Creation of Publicly Available Data Sets for Prognostics and Diagnostics Addressing Data Scenarios Relevant to Industrial Applications



Published Nov 10, 2021
Simon Hagmeyer
Fabian Mauthe
Peter Zeiler


For a successful realization of prognostics and health management (PHM), the availability of sufficient run-to-failure data sets is a crucial factor. The sheer number of given data points holds less importance than the full coverage of the potential state space. However, full coverage is a major challenge in most industrial applications. Among other things, high investment and operating costs as well as the long service life of many technical systems make it difficult to acquire complete run-to-failure data sets. Consequently, in industrial applications data sets with specific deficiencies are frequently encountered. The development of appropriate methods to address such data scenarios is a fundamental research issue. Therefore, the purpose of this paper is to provide facilitation for this research. Accordingly, the paper starts by specifying the value and availability of data in PHM. Subsequently, criteria for characterizing data sets are defined independent of the actual PHM application. The criteria are used to identify typical data scenarios with specific deficiencies that possess significant relevance for industrial applications. Thereafter, the most comprehensive overview of data sets suitable for PHM and currently publicly accessible is provided. Thereby, not all previously identified data scenarios with their specific deficiencies are addressed by at least one data set. A program is established for the aforementioned facilitation of further research. One objective of the program is to create data sets reflecting these data scenarios using a test bench. First, possible applications and their degradation processes to be studied on the test bench are briefly characterized. Thereby, the final decision to select filtration as a test bench application is argued. Subsequently, the test bench created is introduced, including a description of the functional concept, pneumatic layout and components involved, as well as the filter media and test dusts employed. Typical run-to-failure trajectories are illustrated. Thereafter, the data set published under the name Preventive to Predictive Maintenance is presented. Additionally, a schedule for future releases of data sets on further industry-relevant data scenarios is sketched.

Abstract 1998 | PDF Downloads 2523



PHM, Filtration, Data Set, Overview, Data Characteristics, Data Set Criteria, Industry Relevant Data

Abdolghader, P., Brochot, C., Haghighat, F., & Bahloul, A. (2018). Airborne nanoparticles filtration performance of fibrous media: A review. Science and Technology for the Built Environment, 24(6), pp. 648-672. doi:10.1080/23744731.2018.1452454
Aizpurua, J. I., & Catterson, V. M. (2015). Towards a Methodology for Design of Prognostic Systems. Proceedings of the Annual Conference of the Prognostics and Health Management Society, 7. Coronado, USA. doi:10.36001/phmconf.2015.v7i1.2646
Aizpurua, J. I., & Catterson, V. M. (2016). ADEPS: A Methodology for Designing Prognostic Applications. Proceedings of the European Conference of the Prognostics and Health Management Society. Bilbao, Spain. doi:10.36001/phme.2016.v3i1.1585
An, D., Choi, J.-H., & Kim, N. H. (2013). Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab. Reliability Engineering & System Safety, 115, pp. 161-169. doi:10.1016/j.ress.2013.02.019
Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Zerhouni, N. (2017). Prognostics and Health Management for Maintenance Practitioners - Review, Implementation and Tools Evaluation. International Journal of Prognostics and Health Management, 8(3). doi:10.36001/ijphm.2017.v8i3.2667
Bergman, W., Taylor, R. D., Miller, H. H., Bierman, A. H., Hebard, H. D., daRoza, R. A., & Lum, B. Y. (1978). Enhanced filtration program at LLL. A progress report. Proceedings of the 15th Nuclear Air Cleaning Conference. Boston, USA.
Cachada, A., Moreira, P. M., Romero, L., Barbosa, J., Leitno, P., Gcraldcs, C. A., . . . Moreira, A. H. (2018). Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture. Proceedings of the IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), (pp. 139–146). Turin, Italy. doi:10.1109/ETFA.2018.8502489
Cannarile, F., Baraldi, P., & Zio, E. (2019). An evidential similarity-based regression method for the prediction of equipment remaining useful life in presence of incomplete degradation trajectories. Fuzzy Sets and Systems, 367, pp. 36-50. doi:10.1016/j.fss.2018.10.008
Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2021). Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics. Data, 6(1). doi:10.3390/data6010005
Chase, G. G., Beniwal, V., & Venkataraman, C. (2000). Measurement of uni-axial fiber angle in non-woven fibrous media. Chemical Engineering Science, 55(12), pp. 2151-2160. doi:10.1016/S0009-2509(99)00510-2
Cheng, S., Raghavan, N., Gu, J., Mathew, S., & Pecht, M. (2018). Physics-of-failure approach to PHM. In Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things. Hoboken, USA: JohnWiley and Sons Ltd.
Chi, Z., Lin, J., Chen, R., & Huang, S. (2020). Data-driven approach to study the polygonization of high-speed railway train wheel-sets using field data of China's HSR train. Measurement, 149. doi:10.1016/j.measurement.2019.107022
Chikhi, N., Clavier, R., Laurent, J.-P., Fichot, F., & Quintard, M. (2016). Pressure drop and average void fraction measurements for two-phase flow through highly permeable porous media. Annals of Nuclear Energy, 94, pp. 422-432. doi:10.1016/j.anucene.2016.04.007
Cubillo, A., Perinpanayagam, S., & Esperon-Miguez, M. (2016). A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery. Advances in Mechanical Engineering, 8(8), pp. 1–21. doi:10.1177/1687814016664660
Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., & Barbosa, J. (2020). Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Computers in Industry, 123, p. 103298. doi:10.1016/j.compind.2020.103298
Dong, W., Liu, S., & Du, Y. (2019). Optimal periodic maintenance policies for a parallel redundant system with component dependencies. Computers & Industrial Engineering, 138, p. 106133. doi:10.1016/j.cie.2019.106133
Eker, O. F., Camci, F., & Jennions, I. K. (2016). Physics-based prognostic modelling of filter clogging phenomena. Mechanical Systems and Signal Processing, 75, pp. 395-412. doi:10.1016/j.ymssp.2015.12.011
Elattar, H. M., Elminir, H. K., & Riad, A. M. (2016). Prognostics: a literature review. Complex & Intelligent Systems, 2, pp. 125-154. doi:10.1007/s40747-016-0019-3
Ge, M., Du, R., Zhang, G., & Xu, Y. (2004). Fault diagnosis using support vector machine with an application in sheet metal stamping operations. Mechanical Systems and Signal Processing, 18(1), pp. 143 -159. doi:10.1016/S0888-3270(03)00071-2
Goebel, K., Daigle, M., Saxena, A., Sankararaman, S., Roychoudhury, I., & Celaya, J. (2017). Prognostics: The science of making predictions. CreateSpace Independent Publishing Platform.
Gu, J., Barker, D., & Pecht, M. (2007). Prognostics implementation of electronics under vibration loading. Microelectronics Reliability, 47(12), pp. 1849 - 1856. doi:10.1016/j.microrel.2007.02.015
Hagmeyer, S., Mauthe, F., Dutt, M., & Zeiler, P. (2021). Preventive to predictive maintenance., DOI: 10.34740/kaggle/dsv/2339298.
Hemmer, M., Klausen, A., van Khang, H., Robbersmyr, K., & Waag, T. (2019). Simulation-driven Deep Classification of Bearing Faults from Raw Vibration Data. International Journal of Prognostics and Health Management, 10(4). doi:10.36001/ijphm.2019.v10i4.2615
Huang, B., Di, Y., Jin, C., & Lee, J. (2017). Review of data-driven prognostics and health management techniques: lessions learned from PHM data challenge competitions. Proceedings of the Conference Machine Failure Prevention Technology. Virginia Beach, USA.
Huang, C.-Y., & Dzulfikri, Z. (2021). Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network. Sensors, 21(1). doi:10.3390/s21010262
Javed, K., Gouriveau, R., & Zerhouni, N. (2017). State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels. Mechanical Systems and Signal Processing, 94, pp. 214–236. doi:10.1016/j.ymssp.2017.01.050
Jia, X., Huang, B., Feng, J., Cai, H., & Lee, J. (2018). Review of PHM Data Competitions from 2008 to 2017. Proceedings of the Annual Conference of the PHM Society, 10. Philadelphia, USA. doi:10.36001/phmconf.2018.v10i1.462
Kaggle: Your Machine Learning and Data Science Community. (2021). Retrieved 06 17, 2021, from
Kahn, B. K., Strong, D. M., & Wang, R. Y. (2002). Information quality benchmarks: product and service performance. Communications of the ACM, 45(4), pp. 184-192. doi:10.1145/505248.506007
Kim, N.-H., An, D., & Choi, J.-H. (2016). Prognostics and health management of engineering systems: An introduction. Berlin, Heidelberg: Springer.
Kundu, P., Darpe, A. K., & Kulkarni, M. S. (2020). A review on diagnostic and prognostic approaches for gears. Structural Health Monitoring. doi:10.1177/1475921720972926
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, pp. 799–834. doi:10.1016/j.ymssp.2017.11.016
Mendes, A. A., Coit, D. W., & Ribeiro, J. L. (2014). Establishment of the optimal time interval between periodic inspections for redundant systems. Reliability Engineering & System Safety, 131, pp. 148–165. doi:10.1016/j.ress.2014.06.021
Merino, J., Caballero, I., Rivas, B., Serrano, M., & Mario, P. (2016). A Data Quality in Use model for Big Data. Future Generation Computer Systems, 63, pp. 123-130. doi:10.1016/j.future.2015.11.024
Niculita, O., Irving, P., & Jennions, I. K. (2012). Use of COTS Functional Analysis Software as an IVHM Design Tool for Detection and Isolation of UAV Fuel System Faults. Proceedings of the Annual Conference of Prognostics and Health Management Society, 4. Minneapolis, USA. doi:10.36001/phmconf.2012.v4i1.2116
Novick, V., Higgins, P., Dierkschiede, B., Abrahamson, C., Richardson, W., Monson, P., & Ellison, P. (1990). Efficiency and mass loading characteristics of a typical HEPA filter media material. Proceedings of the 21st DOE/NRC Nuclear Air Cleaning Conference. San Diego, USA.
Ochella, S., & Shafiee, M. (2019). Artificial Intelligence in Prognostic Maintenance. Proceedings of the 29th European Safety and Reliability Conference (ESREL), (pp. 3424–3431). Hannover, Germany. doi:10.3850/978-981-11-2724-3_0188-cd
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. doi:10.1016/j.compind.2021.103414
PHM Society. (2021). The Prognostics and Health Management Society . Retrieved 06 16, 2021, from
Pillai, P., Kaushik, A., Bhavikatti, S., Roy, A., & Kumar, V. (2016). A Hybrid Approach for Fusing Physics and Data for Failure Prediction. International Journal of Prognostics and Health Management, 7(4). doi:10.36001/ijphm.2016.v7i4.2463
Popper, K. (1963). Conjectures and refutations : The growth of scientific knowledge. London, UK: Routledge and Kegan Paul.
Prognostics Center of Excellence - Data Repository. (2021). Retrieved 06 16, 2021, from
Ramasso, E., & Saxena, A. (2014). Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets. International Journal of Prognostics and Health Management, 5(2). doi:10.36001/ijphm.2014.v5i2.2236
Saarela, O., Hulsund, J. E., Taipale, A., & Hegle, M. (2014). Remaining Useful Life Estimation for Air Filters at a Nuclear Power Plant. Proceedings of the European Conference of the PHM Society. Nantes, France. doi:10.36001/phme.2014.v2i1.1493
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. Proceedings of the International Conference on Prognostics and Health Management. Denver, USA. doi:10.1109/PHM.2008.4711414
Selcuk, S. (2017). Predictive maintenance, its implementation and latest trends. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(9), pp. 1670-1679. doi:10.1177/0954405415601640
Shanbhag, V. V., Rolfe, B. F., Arunachalam, N., & Pereira, M. P. (2018). Investigating galling wear behaviour in sheet metal stamping using acoustic emissions. Wear, 414, pp. 31-42. doi:10.1016/j.wear.2018.07.003
Shanbhag, V., Pereira, M., Voss, B., Ubhayaratne, I., & Rolfe, B. (2019). Developing smart multi-sensor monitoring for tool wear in stamping process. Proceedings of the IOP Conference Series: Materials Science and Engineering 651. Enschede, Netherlands. doi:10.1088/1757-899X/651/1/012085
Sharma, V., & Parey, A. (2016). A Review of Gear Fault Diagnosis Using Various Condition Indicators. Procedia Engineering, 144, pp. 253-263. doi:10.1016/j.proeng.2016.05.131
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011). Remaining useful life estimation – A review on the statistical data driven approaches. European Journal of Operational Research, 213(1), pp. 1–14. doi:10.1016/j.ejor.2010.11.018
Skaf, Z., Eker, O. F., & Jennions, I. K. (2017). System Component Degradation: Filter Clogging in a UAV Fuel System. Proceedings of the Asia-Pacific Conference of the PHM Society, (pp. 334-342). Jeju, Republik Korea.
Song, C. B., Park, H. S., & Lee, K. W. (2006). Experimental study of filter clogging with monodisperse PSL particles. Powder Technology, 163(3), pp. 152-159. doi:10.1016/j.powtec.2006.01.016
Sparks, T., & Chase, G. (2015). Filters and filtration handbook (6th ed.). Stanford: Elsevier Science.
Sreenuch, T., Khan, F., & Li, J. (2015). Particle Filter with Operational-Scalable Takagi–Sugeno Fuzzy Degradation Model for Filter-Clogging Prognosis. Journal of Aerospace Information Systems, 12(5), pp. 398-412. doi:10.2514/1.I010385
Statista Research Department. (2019). Size of the global industrial air filtration market between 2014 and 2025. Retrieved April 12, 2021.
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), pp. 3549-3561. doi:10.1016/S0009-2509(01)00041-0
Tobon-Mejia, D. A., Medjaher, K., & Zerhouni, N. (2012). CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks. Mechanical Systems and Signal Processing, 28, pp. 167–182. doi:10.1016/j.ymssp.2011.10.018
TV, V., Diksha, Malhotra, P., Vig, L., & Shroff, G. (2019). Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models. International Journal of Prognostics and Health Management, 10(4). doi:10.36001/ijphm.2019.v10i4.2612
UCI Machine Learning Repository. (2021). Retrieved 06 17, 2021, from
Uckun, S., Goebel, K., & Lucas, P. J. (2008). Standardizing research methods for prognostics. Proceedings of the International Conference on Prognostics and Health Management. Denver, USA. doi:10.1109/PHM.2008.4711437
Večeř, P., Kreidl, M., & Šmíd, R. (2005). Condition Indicators for Gearbox Condition Monitoring Systems. Acta Polytechnica, 45(6), pp. 35-43. doi:10.14311/782
Wang, L., Chu, J., & Wu, J. (2007). Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process. International Journal of Production Economics, 107(1), pp. 151-163. doi:10.1016/j.ijpe.2006.08.005
Wang, R. Y., Storey, V. C., & Firth, C. P. (1995). A Framework for Analysis of Data Quality Research. IEEE Transactions on Knowledge and Data Engineering, 7(4), pp. 623-640. doi:10.1109/69.404034
Weiss, B., & Brundage, M. (2021). Measurement and Evaluation for Prognostics and Health Management (PHM) for Manufacturing Operations – Summary of an Interactive Workshop Highlighting PHM Trends. International Journal of Prognostics and Health Management, 12(1). doi:10.36001/IJPHM.2021.v12i1.2653
Widodo, A., & Yang, B.-S. (2011). Machine health prognostics using survival probability and support vector machine. Expert Systems with Applications, 38(7), pp. 8430-8437. doi:10.1016/j.eswa.2011.01.038
Wiese, B., Pedersen, N. L., Nadimi, E. S., & Herp, J. (2020). Estimating the Remaining Power Generation of Wind Turbines–An Exploratory Study for Main Bearing Failures. Energies, 13(13). doi:10.3390/en13133406
Xu, M., Baraldi, P., Al-Dahidi, S., & Zio, E. (2019). Fault Prognostics in Presence of Event-Based Measurements. Proceedings of the 29th European Safety and Reliability Conference (ESREL), (pp. 1187-1193). Hannover, Germany. doi:10.3850/978-981-11-2724-3 0372-cd
Yang, G. (2007). Life Cycle Reliability Engineering. New York: John Wiley & Sons.
Zhu, J., Nostrand, T., Spiegel, C., & Morton, B. (2014). Survey of Condition Indicators for Condition Monitoring Systems. Proceedings of the Annual Conference of the PHM Society, 6. Fort Worth, USA. doi:10.36001/phmconf.2014.v6i1.2514
Technical Papers