Multiobjective-clustering-based optimal heterogeneous sensor placement method for thermo-mechanical load identification
The direct measurement of external loads acting on structures remains a challenge in many engineering applications. In this context, mechanical load identification has received considerable attention to inverse them using some response signals. Obviously, sensor placement is fundamental to the succe...
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sg-ntu-dr.10356-1708772023-10-04T04:27:23Z Multiobjective-clustering-based optimal heterogeneous sensor placement method for thermo-mechanical load identification Liu, Yaru Wang, Lei School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Optimal Sensor Placement Mechanical Load Identification The direct measurement of external loads acting on structures remains a challenge in many engineering applications. In this context, mechanical load identification has received considerable attention to inverse them using some response signals. Obviously, sensor placement is fundamental to the success of load identification. Considering temperature effects in the environment, this study investigates an optimal heterogeneous sensor placement framework for multi-case mechanical load identification. Firstly, the temperature field approximation method is developed using the measuring points with the minimum error in RBF interpolation. Based on the modal superposition theory, the formulas of mechanical load identification under static and dynamic cases are then deduced utilizing the responses eliminating thermal-oriented components. With the specific loading position, an index of the modal contribution rate is defined to provide a reasonable modal selection. Further, an optimal placement framework of multi-type response sensors (strain gauges and accelerometers) considering distance constraints is constructed for mechanical load identification under static/dynamic multiple cases, in which the optimization objective integrates the global performance and local evaluation of the interested modal loads. More specifically, an algorithm of collaborative clustering for heterogeneous modal matrices is involved to avoid redundant information and alleviate the computation burden. Eventually, two numerical examples are discussed to demonstrate the validity and feasibility of the developed approach. The authors would like to thank the National Nature Science Foundation of China (12072007, 12132001, 52192632), the China Scholarship Council (No. 202206020119), the Ningbo Nature Science Foundation (202003N4018), and the Defense Industrial Technology Development Program (JCKY2019205A006, JCKY2019203A003) for the financial supports. 2023-10-04T04:27:23Z 2023-10-04T04:27:23Z 2023 Journal Article Liu, Y. & Wang, L. (2023). Multiobjective-clustering-based optimal heterogeneous sensor placement method for thermo-mechanical load identification. International Journal of Mechanical Sciences, 253, 108369-. https://dx.doi.org/10.1016/j.ijmecsci.2023.108369 0020-7403 https://hdl.handle.net/10356/170877 10.1016/j.ijmecsci.2023.108369 2-s2.0-85152927717 253 108369 en International Journal of Mechanical Sciences © 2023 Elsevier Ltd. All rights reserved. |
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Engineering::Mechanical engineering Optimal Sensor Placement Mechanical Load Identification Liu, Yaru Wang, Lei Multiobjective-clustering-based optimal heterogeneous sensor placement method for thermo-mechanical load identification |
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The direct measurement of external loads acting on structures remains a challenge in many engineering applications. In this context, mechanical load identification has received considerable attention to inverse them using some response signals. Obviously, sensor placement is fundamental to the success of load identification. Considering temperature effects in the environment, this study investigates an optimal heterogeneous sensor placement framework for multi-case mechanical load identification. Firstly, the temperature field approximation method is developed using the measuring points with the minimum error in RBF interpolation. Based on the modal superposition theory, the formulas of mechanical load identification under static and dynamic cases are then deduced utilizing the responses eliminating thermal-oriented components. With the specific loading position, an index of the modal contribution rate is defined to provide a reasonable modal selection. Further, an optimal placement framework of multi-type response sensors (strain gauges and accelerometers) considering distance constraints is constructed for mechanical load identification under static/dynamic multiple cases, in which the optimization objective integrates the global performance and local evaluation of the interested modal loads. More specifically, an algorithm of collaborative clustering for heterogeneous modal matrices is involved to avoid redundant information and alleviate the computation burden. Eventually, two numerical examples are discussed to demonstrate the validity and feasibility of the developed approach. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Liu, Yaru Wang, Lei |
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Article |
author |
Liu, Yaru Wang, Lei |
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Liu, Yaru |
title |
Multiobjective-clustering-based optimal heterogeneous sensor placement method for thermo-mechanical load identification |
title_short |
Multiobjective-clustering-based optimal heterogeneous sensor placement method for thermo-mechanical load identification |
title_full |
Multiobjective-clustering-based optimal heterogeneous sensor placement method for thermo-mechanical load identification |
title_fullStr |
Multiobjective-clustering-based optimal heterogeneous sensor placement method for thermo-mechanical load identification |
title_full_unstemmed |
Multiobjective-clustering-based optimal heterogeneous sensor placement method for thermo-mechanical load identification |
title_sort |
multiobjective-clustering-based optimal heterogeneous sensor placement method for thermo-mechanical load identification |
publishDate |
2023 |
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https://hdl.handle.net/10356/170877 |
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1779171095602003968 |