Bridging the Gap: A Comparative Analysis of Regressive Remaining Useful Life Prediction and Survival Analysis Methods for Predictive Maintenance
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Published
Sep 4, 2023
Mahmoud Rahat
Zahra Kharazian
Peyman Sheikholharam Mashhadi
Thorsteinn Rögnvaldsson
Shamik Choudhury
Abstract
Regressive Remaining Useful Life Prediction and Survival Analysis are two lines of research with similar goals but different origins; one from engineering and the other from survival study in clinical research. Although the two research paths share a common objective of predicting the time to an event, researchers from each path typically do not compare their methods with methods from the other direction. Given the mentioned gap, we propose a framework to compare methods from the two lines of research using run-to-failure datasets. Then by utilizing the proposed framework, we compare six models incorporating three widely recognized degradation models along with two learning algorithms. The first dataset used in this study is C-MAPSS which includes simulation data from aircraft turbofan engines. The second dataset is real-world data from streamed condition monitoring of turbocharger devices installed on a fleet of Volvo trucks.
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Keywords
Remaining Useful Life (RUL) prediction, Survival Analysis, Data-Driven Predictive Maintenance, Regression, Machine Learning
References
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Rahat, M., Pashami, S., Nowaczyk, S., & Kharazian, Z. (2020). Modeling turbocharger failures using markov process for predictive maintenance. In 30th european safety and reliability conference (esrel2020) & 15th probabilistic safety assessment and management conference (psam15), venice, italy, 1-5 november, 2020.
Revanur, V., Ayibiowu, A., Rahat, M., & Khoshkangini, R. (2020). Embeddings based parallel stacked autoencoder approach for dimensionality reduction and predictive maintenance of vehicles. In Iot streams for data-driven predictive maintenance and iot, edge, and mobile for embedded machine learning: Second international workshop, iot streams 2020, and first international workshop, item 2020, co-located with ecml/pkdd 2020, ghent, belgium, september 14-18, 2020, revised selected papers 2 (pp. 127–141).
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine runto-failure simulation. In 2008 international conference on prognostics and health management (pp. 1–9).
Uno, H., Cai, T., Pencina, M. J., D’Agostino, R. B., & Wei, L.-J. (2011). On the c-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in medicine, 30(10), 1105– 1117.
Vieira, D., Gimenez, G., Marmerola, G., & Estima, V. (2021). Xgboost survival embeddings: improving statistical properties of xgboost survival analysis implementation.
Voronov, S., Frisk, E., & Krysander, M. (2018). Data-driven battery lifetime prediction and confidence estimation for heavy-duty trucks. IEEE Transactions on Reliability, 67(2), 623–639.
Yang, Z., Kanniainen, J., Krogerus, T., & Emmert-Streib, F. (2022). Prognostic modeling of predictive maintenance with survival analysis for mobile work equipment. Scientific Reports, 12(1), 1–20
Altarabichi, M. G., Sheikholharam Mashhadi, P., Fan, Y., Pashami, S., Nowaczyk, S., Del Moral, P., . . . Rognvaldsson, T. (2020). Stacking ensembles of heterogenous classifiers for fault detection in evolving environments. In 30th european safety and reliability conference, esrel 2020 and 15th probabilistic safety assessment and management conference, psam15 2020, venice, italy, 1-5 november, 2020 (pp. 1068–1068).
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., . . . Varoquaux, G. (2013). API design for machine learning software: experiences from the scikit-learn project. In Ecml pkdd workshop: Languages for data mining and machine learning (pp. 108–122).
Fotso, S., et al. (2019). PySurvival: Open source package for survival analysis modeling. Retrieved from https://www.pysurvival.io/
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189– 1232.
Goel, M. K., Khanna, P., & Kishore, J. (2010). Understanding survival analysis: Kaplan-meier estimate. International journal of Ayurveda research, 1(4), 274.
Hartman, N., Kim, S., He, K., & Kalbfleisch, J. D. (2023). Pitfalls of the concordance index for survival outcomes. Statistics in Medicine.
Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests.
Karlsson et al., N. (2023). Baseline selection for integrated gradients in predictive maintenance of volvo trucks’ turbocharger. In Vehicular 2023-iaria.
Polsterl, S. (2020). scikit-survival: A library for time-to-event analysis built on top of scikit-learn. Journal of Machine Learning Research, 21(212), 1-6. Retrieved from http://jmlr.org/papers/v21/20-729.html
Rahat, M., Mashhadi, P. S., Nowaczyk, S., Rognvaldsson, T., Taheri, A., & Abbasi, A. (2022). Domain adaptation in predicting turbocharger failures using vehicle’s sensor measurements. In Phm society european conference (Vol. 7, pp. 432–439).
Rahat, M., Pashami, S., Nowaczyk, S., & Kharazian, Z. (2020). Modeling turbocharger failures using markov process for predictive maintenance. In 30th european safety and reliability conference (esrel2020) & 15th probabilistic safety assessment and management conference (psam15), venice, italy, 1-5 november, 2020.
Revanur, V., Ayibiowu, A., Rahat, M., & Khoshkangini, R. (2020). Embeddings based parallel stacked autoencoder approach for dimensionality reduction and predictive maintenance of vehicles. In Iot streams for data-driven predictive maintenance and iot, edge, and mobile for embedded machine learning: Second international workshop, iot streams 2020, and first international workshop, item 2020, co-located with ecml/pkdd 2020, ghent, belgium, september 14-18, 2020, revised selected papers 2 (pp. 127–141).
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine runto-failure simulation. In 2008 international conference on prognostics and health management (pp. 1–9).
Uno, H., Cai, T., Pencina, M. J., D’Agostino, R. B., & Wei, L.-J. (2011). On the c-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in medicine, 30(10), 1105– 1117.
Vieira, D., Gimenez, G., Marmerola, G., & Estima, V. (2021). Xgboost survival embeddings: improving statistical properties of xgboost survival analysis implementation.
Voronov, S., Frisk, E., & Krysander, M. (2018). Data-driven battery lifetime prediction and confidence estimation for heavy-duty trucks. IEEE Transactions on Reliability, 67(2), 623–639.
Yang, Z., Kanniainen, J., Krogerus, T., & Emmert-Streib, F. (2022). Prognostic modeling of predictive maintenance with survival analysis for mobile work equipment. Scientific Reports, 12(1), 1–20
Section
Regular Session Papers
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