Abstract
This paper presents a health-state assessment (HSA) approach based on unsupervised feature selection and fault severity evaluation steps for nuclear power plant (NPP) water screen cleaner condition monitoring. Firstly, different features are extracted from raw accelerometer data acquired from the physical system. Then, different feature selection methods such as between-feature and within-feature selection techniques have been investigated. Next to feature selection step, the coefficient-of-variations (CoV) technique has been utilized to measure the severity levels of multiple fault types. The proposed approach is validated on different fault types acquired from in-field NPP water screen cleaner accelerometer data. The results show that the proposed approach can be effectively used for health-state assessment of NPP water screen cleaners.
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Acknowledgements
The authors acknowledge that all data collection procedure was funded by ASSYSTEM E&I and therefore the dataset used in this research remains the property of ASSYSTEM E&I. However, we would like to thank EDF (Électricité de France) for supporting this research and cooperating with us by giving a full access to their sites to build an experimental test bench on the nuclear power plant (NPP) system.
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Deleplace, A. et al. (2022). Health-State Assessment Approach Based on Unsupervised Feature Selection with Application to Nuclear Power Plant Water Screens. In: Zattoni, E., Simani, S., Conte, G. (eds) 15th European Workshop on Advanced Control and Diagnosis (ACD 2019). ACD 2019 2018. Lecture Notes in Control and Information Sciences - Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-030-85318-1_33
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DOI: https://doi.org/10.1007/978-3-030-85318-1_33
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