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Xianghao Zheng,Suqi Zhang,Yuning Zhang,Jinwei Li,Yuning Zhang 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.12
Evaluation of the operational stability of the main shaft is of great significance to ensure the security and reliability of the prototype reversible pump turbine (RPT). In the present paper, the experimental study was carried out using the sensors with high accuracy to obtain the shaft displacement signals under different load conditions of the RPT in the generating mode. A set of signal extraction procedure based on ensemble empirical mode decomposition, permutation entropy (PE) and modified wavelet soft-threshold de-noising method is proposed to reduce the influences of the random noises and extract the effective components within the signal. The PE values of the extracted shaft displacement signals are all below 0.3, illustrating that good extraction results have been achieved. Meanwhile, the typical shape evolution of the extracted shaft orbit with load variations at the turbine guide bearing is also depicted in detail. And the PE analysis result of the extracted shaft orbit can effectively reflect the evolution of different internal flow patterns under different load partitions of the RPT, which are 0.33-0.36 for low partial load partition, 0.22-0.30 for medium load partition and 0.30-0.32 for high partial load partition, respectively.
Optimal sensor placement for structural health monitoring based on deep reinforcement learning
Yong Huang,Xianghao Meng,Haoyu Zhang,Kailiang Jia,Hui Li 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.31 No.3
In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRLbased optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.