An Algorithm of Dynamic Load Identification Based on FBG Sensor and Kalman Filter

Xuegang, 宋雪刚 Song; Peng, 刘鹏 Liu; Zhuming, 程竹明 Cheng; Zhen, 魏真 Wei; Junsong, 喻俊松 Yu; Jiwei, 黄继伟 Huang; Dakai, 梁大开 Liang

doi:10.3788/aos201838.0328012

Cited by 2 publications

(1 citation statement)

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“…Kahandawa et al 3 proposed a fixed filter decoding system and an integrated artificial neural network algorithm for extracting strain from embedded FBG sensor. On the basis of Kalman filter, Song et al 4 taked the strain value measured by FBG sensor as the observed signal. Through the gain matrix, new information sequence and covariance matrix generated by Kalman filter, the least square algorithm is used to estimate the load size in real time.…”

Section: Related Workmentioning

confidence: 99%

Research on stress curve clustering algorithm of Fiber Bragg grating sensor

Lin

Wang

et al. 2023

Sci Rep

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The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity. Aiming at the problem of low efficiency and poor effect of traditional clustering algorithm, density clustering algorithm based on mutual nearest neighbor is used to cluster. Finally, the classification of the daily stress load characteristics of the sensor is realized, which provides a basis for constructing the mathematical analysis model of building health. The experimental results show that the stress curve clustering method proposed in this paper is better than the latest clustering algorithms such as HDBSCAN, CBKM, K-mean++,FINCH and NPIR, and is suitable for the feature classification of stress curves of fiber Bragg grating sensors.

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Section: Related Workmentioning

confidence: 99%

Research on stress curve clustering algorithm of Fiber Bragg grating sensor

Lin

Wang

et al. 2023

Sci Rep

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Online Dynamic Load Identification Based on Extended Kalman Filter for Structures with Varying Parameters

Jiang

Mohamed

2021

Symmetry

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Dynamic load identification is an inverse problem concerned with finding the load applied on a structure when the dynamic characteristics and the response of the structure are known. In engineering applications, some of the structure parameters such as the mass or the stiffness may be unknown and/or may change in time. In this paper, an online dynamic load identification algorithm based on an extended Kalman filter is proposed. The algorithm not only identifies the load by measuring the structural response but also identifies the unknown structure parameters and tracks their changes. We discuss the proposed algorithm for the cases when the unknown parameters are the stiffness or the mass coefficients. Furthermore, for a system with many degrees of freedom and to achieve online computations, we implement the model reduction theory. Thus, we reduce the number of degrees of freedom in the resulting symmetric system before applying the proposed extended Kalman filter algorithm. The algorithm is used to recover the dynamic loads in three numerical examples. It is also used to identify the dynamic load in a lab experiment for a structure with varying parameters. The simulations and the experimental results show that the proposed algorithm is effective and can simultaneously identify the parameters and any changes in them as well as the applied dynamic load.

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An Algorithm of Dynamic Load Identification Based on FBG Sensor and Kalman Filter

Cited by 2 publications

References 0 publications

Research on stress curve clustering algorithm of Fiber Bragg grating sensor

Research on stress curve clustering algorithm of Fiber Bragg grating sensor

Online Dynamic Load Identification Based on Extended Kalman Filter for Structures with Varying Parameters

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