Abstract
Degradation processes are implicated in a large number of system failures, and are thus crucial to understanding issues related to reliability and safety. Systems typically degrade in response to stressors, such as physical or chemical environmental conditions, which can vary widely for identical units that are deployed in different places or for different uses. This situational variance makes it difficult to develop accurate physics-based or data-driven models to assess and predict the system health status of individual components. To address this issue, we propose a fuzzy set model for representing degradation in engineered systems that is based on a bioinspired concept from the field of epigenetics. Epigenetics is concerned with the regulation of gene expression resulting from environmental or other factors, such as toxicants or diet. One of the most studied epigenetic processes is methylation, which involves the attachment of methyl groups to genomic regulatory regions. Methylation of specific genes has been implicated in numerous chronic diseases, and thus provides an excellent analog to system degradation. In this paper, we present a fuzzy set model for characterizing system degradation as a methylation process based on a set-theoretic representation for epigenetic modeling of engineered systems. This model allows us to capture the individual dynamic relationships among a system, environmental factors, and state of health. We demonstrate application of the model on a use case of corrosion of a metal specimen.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Smith, R.J.: Engineering, https://www.britannica.com/technology/engineering. Last accessed 12 July 2021
Goodman, D., Hofmeister, J., Szidarovszky, F.: Prognostics and Health Management: A Practical Approach to Improving System Reliability Using Condition-Based Data. John Wiley & Sons, Ltd, Hoboken, NJ (2019)
Kothamasu, R., Huang, S.H., VerDuin, W.H.: System health monitoring and prognostics – a review of current paradigms and practices. In: Ben-Daya, M., Duffuaa, S.O., Raouf, A., Knezevic, J., Ait-Kadi, D. (eds.) Handbook of Maintenance Management and Engineering, pp. 337–362. Springer, London, London (2009)
Geobel, K., Celaya, J., Sankararaman, S., Roychoudhury, I., Daigle, M., Saxena, A.: Prognostics: The Science of Making Predictions. CreateSpace Independent Publishing Platform (2017)
Meeker, W., Hong, Y., Escobar, L.: Degradation models and analyses. In: Encyclopedia of Statistical Sciences. John Wiley & Sons (2011)
Seale, M.A., Garcia-Reyero, N., Salter, R.C., Ruvinsky, A.: An epigenetic modeling approach for adaptive prognostics of engineered systems. Procedia Comput. Sci. 185, 311–319 (2021)
Rozenberg, G., Back, T., Kok, J.N.: Handbook of Natural Computing. Springer, Berlin Heidelberg (2012)
Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958)
Holland, J.H.: Genetic algorithms. Sci. Am. 66–72 (1992)
Stolfi, D.H., Alba, E.: Epigenetic algorithms: a new way of building GAs based on epigenetics. Inf. Sci. (Ny) 424, 250–272 (2018)
Birogul, S.: EpiGenetic algorithm for optimization: application to mobile network frequency planning. Arab. J. Sci. Eng. 41(3), 883–896 (2016)
Ricalde, E.: A genetic programming system with an epigenetic mechanism for traffic signal control. PhD thesis. Memorial University of Newfoundland. http://arxiv.org/abs/1903.03854 (2019)
Leenen, F.A.D., Muller, C.P., Turner, J.D.: DNA methylation: conducting the orchestra from exposure to phenotype? Clin. Epigenetics. 8, 1–15 (2016)
Rogers, K., Fridovich-Keil, Judith, L.: Epigenetics. https://www.britannica.com/science/epigenetics. Last accessed 20 July 2021
Moore, L.D., Le, T., Fan, G.: DNA methylation and its basic function. Neuropsychopharmacology 38, 23–38 (2013)
Sjahputera, O., et al.: Relational analysis of CpG islands methylation and gene expression in human lymphomas using possibilistic C-means clustering and modified cluster fuzzy density. IEEE/ACM Trans. Comput. Biol. Bioinforma. 4, 176–188 (2007)
Bouras, E., Karakioulaki, M., Bougioukas, K.I., Aivaliotis, M., Tzimagiorgis, G., Chourdakis, M.: Gene promoter methylation and cancer: an umbrella review. Gene 710, 333–340 (2019)
O’Connor, P.P., Kleyner, A.: Practical Reliability Engineering. Wiley Publishing, Chichester (2012)
Ebeling, C.E.: An Introduction to Reliability and Maintainability Engineering. McGraw-Hill (2004)
Verma, A.K., Ajit, S., Karanki, D.R.: Reliability and Safety Engineering: Second Edition. Springer, London (2016)
Tollefsbol, T.O.: Chapter 1 – Epigenetics: The New Science of Genetics. In: Tollefsbol, T. (ed.) Handbook of Epigenetics, pp. 1–6. Academic Press, San Diego (2011)
Sun, B., Zeng, S., Kang, R., Pecht, M.: Benefits analysis of prognostics in systems. In: 2010 Progn. Syst. Heal. Manag. Conf. PHM ’10 (2010)
Acknowledgments
The use of trade, product, or firm names in this document is for descriptive purposes only and does not imply endorsement by the U.S. Government. The tests described and the resulting data presented herein, unless otherwise noted, are based upon work conducted by the US Army Engineer Research and Development Center supported under PE 601102A, Project AB2, Task 04 ‘Unique Biological Processes and Data Analytics’. Permission was granted by the Computational Science and Engineering Division Chief, Information Technology Laboratory, to publish this information. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Seale, M., Salter, R.C., Garcia-Reyero, N., Ruvinsky, A. (2022). A Fuzzy Epigenetic Model for Representing Degradation in Engineered Systems. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 507. Springer, Cham. https://doi.org/10.1007/978-3-031-10464-0_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-10464-0_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-10463-3
Online ISBN: 978-3-031-10464-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)