Fleet Knowledge for Prognostics and Health Management – Identifying Fleet Dimensions and Characteristics for the Categorization of Fleets



Published Oct 2, 2017
Carolin Wagner Bernd Hellingrath


Current prognostics and health management approaches are often not able to meet expectations due to their limited ability to accurately detect abnormal machine conditions, identify failures and estimate the remaining useful life. This is in many cases attributed to the lack of real data and knowledge about the component or machine under consideration. Instead, experimental data is often used for algorithm training, which is not able to reflect the complexity of real-world systems. To improve prognostics and health management approaches condition data from fleets of machines rather than single units can be taken into consideration. Therefor machine conditions are assessed against situations encountered by machines in the same fleet and knowledge is transferred to allow algorithms to intelligently learn and improve their capabilities.
Several approaches have recently been presented in the literature, which make use of the fleet knowledge for condition-based maintenance. These approaches are designed for specific fleet compositions and characteristics. Therefore, in order to incorporate fleet knowledge into diagnostic and prognostic approaches the fleet under consideration and resulting requirements have to be analyzed. With this information, it is possible to determine whether fleet-based approaches are applicable in general to the specific case as well as facilitate the selection of a suitable fleet-based approach. Three types of fleets are distinguished in the literature, namely identical, homogeneous and heterogeneous fleets. This distinction makes reference to the structural dimension of fleets. For fleet-based approaches, however additional dimensions should be taken into account. These include among others the operating condition in the fleet (e.g. identical, different, or dynamically changing) and the type of available data (e.g. sensor reading, context data, textual description). This paper aims at identifying and analyzing different dimensions and respective characteristics of fleets to be considered in the context of prognostics and health management. The results are synthesized in a classification structure to support the categorization of fleets.

How to Cite

Wagner, C., & Hellingrath, B. (2017). Fleet Knowledge for Prognostics and Health Management – Identifying Fleet Dimensions and Characteristics for the Categorization of Fleets. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2395
Abstract 251 | PDF Downloads 223




Agarwal, V., Lybeck, N. J., Pham, B. T., Bickford, R., & Rusaw, R. (2015). Implementation of Remaining Useful Lifetime Transformer Models in the Fleet-Wide Prognostic and Health Management Suite. In Proceedings of 9th International Conference on Nuclear Plant Instrumentation, Control, & Human-Machine Interface Technologies (NPIC-HMIT) (pp. 23–26).
Agarwal, V., Lybeck, N. J., Pham, B. T., Rusaw, R., & Bickford, R. (2012). Prognostic and Health Management of Active Assets in Nuclear Power Plants.
Al-Dahidi, S., Di Maio, F., Baraldi, P., & Zio, E. (2016). Remaining useful life estimation in heterogeneous fleets working under variable operating conditions. Reliability Engineering & System Safety, 156, 109–124. https://doi.org/10.1016/j.ress.2016.07.019
Al-Dahidi, S., Di Maio, F., Baraldi, P., & Zio, E. (2017). A switching ensemble approach for remaining useful life estimation of electrolytic capacitors. In L. Walls, M. Revie, & T. Bedford (Eds.), Risk, reliability and safety. Innovating theory and practice (pp. 2000–2005). London: Taylor & Francis Group. https://doi.org/10.1201/9781315374987-303
Bagheri, B., Siegel, D., Zhao, W., & Lee, J. (2015). A Stochastic Asset Life Prediction Method for Large Fleet Datasets in Big Data Environment.
Bonissone, P. P., & Varma, A. (2005, May). Predicting the Best Units within a Fleet: Prognostic Capabilities Enabled by Peer Learning, Fuzzy Similarity, and Evolutionary Design Process. In The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05 (pp. 312–318). https://doi.org/10.1109/FUZZY.2005.1452412
Bonissone, P. P., Varma, A., & Aggour, K. S. (2005, July). A fuzzy instance-based model for predicting expected life: a locomotive application. In CIMSA. 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2005 (pp. 20–25). https://doi.org/10.1109/CIMSA.2005.1522819
Byttner, S., Rögnvaldsson, T., & Svensson, M. (2011). Consensus self-organized models for fault detection (COSMO). Engineering Applications of Artificial Intelligence, 24(5), 833–839. https://doi.org/10.1016/j.engappai.2011.03.002
Cristaldi, L., Leone, G., Ottoboni, R., Subbiah, S., & Turrin, S. (2016). A comparative study on data-driven prognostic approaches using fleet knowledge, 1–6. https://doi.org/10.1109/I2MTC.2016.7520371
Fan, Y., Nowaczyk, S., & Rögnvaldsson, T. (2015). Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet. Procedia Computer Science, 53, 447–456. https://doi.org/10.1016/j.procs.2015.07.322
Fang, B., Hongfu, Z., & Shuhong, R. (2010). Average life prediction for aero-engine fleet based on performance degradation data. In IEEE Prognostics & System Health Management Conference 2010. PHM-2010 Macau, 12-14 January, 2010, University of Macau, P.R. China (pp. 1–6). [Piscataway, N.J.]: IEEE. https://doi.org/10.1109/PHM.2010.5414574
Gebraeel, N. (2010). Prognostics-Based Identification of the Top-k Units in a Fleet. IEEE Transactions on Automation Science and Engineering, 7(1), 37–48. https://doi.org/10.1109/TASE.2009.2023209
Ghodrati, B. (2005). Reliability and Operating Environment Based Spare Parts Planning.
Guepie, B. K., & Lecoeuche, S. (2015). Similarity-based residual useful life prediction for partially unknown cycle varying degradation, 1–7. https://doi.org/10.1109/ICPHM.2015.7245054
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. https://doi.org/10.1016/j.ymssp.2005.09.012
Krause, J., Cech, S., Rosenthal, F., Gossling, A., Groba, C., & Vasyutynskyy, V. (2010). Factory-wide predictive maintenance in heterogeneous environments. In J. Proenza (Ed.), 8th IEEE International Workshop on Factory Communication Systems (WFCS), 2010. Nancy, France, 18 - 21 May 2010 (pp. 153–156). Piscataway, NJ: IEEE. https://doi.org/10.1109/WFCS.2010.5548616
Lapira, E. (2012). Fault Detection in a Network of Similar Machines using Clustering Approach. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1339250832
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1–2), 314–334.
Léger, J.-B., & Iung, B. (2012). Ships Fleet-wide Management and Naval Mission Prognostics: Lessons learned and new Issues: 18 - 21 June 2012, Denver, Colorado. IEEE Conference on Prognostics and Health Management (PHM), 2012. Retrieved from http://ieeexplore.ieee.org/ielx5/6294524/6299504/06299531.pdf?tp=&arnumber=6299531&isnumber=6299504
Leone, G., Cristaldi, L., & Turrin, S. (2017). A data-driven prognostic approach based on statistical similarity: An application to industrial circuit breakers. Measurement. Advance online publication. https://doi.org/10.1016/j.measurement.2017.02.017
Liu, J., Djurdjanovic, D., Ni, J., Casoetto, N., & Lee, J. (2007). Similarity based method for manufacturing process performance prediction and diagnosis. Computers in Industry, 58(6), 558–566. https://doi.org/10.1016/j.compind.2006.12.004
Liu, J., & Zio, E. (2016). A framework for asset prognostics from fleet data. In M. J. Zuo (Ed.), Proceedings of 2016 Prognostics and System Health Management Conference (PHM-Chengdu). October 19-21, 2016, Chengdu, Sichuan, China (pp. 1–5). Piscataway, NJ: IEEE. https://doi.org/10.1109/PHM.2016.7819824
Medina-Oliva, G., Voisin, A., Monnin, M., & Léger, J.-B. (2014). Predictive diagnosis based on a fleet-wide ontology approach. Knowledge-Based Systems, 68, 40–57. https://doi.org/10.1016/j.knosys.2013.12.020
Medina-Oliva, G., Voisin, A., Monnin, M., Peysson, F., & Léger, J.-B. (2012). Prognostics assessment using fleet-wide ontology. In Annual Conference of the Prognostics and Health Management Society 2012, PHM Conference 2012 (CDROM). Minneapolis, Minnesota, United States. Retrieved from https://hal.archives-ouvertes.fr/hal-00748697
Merriam-Webster, I. Definition of FLEET. Retrieved from https://www.merriam-webster.com/dictionary/fleet
Monnin, M., Abichou, B., Voisin, A., & Mozzati, C. (2011). Fleet historical cases for predictive maintenance. In International Conference on acoustical and vibratory methods in surveillance and diagnostics, Surveillance 6 (CDROM). Compiègne, France. Retrieved from https://hal.archives-ouvertes.fr/hal-00638399
Monnin, M., Voisin, A., Léger, J.-B., & Iung, B. (2011). Fleet-wide health management architecture. Retrieved from https://ftp.phmsociety.org/sites/phmsociety.org/files/phm_submission/2011/phmc_11_061.pdf
Oxford Dictionaries. fleet - definition of fleet in English. Retrieved from https://en.oxforddictionaries.com/definition/fleet
Saxena, A., Wu, B., & Vachtsevanos, G. (2005). Integrated Diagnosis and Prognosis Architecture for Fleet Vehicles Using Dynamic Case-Based Reasoning. Retrieved from http://ieeexplore.ieee.org/ielx5/10700/33790/01609109.pdf?tp=&arnumber=1609109&isnumber=33790
Saxena, A., & Goebel, K. (2008). PHM08 Challenge Data Set. Retrieved from NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository)
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation, 1–9. https://doi.org/10.1109/PHM.2008.4711414
Saxena, A., Sankararaman, S., & Goebel, K. (2014). Performance Evaluation for Fleet-based and Unit-based Prognostic Methods.
Schneider, K., & Cassady, C. R. (2004, January). Fleet performance under selective maintenance. In Annual Symposium Reliability and Maintainability, 2004 - RAMS (pp. 571–576). https://doi.org/10.1109/RAMS.2004.1285508
Subbiah, S., & Turrin, S. (2015). Extraction and exploitation of R&M knowledge from a fleet perspective. In 2015 Annual Reliability and Maintainability Symposium (RAMS) (pp. 1–6). https://doi.org/10.1109/RAMS.2015.7105072
Technical Research Papers