基于超声脉冲回波技术的动态结冰过程冰型识别

IF 4.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Sensors and Actuators A-physical Pub Date : 2025-06-20 DOI:10.1016/j.sna.2025.116817

Junfeng Ge , Longteng Li , Yabo Zhang , Kang Gui , Lin Ye

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引用次数: 0

摘要

为了提高超声脉冲回波技术用于冰型识别的精度，提出了一种基于动态小波指纹（DWFP）和机器学习的实时测冰方法。通过理论分析，揭示了孔隙度、孔隙当量直径等因素对回波峰等特征的影响。基于DWFP和主成分分析（PCA），从722个结冰案例回波信号中提取了多个冰型回波的时频特征。通过训练机器学习模型，得到了准确率为93.1 %的梯度增强决策树分类器，用于冰型识别。针对实验中回波重叠的情况，结合基于卷积神经网络的回波分离模型，最终实现了结冰动态过程全周期的冰型识别，准确率达到87.8 %。

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Ice type identification during dynamic icing process based on ultrasonic pulse echo technique

In order to improve the accuracy of ultrasonic pulse echo technology for ice type identification, a real-time ice-measurement method based on Dynamic Wavelet Fingerprint (DWFP) and machine learning is proposed. Through theoretical analysis, the influence of factors such as porosity and pore equivalent diameters on the echo peak and other features is revealed. From the echo signals of 722 icing cases, the time-frequency features of multiple ice-type echoes are extracted based on DWFP and principal component analysis (PCA). By training the machine learning model, the Gradient Boosting Decision Tree classifier with an accuracy of 93.1 % for ice type recognition is obtained. For the echo overlapping situation in the experiment, combined with the echo separation model based on a convolutional neural network, the ice type recognition during the full cycle of the icing dynamic process is finally realized with an accuracy of 87.8 %.

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来源期刊

Sensors and Actuators A-physical 工程技术-工程：电子与电气

CiteScore

8.10

自引率

6.50%

发文量

630

审稿时长

49 days

期刊介绍： Sensors and Actuators A: Physical brings together multidisciplinary interests in one journal entirely devoted to disseminating information on all aspects of research and development of solid-state devices for transducing physical signals. Sensors and Actuators A: Physical regularly publishes original papers, letters to the Editors and from time to time invited review articles within the following device areas: • Fundamentals and Physics, such as: classification of effects, physical effects, measurement theory, modelling of sensors, measurement standards, measurement errors, units and constants, time and frequency measurement. Modeling papers should bring new modeling techniques to the field and be supported by experimental results. • Materials and their Processing, such as: piezoelectric materials, polymers, metal oxides, III-V and II-VI semiconductors, thick and thin films, optical glass fibres, amorphous, polycrystalline and monocrystalline silicon. • Optoelectronic sensors, such as: photovoltaic diodes, photoconductors, photodiodes, phototransistors, positron-sensitive photodetectors, optoisolators, photodiode arrays, charge-coupled devices, light-emitting diodes, injection lasers and liquid-crystal displays. • Mechanical sensors, such as: metallic, thin-film and semiconductor strain gauges, diffused silicon pressure sensors, silicon accelerometers, solid-state displacement transducers, piezo junction devices, piezoelectric field-effect transducers (PiFETs), tunnel-diode strain sensors, surface acoustic wave devices, silicon micromechanical switches, solid-state flow meters and electronic flow controllers. Etc...