Abstract
The idea of information processing as a measure of health in a system engineering domain follows the cutting-edge area of research, widely known as prognostics and health management (PHM). The role of decision-making in an industry is one such technological advancement that uses remaining useful life (RUL) as a state of the health indicator for cognitive action. Correlations built using generic experimental data for a system helps in asset management. However, each system has a unique operating history of working in a complex environment. To account for this, degradation modelling-based prognosis that estimates the RUL of the target system by providing an estimate of usefulness in the life of the system contributes towards a much safer industry standard. This chapter aims to contribute in the development of stochastic decision-making using evolutionary techniques. The use of particle filter (PF) as an approach towards state estimation and RUL as a condition indicator (CI) for a degraded system is formulated. Analogy towards the cognitive decision-making has been explained to meet the desired expectation of the current theme in one of the sections. The presented prognostics method is thus validated using degraded motor data from industry. Accuracy in the results of the proposed technique is found to be information specific of the target system before the motor is jeopardized.
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References
An, D., Choi, J. H., & Kim, N. H. (2012). A tutorial for model-based prognostics algorithms based on Matlab code. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2012, PHM 2012 (pp. 224–232).
An, D., Choi, J. H., & Kim, N. H. (2018). Prediction of remaining useful life under different conditions using accelerated life testing data. Journal of Mechanical Science and Technology, 32(6), 2497–2507. https://doi.org/10.1007/s12206-018-0507-z
An, D., Kim, N. H., & Choi, J. H. (2015). Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliability Engineering and System Safety, 133, 223–236. https://doi.org/10.1016/j.ress.2014.09.014
Balka, J., Desmond, A. F., & McNicholas, P. D. (2009). Review and implementation of cure models based on first hitting times for Wiener processes. Lifetime Data Analysis, 15(2), 147–176. https://doi.org/10.1007/s10985-008-9108-y
Banjevic, D. (2009). Remaining useful life in theory and practice. Metrika, 69(2–3), 337–349. https://doi.org/10.1007/s00184-008-0220-5
Celaya, J. R., Saxena, A., Saha, S., & Goebel, K. F. (2014). Prognostics of power mosfets under thermal stress accelerated aging using data-driven and model-based methodologies. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011, PHM 2011 (pp. 443–452).
Gebraeel, N. (2006). Sensory-updated residual life distributions for components with exponential degradation patterns. IEEE Transactions on Automation Science and Engineering, 3(4), 382–393. https://doi.org/10.1109/TASE.2006.876609
He, D., Bechhoefer, E., Ma, J., & Zhu, J. (2012). A particle filtering based approach for gear prognostics. In Diagnostics and prognostics of engineering systems: Methods and techniques (pp. 257–266). Pennsylvania: IGI. https://doi.org/10.4018/978-1-4666-2095-7.ch013
Jardine, A. K. S., 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
Jouin, M., Gouriveau, R., Hissel, D., Péra, M. C., & Zerhouni, N. (2016). Particle filter-based prognostics: Review, discussion and perspectives. Mechanical Systems and Signal Processing, 72–73, 2–31. https://doi.org/10.1016/j.ymssp.2015.11.008
Kramti, S. E., & Ali, J. B. (2019). Particle filter based approach for wind turbine high-speed shaft bearing health prognosis, December 1–6.
Mohanty, A. R. (2014). Machinery condition monitoring: Principles and practices. In Machinery condition monitoring: Principles and practices. Boca Raton, FL: CRC Press.
Mohanty, S. N., & Suar, D. (2013a). Decision‐making in positive and negative prospects: Influence of certainty and affectivity.International Journal of Advances in Psychology, 2(1), 19–28.
Mohanty, S. N., & Suar, D. (2013b). Influence of mood states, group discussion, and interpersonal comparison on change in decision-making and information processing. International Journal of Decision Sciences, Risk and Management, 5(2), 101.
Mohanty, S. N., & Suar, D. (2014). Decision making under uncertainty and information processing in positive and negative mood states. Psychological Reports, 115(1), 91–105.
Moore, D. W. (2002). Question-order effects additive and subtractive previous research on the measurement of question-order effects. Public Opinion Quarterly, 66, 80–91.
Patel, V. L., Kaufman, D. R., & Arocha, J. F. (2002). Emerging paradigms of cognition in medical decision-making. Journal of Biomedical Informatics, 35(1), 52–75. https://doi.org/10.1016/S1532-0464(02)00009-6
Pham, H. (1994). Handbook of reliability engineering. New York: Springer. https://doi.org/10.1002/9780470172414
Shadlen, M. N., & Kiani, R. (2013). “Decision Making as a Window on Cognition” Neuron 80(3): 791–806. https://doi.org/10.1016/j.neuron.2013.10.047
Saidi, L., Ben Ali, J., Bechhoefer, E., & Benbouzid, M. (2017). Wind turbine high-speed shaft bearings health prognosis through a spectral kurtosis-derived indices and SVR. Applied Acoustics, 120, 1–8. https://doi.org/10.1016/j.apacoust.2017.01.005
Shmelova, T., Sikirda, Y., Scarponi, C., & Chialastri, A. (2018). Deterministic and stochastic models of decision making in air navigation socio-technical system. CEUR Workshop Proceedings, 2104, 649–656.
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), 1–14. https://doi.org/10.1016/j.ejor.2010.11.018
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, 167–182.
Wang, Y., & Ruhe, G. (2007). The cognitive process of decision making. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 1(2), 73–85. https://doi.org/10.4018/jcini.2007040105
Wang, Z., Busemeyer, J. R., Atmanspacher, H., & Pothos, E. M. (2013). The potential of using quantum theory to build models of cognition. Topics in Cognitive Science, 5(4), 672–688. https://doi.org/10.1111/tops.12043
Widodo, A., & Caesarendra, W. (2014). Summary of the recent developed techniques for machine health prognostics. Rotasi, 16(1), 21. https://doi.org/10.14710/rotasi.16.1.21-27
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Banerjee, A., Gupta, S.K., Datta, D. (2020). Remaining Useful Life as a Cognitive Tool in the Domain of Manufacturing. In: Mohanty, S.N. (eds) Emotion and Information Processing. Springer, Cham. https://doi.org/10.1007/978-3-030-48849-9_11
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