Phong Ba Dao, dr hab. inż., prof. AGH

Phong Ba Dao, PhD, DSc, Professor of AGH University of Krakow Department of Robotics and Mechatronics Faculty of Mechanical Engineering and Robotics AGH University of Krakow al. Adama Mickiewicza 30 30-059 Krakow Poland Office: room 409 (building D1) / room 401 (building D3) Phone: (+48) 12 617 36 40 E-mail: phongdao@agh.edu.pl ORCID Publons / ResearcherID Scopus Google Scholar Researchgate Loop AGH-BaDAP

Education and qualifications

Eng.                                – Cybernetics, Hanoi University of Science and Technology, 2001
M.Sc.                              – Instrumentation and Control, Hanoi University of Science and Technology, 2004
Ph.D.                              – Intelligent Control and Mechatronics, University of Twente, 2011
D.Sc. (Habilitation)  – Mechanical Engineering, AGH University of Science and Technology, 2020

Awards

• Team Award of the AGH Rector (3rd degree) for scientific achievements, 2018
• Individual Award of the AGH Rector (2nd degree) for scientific achievements, 2019
• Individual Award of the AGH Rector (2nd degree) for scientific achievements, 2021
• 1^st degree Individual Award of the AGH Rector for scientific achievements – top 10% of the best-publishing scientists, 2022 (the evaluation was based on publications in 2020 & 2021)
• 1^st degree Individual Award of the AGH Rector for scientific achievements – top 10% of the best-publishing scientists, 2023 (the evaluation was based on publications in 2020, 2021, 2022)
• 1^st degree Individual Award of the AGH Rector for scientific achievements – top 10% of the best-publishing scientists, 2024 (the evaluation was based on publications in 2021, 2022, 2023)

Research interests

• Structural health monitoring (SHM)
• Non-destructive testing (NDT)
• Condition monitoring and fault diagnosis
• Statistical time series methods for SHM & NDT
• Advanced signal/data processing
• Intelligent control, agent-based control, and mechatronics

Granted patents

1. W.J. Staszewski, P.B. Dao, and K. Zolna, “A method and a system for structural health monitoring,” European Patent Office, patent number: EP 3040700 B1, date of filing application: 29.12.2014, date of publication and mention of the grant of the patent: 24.04.2019, published in Bulletin 2019/17. [B1 patent specification]

Research projects (principal investigator)

1. Research project OPUS 26 no. 2023/51/B/ST8/01253 “Non-Classical Approaches for Condition Monitoring and Fault Detection of Wind Turbines”, funded by the National Science Centre (NCN) of Poland (7/2024 – 6/2027). Budget: 801,093 PLN (200,000 USD).

Ph.D. students

• Sylwester Szewczyk: research topic “Non-classical methods for misalignment monitoring and detection in rotor-shaft systems under varying load conditions” (Implementation Doctorate Programme)
• Debela A. Teklemariyem: research topic “Statistical methods for condition monitoring and fault detection of wind turbines”
• Abu Al Hassan: research topic “Integration of trend monitoring and change-point detection for fault and damage detection”
• Syed N.H. Razvi: research topic “Unit root nonstationarity: From econometrics to structural damage evaluation”
• Krzysztof Kijanowski: research topic “Transfer learning methods for wind turbine monitoring and fault detection using deep learning”

Selected publications (in JCR journals with impact factor)

1. P. Knes, P.B. Dao, “Machine learning and cointegration for wind turbine monitoring and fault detection: From a comparative study to a combined approach,” Energies, vol. 17, no. 20, 5055, 2024. [DOI] (Q1; 2023 Impact Factor: 3.0; CiteScore₂₀₂₃: 6.2)
2. P.B. Dao, T. Barszcz, and W.J. Staszewski, “Anomaly detection of wind turbines based on stationarity analysis of SCADA data,” Renewable Energy, vol. 232, 121076, 2024. [DOI] (Q1; 2023 Impact Factor: 9.0; CiteScore₂₀₂₃: 18.4)
3. J. Długosz, P.B. Dao, W.J. Staszewski, and T. Uhl, “Damage detection in glass fibre composites using cointegrated hyperspectral images,” Sensors, vol. 24, no. 6, 1980, 2024. [DOI] (Q1; 2022 Impact Factor: 3.9; CiteScore₂₀₂₂: 6.8)
4. P.B. Dao, “Lamb wave-based structural damage detection: A time series approach using cointegration,” Materials, vol. 16, no. 21, 6894, 2023. [DOI] (Q2; 2022 Impact Factor: 3.4; CiteScore₂₀₂₂: 5.2)
5. P.B. Dao, “On cointegration analysis for condition monitoring and fault detection of wind turbines using SCADA data,” Energies, vol. 16, no. 5, 2352, 2023. [DOI] (Q1; 2022 Impact Factor: 3.2; CiteScore₂₀₂₂: 5.5)
6. P.B. Dao and W.J. Staszewski, “Cointegration and how it works for structural health monitoring,” Measurement, vol. 209, 112503, 2023. [DOI] (Q1; 2022 Impact Factor: 5.6; CiteScore₂₀₂₂: 9.0)
7. P.B. Dao, “On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines,” Applied Energy, vol. 318, 119209, 2022. [DOI] (Q1; 2022 Impact Factor: 11.2; CiteScore₂₀₂₂: 21.1)
8. P.B. Dao, “Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data,” Renewable Energy, vol. 185, pp. 641–654, 2022. [DOI] (Q1; 2022 Impact Factor: 8.7; CiteScore₂₀₂₂: 16.1)
9. P.B. Dao and W.J. Staszewski, “Lamb wave based structural damage detection using stationarity tests,” Materials, vol. 14, no. 22, 6823, 2021. [DOI] (Q2; 2021 Impact Factor: 3.748)
10. P.B. Dao, “A CUSUM-based approach for condition monitoring and fault diagnosis of wind turbines,” Energies, vol. 14, no. 11, 3236, 2021. [DOI] (Q1; 2021 Impact Factor: 3.252)
11. P.B. Dao, “OROMACS: A design framework for multi-agent control system,” International Journal of Control, Automation and Systems, vol. 19, no. 5, pp. 1907–1919, 2021. [DOI] (Q1; 2021 Impact Factor: 2.964; CiteScore₂₀₂₁: 5.7)
12. P.B. Dao, “Learning feedforward control using multiagent control approach for motion control systems,” Energies, vol. 14, no. 2, 420, 2021. [DOI] (Q1; 2021 Impact Factor: 3.252)
13. P.B. Dao, W.J. Staszewski, T. Barszcz, and T. Uhl, “Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data,” Renewable Energy, vol. 116, part B, pp. 107–122, 2018. [DOI] (Q1; 2018 Impact Factor: 5.439; CiteScore₂₀₁₈: 9.9)
14. P.B. Dao, A. Klepka, Ł. Pieczonka, F. Aymerich, and W.J. Staszewski, “Impact damage detection in smart composites using nonlinear acoustics – cointegration analysis for removal of undesired load effect,” Smart Materials and Structures, vol. 26, no. 3, 035012, 2017. [DOI] (Q1; 2017 Impact Factor: 2.963)
15. P.B. Dao, W.J. Staszewski, and A. Klepka, “Stationarity-based approach for the selection of lag length in cointegration analysis used for structural damage detection,” Computer-Aided Civil and Infrastructure Engineering, vol. 32, no. 2, pp. 138–153, 2017. [DOI] (Q1; 2017 Impact Factor: 5.475)
16. K. Zolna, P.B. Dao, W.J. Staszewski, and T. Barszcz, “Towards homoscedastic nonlinear cointegration for structural health monitoring,” Mechanical Systems and Signal Processing, vol. 75, pp. 94–108, 2016. [DOI] (Q1; 2016 Impact Factor: 4.116)
17. K. Zolna, P.B. Dao, W.J. Staszewski, and T. Barszcz, “Nonlinear cointegration approach for condition monitoring of wind turbines,” Mathematical Problems in Engineering, vol. 2015, Article ID 978156, 11 pages, 2015. [DOI] (Q2; 2015 Impact Factor: 0.644)
18. P.B. Dao and W.J. Staszewski, “Lamb wave based structural damage detection using cointegration and fractal signal processing,” Mechanical Systems and Signal Processing, vol. 49, no. 1–2, pp. 285–301, 2014. [DOI] (Q1; 2014 Impact Factor: 2.256)
19. P.B. Dao and W.J. Staszewski, “Data normalisation for Lamb wave–based damage detection using cointegration: A case study with single- and multiple-temperature trends,” Journal of Intelligent Material Systems and Structures, vol. 25, no. 7, pp. 845–857, 2014. [DOI] (Q1; 2014 Impact Factor: 2.072)
20. P.B. Dao and W.J. Staszewski, “Cointegration approach for temperature effect compensation in Lamb wave based damage detection,” Smart Materials and Structures, vol. 22, no. 9, 095002, 2013. [DOI] (Q1; 2013 Impact Factor: 2.449)

Theses / Monographs

1. P.B. Dao, Cointegration-Based Approach for Structural Health Monitoring: Theory and Applications, monograph nr. 339, AGH University of Science and Technology Publishing House, Kraków, 2018, 124 pages, ISBN 978-83-66016-44-6. (D.Sc. / Habilitation Monograph) [full text]
2. P.B. Dao, Safe-Guarded Multi-Agent Control for Mechatronic Systems: Implementation Framework and Design Patterns, Ph.D. Thesis, University of Twente, Enschede, The Netherlands, 2011, 195 pages, ISBN 978-90-365-3148-1. [full text]

Editorial activities

• Topic Editor of MDPI’s Research Topic entitled “Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures (2nd Edition)”
• Topic Editor of MDPI’s Research Topic entitled “Structural Health Monitoring and Non-Destructive Testing for Large-Scale Structures”
• Guest Editor of the Special Issue on “Advanced Signal/Data Processing for Structural Health Monitoring”
• Active referee for many international journals such as Smart Materials and Structures, IEEE Transactions on Industrial Electronics, Applied Energy, Expert Systems with Applications, Mechanical Systems and Signal Processing, Sensors, Automatica, and others. More details can be found on my WoS account.

Teaching

• Control Theory (lecture classes, laboratory and exercise classes) for the 3rd-year students (BSc programme in Mechatronics) Syllabus
• Mechatronic System Identification (laboratory and project classes) for the 4th-year students (MSc programme in Mechatronic Design)
• Mechatronic Systems (project classes) for the 5th-year students (MSc programme in Mechatronic Design)
• Mechatronic Design (laboratory classes) for the 4th-year students (BSc programme in Mechatronics)

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