A Novel Taxonomy and Approaches for the Identification of Frequently Occurring Regularities in Degradation Processes of Engineering Systems
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Dec 28, 2025
Fabian Mauthe
Christopher Braun
Julian Raible
Peter Zeiler
Marco F. Huber
https://orcid.org/0000-0003-3464-0586
Christopher Braun
Julian Raible
Peter Zeiler
Marco F. Huber
Abstract
The trend is shifting toward hybrid methods that incorporate prior knowledge into data-driven methods to address challenges in diagnostics and prognostics such as limited data, interpretability, and complex system behavior. While system-specific prior knowledge facilitates accurate, physically plausible modeling, the resulting hybrid model is typically tightly coupled to an individual engineering system. In contrast, general prior knowledge—such as fundamental physical laws or broadly applicable degradation knowledge—supports scalable, transferable models across various engineering systems. This opens the door to more adaptable approaches for diagnostics and prognostics, but the potential remains underexplored. To address this, a taxonomy is proposed that defines prior knowledge as frequently occurring regularities with four levels of validity, enabling hybrid methods to be characterized by their expected transferability. Two approaches are introduced and applied, both aimed at systematically identifying such regularities: one driven by expert knowledge, the other by data. Expert interviews further validate both the taxonomy and the identified regularities, establishing a foundation for developing transferable hybrid methods between various engineering systems.
##plugins.themes.bootstrap3.article.details##
Keywords
Prognostics and Health Management, Hybrid Methods, Transferability, Prior Knowledge, Knowledge Extraction, Frequently Occurring Regularity
References
Agogino, A. and Goebel, K. (2007). Milling data set. Moffett Field, CA. Retrieved 20.03.24, from https://www.nasa.gov/ intelligent-systems-division/ discovery-and-systems-health/pcoe/ pcoe-data-set-repository/
Aimiyekagbon, O. K. (2024). Run-to-failure data set of ball bearings subjected to time-varying load and speed conditions. Retrieved 29.03.25, from https://zenodo.org/records/10868257
Bajarunas, K., Baptista, M. L., Goebel, K., & Chao, M. A. (2024). Health index estimation through integration of general knowledge with unsupervised learning. Reliability Engineering & System Safety, 251, 110352. doi: 10.1016/j.ress.2024.110352
Barandas, M., Folgado, D., Fernandes, L., Santos, S., Abreu, M., Bota, P., ... Gamboa, H. (2020). TSFEL: Time Series Feature Extraction Library. SoftwareX, 11, 100456.
BenChikha, K., Kandidayeni, M., Amamou, A., Kelouwani, S., Agbossou, K., & Abdelghani, A. B. B. (2022). Fuel cell ageing prediction and remaining useful life forecasting. In 2022 ieee vehicle power and propulsion conference (vppc) (pp. 1–6). Piscataway, NJ: IEEE. doi: 10.1109/VPPC55846.2022.10003313
Berghout, T., & Benbouzid, M. (2022). A systematic guide for predicting remaining useful life with machine learning. Electronics, 11(7), 1125. doi: 10.3390/ electronics11071125
Birkl, C., & Howey, D. (2017). Oxford battery degradation dataset 1. Retrieved 29.03.25, from https://ora.ox.ac.uk/objects/uuid:03ba4b01-cfed-46d3-9b1a-7d4a7bdf6fac
Bole, B. and Kulkarni, C. and Daigle, M. (2014). Randomized battery usage data set. Moffett Field, CA. Retrieved 20.03.24, from https://www.nasa.gov/ intelligent-systems-division/ discovery-and-systems-health/pcoe/pcoe-data-set-repository/
Braig, M., & Zeiler, P. (2023). Using data from similar systems for data-driven condition diagnosis and prognosis of engineering systems: A review and an outline of future research challenges. IEEE Access, 11, 1506–1554. doi: 10.1109/ACCESS.2022.3233220
Branch, M. A., Coleman, T. F., & Li, Y. (1999). A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems. SIAM Journal on Scientific Computing, 21(1), 1–23.
Castillo, C., Ferbández-Canteli, A., Castillo, E., & Pinto, H. (2010). Building models for crack propagation under fatigue loads: application to macrocrack growth. Fatigue & Fracture of Engineering Materials & Structures, 33(10), 619–632. doi: 10.1111/j.1460-2695.2010.01475.x
Chen, X., Ma, M., Zhao, Z., Zhai, Z., & Mao, Z. (2022). Physics-informed deep neural network for bearing prognosis with multisensory signals. Journal of dynamics, monitoring and diagnostics, 200–207.
Deng, W., Nguyen, K. T., Medjaher, K., Gogu, C., & Morio, J. (2023). Physics-informed machine learning in prognostics and health management: State of the art and challenges. Applied Mathematical Modelling, 124, 325–352. doi: 10.1016/j.apm.2023.07.011
Diao, W., Kim, J., Azarian, M. H., & Pecht, M. (2022). Degradation modes and mechanisms analysis of lithium-ion batteries with knee points. Electrochimica Acta, 431, 141143. doi: 10.1016/j.electacta.2022.141143
E, L., Wang, J., Yang, R., Wang, C., Li, H., & Xiong, R. (2025). A physics-informed neural network-based method for predicting degradation trajectories and remaining useful life of supercapacitors. Green Energy and Intelligent Transportation, 4(3), 100291. doi: 10.1016/j.geits.2025.100291
Eker, O. F., Camci, F., & Jennions, I. K. (2016). Physics-based prognostic modelling of filter clogging phenomena. Mechanical Systems and Signal Processing, 75, 395–412. doi: 10.1016/j.ymssp.2015.12.011
Eker, O. F., Camci, F., & Jennions, I. K. (2019). A new hybrid prognostic methodology. International Journal of Prognostics and Health Management, 10(2). doi: 10.36001/ijphm.2019.v10i2.2727
FCLAB Federation. (2014). Ieee phm 2014 data challenge - fuel cell. FR CNRS 3539, France.
Gabrielli, A., Battarra, M., Mucchi, E., & Dalpiaz, G. (2024). Physics-based prognostics of rolling-element bearings: The equivalent damaged volume algorithm. Mechanical Systems and Signal Processing, 215, 111435. Retrieved from https:// www.sciencedirect.com/science/article/pii/S0888327024003339 doi: https://doi.org/10.1016/j.ymssp.2024.111435
Hagmeyer, S., Mauthe, F., & Zeiler, P. (2021). Creation of publicly available data sets for prognostics and diagnostics addressing data scenarios relevant to industrial applications. International Journal of Prognostics and Health Management, 12(2).
Hagmeyer, S., & Zeiler, P. (2023). A comparative study on methods for fusing data-driven and physics-based models for hybrid remaining useful life prediction of air filters. IEEE Access, 11, 35737–35753. doi: 10.1109/ ACCESS.2023.3265722
Hagmeyer, S., Zeiler, P., & Huber, M. F. (2022). On the integration of fundamental knowledge about degradation processes into data-driven diagnostics and prognostics using theory-guided data science. In Phm society european conference (Vol. 7, pp. 156–165).
He, Z., Shi, T., & Xuan, J. (2022). Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders. Measurement, 190, 110719. doi: 10.1016/j.measurement.2022.110719
Hoenig, M., Hagmeyer, S., & Zeiler, P. (2019). Enhancing remaining useful lifetime prediction by an advanced ensemble method adapted to the specific characteristics of prognostics and health management. In Proceedings of the 29th european safety and reliability conference (esrel) (pp. 1155–1162). doi: 10.3850/ 978-981-11-2724-3 0204-cd
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 3(6), 422– 440.
Kordestani, M., Saif, M., Orchard, M. E., Razavi-Far, R., & Khorasani, K. (2021). Failure prognosis and applications—a survey of recent literature. IEEE Transactions on Reliability, 70(2), 728–748. doi: 10.1109/TR.2019.2930195
Kulkarni, C. and Hogge, E. and Quach, C. and Goebel, K. (2015). Hirf battery data set. Moffett Field, CA. Retrieved 20.03.24, from https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/
Lee, J. and Qiu, H. and Yu, G. and Lin, J. (2007). Bearing data set. Moffett Field, CA. Retrieved 20.03.24, from https://www.nasa.gov/ intelligent-systems-division/ discovery-and-systems-health/pcoe/ pcoe-data-set-repository/
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to rul prediction. Mechanical Systems and Signal Processing, 104, 799–834. doi: 10.1016/j.ymssp.2017.11.016
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587. doi: 10.1016/j.ymssp.2019.106587
Li, X., Lim, B. S., Zhou, J. H., Huang, S., Phua, S. J., Shaw, K. C., & Er, M. J. (2009). Fuzzy neural network modelling for tool wear estimation in dry milling operation. Annual Conference of the PHM Society, 1(1).
Li, Y.-F., Wang, H., & Sun, M. (2024). Chatgpt-like large-scale foundation models for prognostics and health management: A survey and roadmaps. Reliability Engineering & System Safety, 243, 109850.
Lu, J., Xiong, R., Tian, J., Wang, C., Hsu, C.-W., Tsou, N.-T., … Li, J. (2022). Battery degradation dataset (fixed current profiles & arbitrary uses profiles). Retrieved 29.03.25, from https://data.mendeley.com/ datasets/kw34hhw7xg/3
Makdessi, M., Sari, A., Venet, P., Aubard, G., Chevalier, F., Pre´seau, R., … Duwattez, J. (2015). Lifetime estimation of high-temperature high-voltage polymer film capacitor based on capacitance loss. Microelectronics Reliability, 55(9-10), 2012–2016. doi: 10.1016/ j.microrel.2015.06.099
Mauthe, F., Bakir, E. M., Scheerer, T., & Zeiler, P. (2022). Prognosis based on varying data quality. Kaggle. Retrieved from https://www.kaggle.com/ datasets/prognosticshse/prognosis-based-on-varying-data-quality doi: 10.34740/kaggle/dsv/3814464
Mauthe, F., Braun, C., Raible, J., Zeiler, P., & Huber, M. F. (2024). Overview of publicly available degradation data sets for tasks within prognostics and health management. Retrieved from https://arxiv.org/ abs/2403.13694
Mauthe, F., Steinmann, L., Neu, M., & Zeiler, P. (2025). Overview and analysis of publicly available degradation data sets for tasks within prognostics and health management. In E. B. Abrahamsen, T. Aven, F. Bouder, R. Flage, & M. Yloenen (Eds.), 35th european safety and reliability conference. (accepted). Research Publishing.
Meeker, W. Q., Escobar, L. A., & Pascual, F. G. (2022). Statistical methods for reliability data (Second edition ed.). Hoboken, NJ: Wiley.
Meghoe, A., Loendersloot, R., & Tinga, T. (2020). Rail wear and remaining life prediction using meta-models. International Journal of Rail Transportation, 8(1), 1–26. doi: 10.1080/23248378.2019.1621780
Nectoux, P. and Gouriveau, R. and Medjaher, K. and Ramasso, E. and Morello, B. and Zerhouni, N. and Varnier, C. (2012). Pronostia : An experimental platform for bearings accelerated degradation tests. Denver, CO, USA. Retrieved 20.03.24, from https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/
Pan, R., Yang, D., Wang, Y., & Chen, Z. (2020). Performance degradation prediction of proton exchange membrane fuel cell using a hybrid prognostic approach. International Journal of Hydrogen Energy, 45(55), 30994– 31008. doi: 10.1016/j.ijhydene.2020.08.082
Sadoughi, M., Lu, H., & Hu, C. (2019). A deep learning approach for failure prognostics of rolling element bearings. In 2019 ieee international conference on prognostics and health management (icphm) (pp. 1–7). Piscataway, NJ: IEEE. doi: 10.1109/ICPHM.2019.8819442
Saha, B. and Goebel, K. (2007). Battery data set. Moffett Field, CA. Retrieved 20.03.24, from https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management, 1(1). doi: 10.36001/ijphm.2010.v1i1.1336
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., … Braatz, R. D. (2019). Data-driven prediction of battery cycle life before capacity degradation. Nature Energy, 4(5), 383–391. doi: 10.1038/ s41560-019-0356-8
Shrivastava, P., Naidu, P. A., Sharma, S., Panigrahi, B. K., & Garg, A. (2023). Review on technological advancement of lithium-ion battery states estimation methods for electric vehicle applications. Journal of Energy Storage, 64, 107159. doi: 10.1016/j.est.2023.107159
Thomas, D., Penicot, P., Contal, P., Leclerc, D., & Vendel, J. (2001). Clogging of fibrous filters by solid aerosol particles experimental and modelling study. Chemical Engineering Science, 56(11), 3549–3561. doi: 10.1016/S0009-2509(01)00041-0
Wang, B., Lei, Y., Li, N., & Li, N. (2020a). A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 69(1), 401–412. doi: 10.1109/TR.2018.2882682
Wang, B., Lei, Y., Li, N., & Li, N. (2020b). A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 69(1), 401-412. doi: 10.1109/TR.2018.2882682
Wang, D., Tsui, K.-L., & Miao, Q. (2018). Prognostics and health management: A review of vibration based bearing and gear health indicators. IEEE Access, 6, 665– 676. doi: 10.1109/ACCESS.2017.2774261
Zhou, H., Huang, X., Wen, G., Lei, Z., Dong, S., Zhang, P., & Chen, X. (2022). Construction of health indicators for condition monitoring of rotating machinery: A review of the research. Expert Systems with Applications, 203, 117297. doi: 10.1016/j.eswa.2022.117297
Zhou, Y., Liu, C., Yu, X., Liu, B., & Quan, Y. (2022). Tool wear mechanism, monitoring and remaining useful life (rul) technology based on big data: a review. SN Applied Sciences, 4(8). doi: 10.1007/s42452-022-05114-9
Zhu, J., Nostrand, T., Spiegel, C., & Morton, B. (2014). Survey of condition indicators for condition monitoring systems. Annual Conference of the PHM Society, 6(1). doi: 10.36001/phmconf.2014.v6i1.2514
Zhu, Jiangong. (2022). Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation. Retrieved 29.03.25, from https://zenodo.org/records/6405084
Zio, E. (2022). Prognostics and health management (phm): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, 218, 108119. doi: 10.1016/j.ress.2021.108119
Aimiyekagbon, O. K. (2024). Run-to-failure data set of ball bearings subjected to time-varying load and speed conditions. Retrieved 29.03.25, from https://zenodo.org/records/10868257
Bajarunas, K., Baptista, M. L., Goebel, K., & Chao, M. A. (2024). Health index estimation through integration of general knowledge with unsupervised learning. Reliability Engineering & System Safety, 251, 110352. doi: 10.1016/j.ress.2024.110352
Barandas, M., Folgado, D., Fernandes, L., Santos, S., Abreu, M., Bota, P., ... Gamboa, H. (2020). TSFEL: Time Series Feature Extraction Library. SoftwareX, 11, 100456.
BenChikha, K., Kandidayeni, M., Amamou, A., Kelouwani, S., Agbossou, K., & Abdelghani, A. B. B. (2022). Fuel cell ageing prediction and remaining useful life forecasting. In 2022 ieee vehicle power and propulsion conference (vppc) (pp. 1–6). Piscataway, NJ: IEEE. doi: 10.1109/VPPC55846.2022.10003313
Berghout, T., & Benbouzid, M. (2022). A systematic guide for predicting remaining useful life with machine learning. Electronics, 11(7), 1125. doi: 10.3390/ electronics11071125
Birkl, C., & Howey, D. (2017). Oxford battery degradation dataset 1. Retrieved 29.03.25, from https://ora.ox.ac.uk/objects/uuid:03ba4b01-cfed-46d3-9b1a-7d4a7bdf6fac
Bole, B. and Kulkarni, C. and Daigle, M. (2014). Randomized battery usage data set. Moffett Field, CA. Retrieved 20.03.24, from https://www.nasa.gov/ intelligent-systems-division/ discovery-and-systems-health/pcoe/pcoe-data-set-repository/
Braig, M., & Zeiler, P. (2023). Using data from similar systems for data-driven condition diagnosis and prognosis of engineering systems: A review and an outline of future research challenges. IEEE Access, 11, 1506–1554. doi: 10.1109/ACCESS.2022.3233220
Branch, M. A., Coleman, T. F., & Li, Y. (1999). A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems. SIAM Journal on Scientific Computing, 21(1), 1–23.
Castillo, C., Ferbández-Canteli, A., Castillo, E., & Pinto, H. (2010). Building models for crack propagation under fatigue loads: application to macrocrack growth. Fatigue & Fracture of Engineering Materials & Structures, 33(10), 619–632. doi: 10.1111/j.1460-2695.2010.01475.x
Chen, X., Ma, M., Zhao, Z., Zhai, Z., & Mao, Z. (2022). Physics-informed deep neural network for bearing prognosis with multisensory signals. Journal of dynamics, monitoring and diagnostics, 200–207.
Deng, W., Nguyen, K. T., Medjaher, K., Gogu, C., & Morio, J. (2023). Physics-informed machine learning in prognostics and health management: State of the art and challenges. Applied Mathematical Modelling, 124, 325–352. doi: 10.1016/j.apm.2023.07.011
Diao, W., Kim, J., Azarian, M. H., & Pecht, M. (2022). Degradation modes and mechanisms analysis of lithium-ion batteries with knee points. Electrochimica Acta, 431, 141143. doi: 10.1016/j.electacta.2022.141143
E, L., Wang, J., Yang, R., Wang, C., Li, H., & Xiong, R. (2025). A physics-informed neural network-based method for predicting degradation trajectories and remaining useful life of supercapacitors. Green Energy and Intelligent Transportation, 4(3), 100291. doi: 10.1016/j.geits.2025.100291
Eker, O. F., Camci, F., & Jennions, I. K. (2016). Physics-based prognostic modelling of filter clogging phenomena. Mechanical Systems and Signal Processing, 75, 395–412. doi: 10.1016/j.ymssp.2015.12.011
Eker, O. F., Camci, F., & Jennions, I. K. (2019). A new hybrid prognostic methodology. International Journal of Prognostics and Health Management, 10(2). doi: 10.36001/ijphm.2019.v10i2.2727
FCLAB Federation. (2014). Ieee phm 2014 data challenge - fuel cell. FR CNRS 3539, France.
Gabrielli, A., Battarra, M., Mucchi, E., & Dalpiaz, G. (2024). Physics-based prognostics of rolling-element bearings: The equivalent damaged volume algorithm. Mechanical Systems and Signal Processing, 215, 111435. Retrieved from https:// www.sciencedirect.com/science/article/pii/S0888327024003339 doi: https://doi.org/10.1016/j.ymssp.2024.111435
Hagmeyer, S., Mauthe, F., & Zeiler, P. (2021). Creation of publicly available data sets for prognostics and diagnostics addressing data scenarios relevant to industrial applications. International Journal of Prognostics and Health Management, 12(2).
Hagmeyer, S., & Zeiler, P. (2023). A comparative study on methods for fusing data-driven and physics-based models for hybrid remaining useful life prediction of air filters. IEEE Access, 11, 35737–35753. doi: 10.1109/ ACCESS.2023.3265722
Hagmeyer, S., Zeiler, P., & Huber, M. F. (2022). On the integration of fundamental knowledge about degradation processes into data-driven diagnostics and prognostics using theory-guided data science. In Phm society european conference (Vol. 7, pp. 156–165).
He, Z., Shi, T., & Xuan, J. (2022). Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders. Measurement, 190, 110719. doi: 10.1016/j.measurement.2022.110719
Hoenig, M., Hagmeyer, S., & Zeiler, P. (2019). Enhancing remaining useful lifetime prediction by an advanced ensemble method adapted to the specific characteristics of prognostics and health management. In Proceedings of the 29th european safety and reliability conference (esrel) (pp. 1155–1162). doi: 10.3850/ 978-981-11-2724-3 0204-cd
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 3(6), 422– 440.
Kordestani, M., Saif, M., Orchard, M. E., Razavi-Far, R., & Khorasani, K. (2021). Failure prognosis and applications—a survey of recent literature. IEEE Transactions on Reliability, 70(2), 728–748. doi: 10.1109/TR.2019.2930195
Kulkarni, C. and Hogge, E. and Quach, C. and Goebel, K. (2015). Hirf battery data set. Moffett Field, CA. Retrieved 20.03.24, from https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/
Lee, J. and Qiu, H. and Yu, G. and Lin, J. (2007). Bearing data set. Moffett Field, CA. Retrieved 20.03.24, from https://www.nasa.gov/ intelligent-systems-division/ discovery-and-systems-health/pcoe/ pcoe-data-set-repository/
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to rul prediction. Mechanical Systems and Signal Processing, 104, 799–834. doi: 10.1016/j.ymssp.2017.11.016
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587. doi: 10.1016/j.ymssp.2019.106587
Li, X., Lim, B. S., Zhou, J. H., Huang, S., Phua, S. J., Shaw, K. C., & Er, M. J. (2009). Fuzzy neural network modelling for tool wear estimation in dry milling operation. Annual Conference of the PHM Society, 1(1).
Li, Y.-F., Wang, H., & Sun, M. (2024). Chatgpt-like large-scale foundation models for prognostics and health management: A survey and roadmaps. Reliability Engineering & System Safety, 243, 109850.
Lu, J., Xiong, R., Tian, J., Wang, C., Hsu, C.-W., Tsou, N.-T., … Li, J. (2022). Battery degradation dataset (fixed current profiles & arbitrary uses profiles). Retrieved 29.03.25, from https://data.mendeley.com/ datasets/kw34hhw7xg/3
Makdessi, M., Sari, A., Venet, P., Aubard, G., Chevalier, F., Pre´seau, R., … Duwattez, J. (2015). Lifetime estimation of high-temperature high-voltage polymer film capacitor based on capacitance loss. Microelectronics Reliability, 55(9-10), 2012–2016. doi: 10.1016/ j.microrel.2015.06.099
Mauthe, F., Bakir, E. M., Scheerer, T., & Zeiler, P. (2022). Prognosis based on varying data quality. Kaggle. Retrieved from https://www.kaggle.com/ datasets/prognosticshse/prognosis-based-on-varying-data-quality doi: 10.34740/kaggle/dsv/3814464
Mauthe, F., Braun, C., Raible, J., Zeiler, P., & Huber, M. F. (2024). Overview of publicly available degradation data sets for tasks within prognostics and health management. Retrieved from https://arxiv.org/ abs/2403.13694
Mauthe, F., Steinmann, L., Neu, M., & Zeiler, P. (2025). Overview and analysis of publicly available degradation data sets for tasks within prognostics and health management. In E. B. Abrahamsen, T. Aven, F. Bouder, R. Flage, & M. Yloenen (Eds.), 35th european safety and reliability conference. (accepted). Research Publishing.
Meeker, W. Q., Escobar, L. A., & Pascual, F. G. (2022). Statistical methods for reliability data (Second edition ed.). Hoboken, NJ: Wiley.
Meghoe, A., Loendersloot, R., & Tinga, T. (2020). Rail wear and remaining life prediction using meta-models. International Journal of Rail Transportation, 8(1), 1–26. doi: 10.1080/23248378.2019.1621780
Nectoux, P. and Gouriveau, R. and Medjaher, K. and Ramasso, E. and Morello, B. and Zerhouni, N. and Varnier, C. (2012). Pronostia : An experimental platform for bearings accelerated degradation tests. Denver, CO, USA. Retrieved 20.03.24, from https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/
Pan, R., Yang, D., Wang, Y., & Chen, Z. (2020). Performance degradation prediction of proton exchange membrane fuel cell using a hybrid prognostic approach. International Journal of Hydrogen Energy, 45(55), 30994– 31008. doi: 10.1016/j.ijhydene.2020.08.082
Sadoughi, M., Lu, H., & Hu, C. (2019). A deep learning approach for failure prognostics of rolling element bearings. In 2019 ieee international conference on prognostics and health management (icphm) (pp. 1–7). Piscataway, NJ: IEEE. doi: 10.1109/ICPHM.2019.8819442
Saha, B. and Goebel, K. (2007). Battery data set. Moffett Field, CA. Retrieved 20.03.24, from https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management, 1(1). doi: 10.36001/ijphm.2010.v1i1.1336
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., … Braatz, R. D. (2019). Data-driven prediction of battery cycle life before capacity degradation. Nature Energy, 4(5), 383–391. doi: 10.1038/ s41560-019-0356-8
Shrivastava, P., Naidu, P. A., Sharma, S., Panigrahi, B. K., & Garg, A. (2023). Review on technological advancement of lithium-ion battery states estimation methods for electric vehicle applications. Journal of Energy Storage, 64, 107159. doi: 10.1016/j.est.2023.107159
Thomas, D., Penicot, P., Contal, P., Leclerc, D., & Vendel, J. (2001). Clogging of fibrous filters by solid aerosol particles experimental and modelling study. Chemical Engineering Science, 56(11), 3549–3561. doi: 10.1016/S0009-2509(01)00041-0
Wang, B., Lei, Y., Li, N., & Li, N. (2020a). A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 69(1), 401–412. doi: 10.1109/TR.2018.2882682
Wang, B., Lei, Y., Li, N., & Li, N. (2020b). A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 69(1), 401-412. doi: 10.1109/TR.2018.2882682
Wang, D., Tsui, K.-L., & Miao, Q. (2018). Prognostics and health management: A review of vibration based bearing and gear health indicators. IEEE Access, 6, 665– 676. doi: 10.1109/ACCESS.2017.2774261
Zhou, H., Huang, X., Wen, G., Lei, Z., Dong, S., Zhang, P., & Chen, X. (2022). Construction of health indicators for condition monitoring of rotating machinery: A review of the research. Expert Systems with Applications, 203, 117297. doi: 10.1016/j.eswa.2022.117297
Zhou, Y., Liu, C., Yu, X., Liu, B., & Quan, Y. (2022). Tool wear mechanism, monitoring and remaining useful life (rul) technology based on big data: a review. SN Applied Sciences, 4(8). doi: 10.1007/s42452-022-05114-9
Zhu, J., Nostrand, T., Spiegel, C., & Morton, B. (2014). Survey of condition indicators for condition monitoring systems. Annual Conference of the PHM Society, 6(1). doi: 10.36001/phmconf.2014.v6i1.2514
Zhu, Jiangong. (2022). Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation. Retrieved 29.03.25, from https://zenodo.org/records/6405084
Zio, E. (2022). Prognostics and health management (phm): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, 218, 108119. doi: 10.1016/j.ress.2021.108119
Section
Technical Papers