ISA-PHM – a Standardized Format for Storing and Utilizing Meta-data of Diagnostic and Prognostic Tests
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Published
Apr 27, 2026
Tiedo Tinga
Luc S. Keizers
Wouter van Riel
Nathan M. Houwaart
Jurjen R. Helmus
Abstract
The development and implementation of diagnostic and prognostic algorithms for smart maintenance purposes is hindered by the fundamental lack of relevant, complete and properly labeled data from fielded systems. This issue is partly tackled by the generation of well-defined datasets using numerical simulations or experimental set-ups in a laboratory environment. However, the widely varying formats of (the description of) these datasets make that data scientists need to invest heavily in interpreting the data and transforming it to a format that fit the model requirements. To reduce that effort and ensure a robust and consistent processing, this work proposes a standardized way of documenting such datasets. This ISA-PHM standard is based on the existing ISA metadata standard originating from life and biomedical sciences, that has been translated to the prognostics and health management (PHM) context. This is achieved by firstly structuring and generalizing the information required to document both diagnostic and prognostic (numerical or physical) experiments. This information is then carefully mapped to the ISA ontology, ensuring a complete and unambiguous documentation of any PHM-related test. The concept is demonstrated by application of ISA-PHM to three well-known public datasets (NLN-EMP, NASA milling data, CMAPSS) and a real failure. Finally, for the implementation, some practical software tools are presented (available on linked website) as well as the planned future extension towards collective data generation through distributed testing.
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Keywords
Data model, Prognostics, Diagnostics, test data
References
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Akkermans, H., Basten, R., Zhu, Q., & Van Wassenhove, L. (2024). Transition paths for condition based maintenance driven smart services. Journal of Operations Management. https://doi.org/10.1002/joom.1295
Alves da Silveira, N. N. (2025). Guiding predictive maintenance: A structured framework for approach selection and data quality assessment PhD thesis, University of Twente. Enschede, The Netherlands.
Arias Chao, M., Kulkarni, C., Goebel, K., & Fink, O. (2021). Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics. Data, 6(1). https://doi.org/10.3390/data6010005
Bird, J., & Shao, G. (2013). A view of standards for Prognostics and Health Management. International Journal of Prognostics and Health Management, 4(2). https://doi.org/https://doi.org/10.36001/ijphm.2013.v4i2.2055
Bruinsma, S., Geertsma, R. D., Loendersloot, R., & Tinga, T. (2024). Motor current and vibration monitoring dataset for various faults in an e-motor-driven centrifugal pump. Data in Brief, 52, 1-16, Article 109987. https://doi.org/https://doi.org/10.1016/j.dib.2023.109987
Corrêa, D., Polpo, A., Small, M., Srikanth, S., Hollins, K., & Hodkiewicz, M. (2022). Data-driven approach for labelling process plant event data. International Journal of Prognostics and Health Management, 13(1). https://doi.org/10.36001/ijphm.2022.v13i1.3045
de Jonge, B., & Scarf, P. A. (2020). A review on maintenance optimization. European Journal of Operational Research, 285(3), 805-824. https://doi.org/10.1016/j.ejor.2019.09.047
de Pater, I., Reijns, A., & Mitici, M. (2022). Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics. Reliability Engineering & System Safety, 221. https://doi.org/10.1016/j.ress.2022.108341
Grubic, T., Redding, L., Baines, T., & Julien, D. (2011). The adoption and use of diagnostic and prognostic technology within UK-based manufacturers. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(8), 1457-1470. https://doi.org/10.1177/0954405410395858
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). https://doi.org/10.36001/ijphm.2021.v12i2.3087
ISA-community. (2016). ISA model and serialization specifications 1.0. In. Zenodo. http://doi.org/10.5281/zenodo.163640: Sansone, Susanna-Assunta, Rocca-Serra, Philippe, Gonzalez-Beltran, Alejandra, Johnson, David.
ISA-specs. (2016). ISA Model and Serialization Specifications. https://isa-specs.readthedocs.io/en/latest/index.html
ISA-tools. (2014). ISA-tools. https://isa-tools.org/index.html
Keizers, L. S., Loendersloot, R., & Tinga, T. (2025a). Bayesian filtering based prognostic framework incorporating varying loads. Mechanical Systems and Signal Processing, 224. https://doi.org/10.1016/j.ymssp.2024.111992
Keizers, L. S., Loendersloot, R., & Tinga, T. (2025b). Bond graphs for quantitative fault diagnostics to support Bayesian filtering-based failure prognostics. Mechanical Systems and Signal Processing, 237. https://doi.org/10.1016/j.ymssp.2025.113093
Khan, S., & Yairi, T. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107, 241-265. https://doi.org/10.1016/j.ymssp.2017.11.024
Lehmann, F. A., Hille, F., & Glišić, B. (2025). Global perspectives on structural monitoring in civil engineering 13th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Graz, Austria.
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 35th European Safety and Reliability Conference (ESREL 2025), Stavanger.
MIMOSA. (2013). Open System Architecture for Condition-Based Maintenance, V3.3.1. In: MIMOSA (http://www.mimosa.org).
Omri, N., Al Masry, Z., Giampiccolo, S., Mairot, N., & Zerhouni, N. (2019). Data management requirements for PHM implementation in SMEs International Conference on Prognostics and Health Management, Paris, France.
Omri, N., Al Masry, Z., Mairot, N., Giampiccolo, S., & Zerhouni, N. (2021). Towards an adapted PHM approach: Data quality requirements methodology for fault detection applications. Computers in Industry, 127. https://doi.org/10.1016/j.compind.2021.103414
Pinciroli, L., Baraldi, P., & Zio, E. (2023). Maintenance optimization in industry 4.0. Reliability engineering & system safety, 234. https://doi.org/10.1016/j.ress.2023.109204
Rijsdijk, C., Wijnckel, M. J. R. v. d., & Tinga, T. (2024). A maturity framework for data driven maintenance European Conference of the Prognostics and Health Management Society, Prague, Czech Republic.
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine prognostics. International Conference on Prognostics and Health Management,
Schultes, E., Magagna, B., Hettne, K. M., Pergl, R., Suchánek, M., & Kuhn, T. (2020). Reusable FAIR implementation profiles as accelerators of FAIR convergence. In Advances in Conceptual Modeling (pp. 138-147). https://doi.org/10.1007/978-3-030-65847-2_13
Tamssaouet, F., Nguyen, K. T. P., Medjaher, K., & Orchard, M. E. (2022). System-level failure prognostics: Literature review and main challenges. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 237(3), 524-545. https://doi.org/10.1177/1748006x221118448
Tiddens, W. W., Braaksma, J., & Tinga, T. (2022). Exploring predictive maintenance applications in industry. Journal of Quality in Maintenance Engineering, 28(1), 68-85. https://doi.org/10.1108/JQME-05-2020-0029
Tinga, T. (2025). Practical application of predictive maintenance - solving the challenges 35th European Safety and Reliability Conference, Stavanger, Norway.
Tsalapati, E., & Koubarakis, M. (2023). SHM: A light-weight, mid-level ontology for reliable system health monitoring SemIIM’23: 2nd International Workshop on Semantic Industrial Information Modelling, Athens.
Tsialiamanis, G., Wagg, D. J. W., Antoniadou, I., & Worden, K. (2021). An ontological approach to Structural Health Monitoring. In B. Dilworth & M. Mains (Eds.), Topics in Modal Analysis & Testing (Vol. 8). River Publishers. https://doi.org/10.1007/978-3-030-47717-2_6
Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). Standards Related to Prognostics and Health Management (PHM) for Manufacturing (NISTIR 8012). N. I. o. S. a. Technology.
Williams, T., Ribadeneira, X., Billington, S., & Kurfess, T. (2001). Rolling element bearing diagnostics in run-to-failure lifetime testing. Mechanical Systems and Signal Processing, 15(5), 979-993. https://doi.org/10.1006/mssp.2001.1418
Zaghdoudi, M. A., Varnier, C., Hajri-Gabouj, S., & Zerhouni, N. (2024). A novel approach for evaluating datasets similarities based on Analytical Hierarchy Process in the industrial PHM context 8th European Conference of the Prognostics and Health Management Society, Prague.
Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., Silveira da Trindade, E., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150(106889), 1-17.
Akkermans, H., Basten, R., Zhu, Q., & Van Wassenhove, L. (2024). Transition paths for condition based maintenance driven smart services. Journal of Operations Management. https://doi.org/10.1002/joom.1295
Alves da Silveira, N. N. (2025). Guiding predictive maintenance: A structured framework for approach selection and data quality assessment PhD thesis, University of Twente. Enschede, The Netherlands.
Arias Chao, M., Kulkarni, C., Goebel, K., & Fink, O. (2021). Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics. Data, 6(1). https://doi.org/10.3390/data6010005
Bird, J., & Shao, G. (2013). A view of standards for Prognostics and Health Management. International Journal of Prognostics and Health Management, 4(2). https://doi.org/https://doi.org/10.36001/ijphm.2013.v4i2.2055
Bruinsma, S., Geertsma, R. D., Loendersloot, R., & Tinga, T. (2024). Motor current and vibration monitoring dataset for various faults in an e-motor-driven centrifugal pump. Data in Brief, 52, 1-16, Article 109987. https://doi.org/https://doi.org/10.1016/j.dib.2023.109987
Corrêa, D., Polpo, A., Small, M., Srikanth, S., Hollins, K., & Hodkiewicz, M. (2022). Data-driven approach for labelling process plant event data. International Journal of Prognostics and Health Management, 13(1). https://doi.org/10.36001/ijphm.2022.v13i1.3045
de Jonge, B., & Scarf, P. A. (2020). A review on maintenance optimization. European Journal of Operational Research, 285(3), 805-824. https://doi.org/10.1016/j.ejor.2019.09.047
de Pater, I., Reijns, A., & Mitici, M. (2022). Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics. Reliability Engineering & System Safety, 221. https://doi.org/10.1016/j.ress.2022.108341
Grubic, T., Redding, L., Baines, T., & Julien, D. (2011). The adoption and use of diagnostic and prognostic technology within UK-based manufacturers. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(8), 1457-1470. https://doi.org/10.1177/0954405410395858
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). https://doi.org/10.36001/ijphm.2021.v12i2.3087
ISA-community. (2016). ISA model and serialization specifications 1.0. In. Zenodo. http://doi.org/10.5281/zenodo.163640: Sansone, Susanna-Assunta, Rocca-Serra, Philippe, Gonzalez-Beltran, Alejandra, Johnson, David.
ISA-specs. (2016). ISA Model and Serialization Specifications. https://isa-specs.readthedocs.io/en/latest/index.html
ISA-tools. (2014). ISA-tools. https://isa-tools.org/index.html
Keizers, L. S., Loendersloot, R., & Tinga, T. (2025a). Bayesian filtering based prognostic framework incorporating varying loads. Mechanical Systems and Signal Processing, 224. https://doi.org/10.1016/j.ymssp.2024.111992
Keizers, L. S., Loendersloot, R., & Tinga, T. (2025b). Bond graphs for quantitative fault diagnostics to support Bayesian filtering-based failure prognostics. Mechanical Systems and Signal Processing, 237. https://doi.org/10.1016/j.ymssp.2025.113093
Khan, S., & Yairi, T. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107, 241-265. https://doi.org/10.1016/j.ymssp.2017.11.024
Lehmann, F. A., Hille, F., & Glišić, B. (2025). Global perspectives on structural monitoring in civil engineering 13th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Graz, Austria.
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 35th European Safety and Reliability Conference (ESREL 2025), Stavanger.
MIMOSA. (2013). Open System Architecture for Condition-Based Maintenance, V3.3.1. In: MIMOSA (http://www.mimosa.org).
Omri, N., Al Masry, Z., Giampiccolo, S., Mairot, N., & Zerhouni, N. (2019). Data management requirements for PHM implementation in SMEs International Conference on Prognostics and Health Management, Paris, France.
Omri, N., Al Masry, Z., Mairot, N., Giampiccolo, S., & Zerhouni, N. (2021). Towards an adapted PHM approach: Data quality requirements methodology for fault detection applications. Computers in Industry, 127. https://doi.org/10.1016/j.compind.2021.103414
Pinciroli, L., Baraldi, P., & Zio, E. (2023). Maintenance optimization in industry 4.0. Reliability engineering & system safety, 234. https://doi.org/10.1016/j.ress.2023.109204
Rijsdijk, C., Wijnckel, M. J. R. v. d., & Tinga, T. (2024). A maturity framework for data driven maintenance European Conference of the Prognostics and Health Management Society, Prague, Czech Republic.
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine prognostics. International Conference on Prognostics and Health Management,
Schultes, E., Magagna, B., Hettne, K. M., Pergl, R., Suchánek, M., & Kuhn, T. (2020). Reusable FAIR implementation profiles as accelerators of FAIR convergence. In Advances in Conceptual Modeling (pp. 138-147). https://doi.org/10.1007/978-3-030-65847-2_13
Tamssaouet, F., Nguyen, K. T. P., Medjaher, K., & Orchard, M. E. (2022). System-level failure prognostics: Literature review and main challenges. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 237(3), 524-545. https://doi.org/10.1177/1748006x221118448
Tiddens, W. W., Braaksma, J., & Tinga, T. (2022). Exploring predictive maintenance applications in industry. Journal of Quality in Maintenance Engineering, 28(1), 68-85. https://doi.org/10.1108/JQME-05-2020-0029
Tinga, T. (2025). Practical application of predictive maintenance - solving the challenges 35th European Safety and Reliability Conference, Stavanger, Norway.
Tsalapati, E., & Koubarakis, M. (2023). SHM: A light-weight, mid-level ontology for reliable system health monitoring SemIIM’23: 2nd International Workshop on Semantic Industrial Information Modelling, Athens.
Tsialiamanis, G., Wagg, D. J. W., Antoniadou, I., & Worden, K. (2021). An ontological approach to Structural Health Monitoring. In B. Dilworth & M. Mains (Eds.), Topics in Modal Analysis & Testing (Vol. 8). River Publishers. https://doi.org/10.1007/978-3-030-47717-2_6
Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). Standards Related to Prognostics and Health Management (PHM) for Manufacturing (NISTIR 8012). N. I. o. S. a. Technology.
Williams, T., Ribadeneira, X., Billington, S., & Kurfess, T. (2001). Rolling element bearing diagnostics in run-to-failure lifetime testing. Mechanical Systems and Signal Processing, 15(5), 979-993. https://doi.org/10.1006/mssp.2001.1418
Zaghdoudi, M. A., Varnier, C., Hajri-Gabouj, S., & Zerhouni, N. (2024). A novel approach for evaluating datasets similarities based on Analytical Hierarchy Process in the industrial PHM context 8th European Conference of the Prognostics and Health Management Society, Prague.
Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., Silveira da Trindade, E., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150(106889), 1-17.
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