A data-driven adaptive algorithm and decision support design of multisensory information fusion for prognostics and health management applications

Multisensory systems play a critical role in prognostics and health management (PHM), and utilise the information from multi-device synchronous measurements for fault diagnosis and predictive maintenance. But it is not suitable for specific systems with limited bandwidth and energy reservoirs since...

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Vydané v:	Journal of engineering design Ročník 34; číslo 2; s. 158 - 179
Hlavní autori:	Xie, Tingli, Huang, Xufeng, Park, Hyung Wook, Kim, Heung Soo, Choi, Seung-Kyum
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Abingdon Taylor & Francis 01.02.2023 Taylor & Francis Ltd
Predmet:	Adaptive algorithms Artificial neural networks Data integration Data-driven adaptive decision support design Fault diagnosis Feature extraction Functional analysis Measuring instruments Multisensor fusion multisensory information fusion Predictive maintenance prognostics and health management Qualitative analysis Sensors Systems analysis
ISSN:	0954-4828, 1466-1837
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Abstract	Multisensory systems play a critical role in prognostics and health management (PHM), and utilise the information from multi-device synchronous measurements for fault diagnosis and predictive maintenance. But it is not suitable for specific systems with limited bandwidth and energy reservoirs since the increased sophistication of measurement devices requires more computation and power resources. This research explores a data-driven analytical framework for multisensory system analysis and design in PHM. The proposed framework provides the optimal subset of reliable sensors to make trade-offs between accuracy demands and system constraints. The integration definition for function modelling method is adopted for modelling and functional analysis of the proposed framework. An adaptive signal conversion algorithm is designed to process the data from all reliable sensors in the system. The convolutional neural network with residual learning is built for automatic feature extraction. Combined with the evaluation rules and expert knowledge, performance analyses are obtained, including qualitative results, fault diagnosis, and the optimal sensor combination. An open-source bearing dataset of the multisensory system with five measurements is conducted to demonstrate the effectiveness and feasibility of the proposed framework.
AbstractList	Multisensory systems play a critical role in prognostics and health management (PHM), and utilise the information from multi-device synchronous measurements for fault diagnosis and predictive maintenance. But it is not suitable for specific systems with limited bandwidth and energy reservoirs since the increased sophistication of measurement devices requires more computation and power resources. This research explores a data-driven analytical framework for multisensory system analysis and design in PHM. The proposed framework provides the optimal subset of reliable sensors to make trade-offs between accuracy demands and system constraints. The integration definition for function modelling method is adopted for modelling and functional analysis of the proposed framework. An adaptive signal conversion algorithm is designed to process the data from all reliable sensors in the system. The convolutional neural network with residual learning is built for automatic feature extraction. Combined with the evaluation rules and expert knowledge, performance analyses are obtained, including qualitative results, fault diagnosis, and the optimal sensor combination. An open-source bearing dataset of the multisensory system with five measurements is conducted to demonstrate the effectiveness and feasibility of the proposed framework.
Author	Park, Hyung Wook Xie, Tingli Huang, Xufeng Choi, Seung-Kyum Kim, Heung Soo
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Cites_doi	10.1016/j.jmsy.2020.07.021 10.1016/j.aei.2022.101725 10.1109/TNNLS.2021.3071292 10.1080/09544828.2022.2106123 10.1016/j.ijepes.2021.107828 10.1109/TIM.19 10.1109/TTE.2022.3161988 10.1038/s41467-022-31747-w 10.1016/j.ymssp.2020.106861 10.1109/TII.2021.3112504 10.1016/j.ymssp.2020.107050 10.1016/j.knosys.2022.109846 10.1016/j.knosys.2021.107652 10.1016/j.neucom.2017.06.070 10.1109/TII.2022.3190394 10.1016/j.neunet.2021.01.026 10.1007/s10489-021-02555-4 10.1016/j.ress.2022.108969 10.1016/j.ymssp.2019.106587 10.1007/s10462-020-09825-6 10.1109/JPROC.2017.2761740 10.1109/ACCESS.2019.2907131 10.1007/s10462-020-09910-w 10.1007/s10845-022-02020-0 10.1080/09544828.2022.2164440 10.1109/TII.2020.2979467 10.1016/j.aei.2021.101286 10.1080/00207721.2021.2005178 10.1016/j.ress.2021.108179 10.1177/1729881420911257 10.1007/s00170-017-1474-8 10.1080/09544820601010981 10.1016/j.jmsy.2021.05.011 10.1016/j.ress.2022.108900 10.1016/j.knosys.2022.110212 10.1109/JSEN.2020.3000257 10.1109/TIE.2022.3169709 10.1016/j.inffus.2018.10.005 10.1016/j.ress.2021.108119 10.1016/j.measurement.2022.111536 10.1016/j.measurement.2020.107929 10.1109/TII.2021.3102017 10.1016/j.knosys.2021.106873 10.1016/j.knosys.2020.106230 10.1016/j.measurement.2020.108718 10.1016/j.ymssp.2021.108668 10.1109/TSMC.2022.3151185 10.1109/TMECH.2017.2728371 10.1016/j.asoc.2018.03.043 10.1016/j.isatra.2022.06.047 10.1109/SENSORS47125.2020.9278616 10.1016/j.jmsy.2021.03.005 10.1016/j.ymssp.2015.04.021 10.1109/JIOT.2022.3163606 10.1016/j.sigpro.2013.04.015 10.5772/67145 10.1016/j.ress.2021.108063 10.1115/1.4051749 10.1016/j.ymssp.2021.108068 10.1080/09544828.2022.2150448
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SubjectTerms	Adaptive algorithms Artificial neural networks Data integration Data-driven adaptive decision support design Fault diagnosis Feature extraction Functional analysis Measuring instruments Multisensor fusion multisensory information fusion Predictive maintenance prognostics and health management Qualitative analysis Sensors Systems analysis
Title	A data-driven adaptive algorithm and decision support design of multisensory information fusion for prognostics and health management applications
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