Sparse Learning Method With Feature Selection for Sensor Placement and Response Prediction

Zhang, Minzhao; Ding, Junliang; Li, Bin

doi:10.1109/taes.2024.3423849

IEEE Trans. Aerosp. Electron. Syst.

2024

DOI: 10.1109/taes.2024.3423849

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Sparse Learning Method With Feature Selection for Sensor Placement and Response Prediction

Minzhao Zhang,

Junliang Ding,

Bin Li

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2024

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Strain Gauge Location Optimization for Operational Load Monitoring of an Aircraft Wing Using an Improved Correlation Measure

Peng,

Wang,

Ning

et al. 2024

Applied Sciences

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Operational loads of an aircraft are the prerequisite for assessing its safety or fatigue life. Traditionally, numerous strain gauge sensors are installed to monitor the operational loads, which inevitably increase the weight and system complexity of the aircraft. Therefore, in order to decrease the maintenance costs and data redundancy, the number and location of strain sensors should be optimized for accurate and reliable operational load monitoring. In this paper, a novel two-stage strain gauge location optimization method is proposed to reduce the number of strain gauges while maintaining the operational load monitoring accuracy, which is validated by a numerical case study of an aircraft wing. In the first stage, the traditional Pearson correlation measure is harnessed to initially eliminate numerous correlated strain gauge monitoring points, reducing 996 original strain gauge measurement points to 13 for the aircraft wing box. In the second stage, an improved correlation measure method is proposed to further reduce the 13 strain gauge points to 2, which can evaluate the correlation degree of several variables and simultaneously determine the optimal strain monitoring locations for the two load actuators in this study. The relative errors between the predicted loads and the actual loads for both load actuators are less than 4% when only two optimized monitoring points are adopted. In addition, a comparison study with LASSO regression and principal component regression methods is conducted. The results demonstrate that the proposed method has the characteristics of less monitoring points and higher load prediction precision.

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Strain Gauge Location Optimization for Operational Load Monitoring of an Aircraft Wing Using an Improved Correlation Measure

Peng,

Wang,

Ning

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

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Sparse Learning Method With Feature Selection for Sensor Placement and Response Prediction

Cited by 1 publication

References 47 publications

Strain Gauge Location Optimization for Operational Load Monitoring of an Aircraft Wing Using an Improved Correlation Measure

Strain Gauge Location Optimization for Operational Load Monitoring of an Aircraft Wing Using an Improved Correlation Measure

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