Self-calibration method of optical fiber shape sensor placement angle deviation based on GA

Tan, Tu; Xie, Yinlei; Duan, Chao; Xiao, Gui; Sun, Geng; Luo, Yuxiang; Tian, Ye; Zhang, Jianzhong

doi:10.1016/j.measurement.2022.111844

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…In order to improve the accuracy of FBG curvature measurement, researchers have conducted extensive research on the sensitivity of FBG curvature measurement [ 8 , 9 ]. For an FBG multi-core shape sensor, the placement angle deviation and calibration direction error of the FBG are the main factors that lead to the measurement error of curvature and bending direction [ 10 , 11 , 12 ]. The superposition of the two seriously affects curvature and bending direction measurement accuracy and may even cause incorrect shape reconfiguration results.…”

Section: Introductionmentioning

confidence: 99%

“…This method ignores the influence of the FBG placement angle on the measured curvature [ 14 ]. Tan et al proposed a self-calibration method based on a genetic algorithm to correct the placement angle in FSS, which automatically calibrates the placement angle of the sensor according to the measured strain and angle, resulting in an improved measurement accuracy of the curvature and bending direction [ 12 ]. The influence of the calibration direction deviation is ignored, and the need for accurate strain data increases the complexity of the experiment.…”

Section: Introductionmentioning

confidence: 99%

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Reconfiguration Error Correction Model for an FBG Shape Sensor Based on the Sparrow Search Algorithm

Shang

Feng

2023

Sensors

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A reconfiguration error correction model for an FBG shape sensor (FSS) is proposed. The model includes curvature, bending direction error correction, and the self-correction of the FBG placement angle and calibration error based on an improved sparrow search algorithm (SSA). SSA could automatically correct the placement angle and calibration direction of the FBG, and then use the corrected placement angle and calibration direction to correct the curvature and bending direction of the FSS, thereby improving the accuracy of shape reconfiguration. After error correction, the tail point reconfiguration errors of different shapes were reduced from 2.56% and 4.96% to 1.12% and 2.45%, respectively. This paper provides a new reconfiguration error correction method for FSS that does not require a complicated experimental calibration process, is simpler, more efficient, and more operable than traditional methods, and has great potential in FSS application scenarios.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconfiguration Error Correction Model for an FBG Shape Sensor Based on the Sparrow Search Algorithm

Shang

Feng

2023

Sensors

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A feature extraction method for rotating machinery fault diagnosis based on a target detection index and successive variational mode decomposition

Cao,

Zhang,

et al. 2023

Meas. Sci. Technol.

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Rotating machinery feature extraction is critical for the subsequent fault diagnosis and ensuring safe and stable operation. However, the commonly used methods often have limitations, such as the extracted features being redundant or insufficient and the method parameters generally being set based on experience. Although some feature extraction methods apply optimization algorithms for parameter setting, their objective functions are often too simple to obtain favorable fault diagnosis results. To address these issues, a target detection index (TDI) is constructed which can consider the discrimination among features more comprehensively and make the extracted features more sensitive. Furthermore, a fault feature extraction method is proposed based on TDI and successive variational mode decomposition (SVMD). Taking TDI as the objective function, genetic algorithm (GA) is used for the feature selection process and SVMD parameter optimization. The obtained features are then fused and visualized using t-distributed stochastic neighbor embedding and are classified using support vector machines. The Case Western Reserve University data and hydropower generating unit data are employed for method verification. When compared with other decomposition algorithms, the proposed method exhibits great ability at extracting highly sensitive features.

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A Novel FBG Shape Sensor Based on Mechanical Model for Continuum Manipulator

Zhang,

Dong,

Yan

et al. 2024

2024 10th International Conference on Automation, Robotics and Applications (ICARA)

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Self-calibration method of optical fiber shape sensor placement angle deviation based on GA

Cited by 6 publications

References 29 publications

Reconfiguration Error Correction Model for an FBG Shape Sensor Based on the Sparrow Search Algorithm

Reconfiguration Error Correction Model for an FBG Shape Sensor Based on the Sparrow Search Algorithm

A feature extraction method for rotating machinery fault diagnosis based on a target detection index and successive variational mode decomposition

A Novel FBG Shape Sensor Based on Mechanical Model for Continuum Manipulator

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