TY - JOUR
T1 - A data-driven adaptive algorithm and decision support design of multisensory information fusion for prognostics and health management applications
AU - Xie, Tingli
AU - Huang, Xufeng
AU - Park, Hyung Wook
AU - Kim, Heung Soo
AU - Choi, Seung Kyum
N1 - Publisher Copyright:
© 2023 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Data-driven adaptive
KW - decision support design
KW - multisensory information fusion
KW - prognostics and health management
UR - http://www.scopus.com/inward/record.url?scp=85148505962&partnerID=8YFLogxK
U2 - 10.1080/09544828.2023.2177937
DO - 10.1080/09544828.2023.2177937
M3 - Article
AN - SCOPUS:85148505962
SN - 0954-4828
VL - 34
SP - 158
EP - 179
JO - Journal of Engineering Design
JF - Journal of Engineering Design
IS - 2
ER -