dynamic Behavior for Centrifugal Gas Compressors.
Le, H., Zach, C., Rosten, E., & Woodford, O. J. (2020). Progressive batching for efficient non-linear least squares. In Proceedings of the Asian Conference on Computer Vision. doi:10.48550/arXiv.2010.10968.
Lim, K. Y. H., Zheng, P., & Chen, C. H. (2020). A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of intelligent manufacturing, 31(6), 1313-1337.doi: 10.1007/s10845-019-01512-w
Liu, H., Xia, M., Williams, D., Sun, J., & Yan, H. (2022). Digital Twin‐Driven Machine Condition Monitoring: A Literature Review. Journal of Sensors, 2022(1), 6129995. doi: 10.1155/2022/6129995
Liu, H., Xia, M., Williams, D., Sun, J., & Yan, H. (2022). Digital Twin‐Driven Machine Condition Monitoring: A Literature Review. Journal of Sensors, 2022(1), 6129995.doi: 10.1155/2022/6129995
Liu, Z., Meyendorf, N., Blasch, E., Tsukada, K., Liao, M., & Mrad, N. (2025). The role of data fusion in predictive maintenance using digital twins. In Handbook of nondestructive evaluation 4.0 (pp. 429-451). Cham: Springer Nature Switzerland.doi: 10.1007/978-3-031-84477-5_65
Louise Bonnar (2019), “Twining: Digital Twins Show their Power”, https://aerospacetechreview.com/twinning-digital-twins-show-their-power-by-louise-bonnar/
Aerospace Tech Review
Ma, J., Lu, C., & Liu, H. (2015). Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter. PloS one, 10(3), e0122829.doi: 10.1371/journal.pone.0122829
Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging Digital Twin Technology in Model-Based Systems Engineering. Systems, 7(1), 7. doi:10.3390/systems7010007
Newman, D. (2023, September). A Method for Executing Digital System Models and Digital Twins at Scale to Enrich Fleet Health Management. In PHM Society Asia-Pacific Conference (Vol. 4, No. 1).
Nikula, R., Paavola, M., Ruusunen, M. & Keski-Rahkonen, J. (2020). Towards online adaptation of digital twins. Open Engineering, 10(1), 776-783. doi:10.1515/eng-2020-0088
Pecht, M., & Kumar, S. (2008, January). Data analysis approach for system reliability, diagnostics and prognostics. In Pan Pacific Microelectronics Symposium (Vol. 795, pp. 1-9). Hawaii, USA: Kauai.
Pujana, A., Esteras, M., Perea, E., Maqueda, E., & Calvez, P. (2023). Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation. Energies, 16(2), 861. doi:10.3390/en16020861
Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modeling perspective. IEEE access, 8, pp.21980-22012. doi: 10.1109/ACCESS.2020.2970143.
Ravi, A., Sznajder, L., & Bennett, I. (2015, July). Compressor map prediction tool. In IOP Conference Series: Materials Science and Engineering (Vol. 90, No. 1, p. 012042). IOP Publishing. doi:10.1088/1757-899X/90/1/012042
Redding, L. (2011). Integrated vehicle health management: perspectives on an emerging field, SAE International
Reid, J. B., & Rhodes, D. H. (2016, March). Digital system models: An investigation of the non-technical challenges and research needs. In Conference on systems engineering research (pp. 1-10).
Schleich, B., Anwer, N., Mathieu, L., & Wartzack, S. (2017). Shaping the digital twin for design and production engineering. CIRP annals, 66(1), 141-144.doi: 10.1016/j.cirp.2017.04.040
Shah, S., Liu, G., & Greatrix, D. R. (2009, August). On-line fouling detection of aircraft environmental control system cross flow heat exchanger. In 2009 International Conference on Mechatronics and Automation (pp. 2940-2945). IEEE.doi: 10.1109/ICMA.2009.5246062
Shigetomi, S., Imamura, M., Kaido, N., Taniguchi, M., Nishiwaki, M., & Kaya, J. (2023, September). Anomaly Detection in Airliner Centrifugal Compressor Using Sensor Data during the Climb Phase. In PHM Society Asia-Pacific Conference (Vol. 4, No. 1).
Sun, Y., Wang, Y., Bai, L., Hu, Y., Sidorov, D., & Panasetsky, D. (2018). Parameter estimation of electromechanical oscillation based on a constrained EKF with C&I-PSO. Energies, 11(8), 2059. doi:10.3390/en11082059
Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. (2018). Digital twin driven prognostics and health management for complex equipment. Cirp Annals, 67(1), 169-172.doi: 10.1016/j.cirp.2018.04.055
Tavakoli, S., Griffin, I., & Fleming, P. (2004). An overview of compressor instabilities: basic concepts and control. IFAC Proceedings Volumes, 37(6), 523-528.doi: 10.1016/S1474-6670(17)32228-0.
Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and health management: A review on data driven approaches. Mathematical Problems in Engineering, 2015(1), 793161.doi: 10.1155/2015/793161
Tuegel, E. (2012, April). The airframe digital twin: Some challenges to realization. In 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA (p. 1812). doi.:10.2514/6.2012-1812
Xu, B., Ren, Y., Zhu, P., & Lu, M. (2014, December). A PSO-based approach for multi-cell multi-parameter estimation. In The 2014 International Conference on Control, Automation and Information Sciences (ICCAIS 2014) (pp. 65-70). IEEE. doi: 10.1109/ICCAIS.2014.7020569
Yu, X., Wei, J., Dong, G., Chen, Z., & Zhang, C. (2019). State-of-charge estimation approach of lithium-ion batteries using an improved extended Kalman filter. Energy Procedia, 158, 5097-5102.doi: 10.1016/j.egypro.2019.01.691
Zakrajsek, A. J., & Mall, S. (2017). The development and use of a digital twin model for tire touchdown health monitoring. In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (p. 0863). doi:10.2514/6.2017-0863
Zhang, J., Zhang, L., Wang, R., & Hou, G. (2015, June). Fouling detection in heat exchanger using a bilinear model-based parameter estimation method. In 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA) (pp. 1831-1836). IEEE.doi: 10.1109/ICIEA.2015.7334409
Zhao, Z., Qi, P., & Liu, F. (2017, May). Iterative learning state estimation for batch process. In 2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP) (pp. 424-429). IEEE.doi: 10.1109/ADCONIP.2017.7983818.
Section
Regular Session Papers