PHM Decision Support under Uncertainty
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Abstract
Decision support systems aim to improve the quality of services and help operators perform their duties faster, more accurately and more efficiently by providing an immense amount of knowledge. As human operators cannot convey their complete understanding of the situation to the system, decision support systems face the challenge of interpreting human intent based on operator inputs, which introduces a high level of uncertainty into the system. In this paper, a decision support system is used for determining system health status as a decision aid to the operator. The goal of this work is to supplement sensor data with human inputs in a prognostic health management environment while minimizing the effects of uncertainty and to provide situational awareness for the operator.
How to Cite
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decision support, human-machine interaction, mixed data, uncertainty management
Duda, R. O., Hart, P. E. & Stork, D. G. (2000). Pattern Classification (2Nd Edition). Wiley-Interscience.
Guyon, I. & Elisseeff, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, vol. 3, pp. 1157-1182.
Hu, Q. H., Zhang, L., Zhang, D., Pan, W., An, S., & Pedrycz, W. (2011). Measuring relevance between discrete and continuous features based on neighborhood mutual information. Expert Systems with Applications, vol. 38, no. 9, pp. 10737–10750.
Jollois, F.-X. & Nadif, M. (2002). Clustering Large Categorical Data. Advances in Knowledge Discovery and Data Mining, pp 257-263. Berlin: Springer.
Pages, J. (2014). Multiple Factor Analysis by Example Using R. Chapman and Hall/CRC.
Sarkar, S., Sarkar, S., Virani, N., Ray A. & Yasar, M. (2014). Sensor Fusion for Fault Detection & Classification in Distributed Physical Processes.
Frontiers in Robotics and AI, vol. 1, article 16.
Scheaffer, R. L. (1999). Categorical Data Analysis. NCSSM Statistics Leadership Institute.
Wilson, D. R. & Martinez. T. R. (1997), Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research, vol. 6, pp. 1-34.
Yasar, M., Beytin, A., Bajpai, G., & Kwatny, H. G. (2009). Integrated Electric Power System Supervision for Reconfiguration and Damage Mitigation. IEEE Electric Ship Technologies Symposium, Baltimore, MD.
Yasar, M., Ray, A., & Kwatny, H. G. (2010). Minimum rotation partitioning for data analysis and its application to fault detection. American Control Conference, Baltimore, MD.
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