本文選題：磁彈效應(yīng)傳感器 + BP神經(jīng)網(wǎng)絡(luò)�。� 參考：《安徽理工大學(xué)》2017年碩士論文

【摘要】：橋梁的索力健康至關(guān)重要,它是大跨度橋梁的受力構(gòu)件最主要的一部分,對(duì)于索力的實(shí)時(shí)撿測一直是橋梁安全問題的核心之一�；诖艔椥�(yīng)的索力檢測是一種新型等優(yōu)點(diǎn)。目前,雖然已有基于磁彈效應(yīng)的索力傳感器應(yīng)用于實(shí)際的橋梁索力檢測,但是這種索力傳感器的研制和索力測量是基于大量實(shí)驗(yàn)數(shù)據(jù)的統(tǒng)計(jì)和分析,缺乏理論上對(duì)影響鋼纜索索力測量的磁化特性、應(yīng)力變化和溫度變化的系統(tǒng)分析。根據(jù)鐵磁材料的逆磁致伸縮效應(yīng),鐵磁材料在磁場中會(huì)被磁化,如果鐵磁材料受到外力(如拉力)的作用,那么材料的磁化參數(shù)(磁導(dǎo)率μ)同時(shí)將會(huì)發(fā)生改變,我們把該現(xiàn)象為磁彈效應(yīng)。磁彈效應(yīng)傳感器的原理是磁導(dǎo)率變化與索力存在對(duì)應(yīng)關(guān)系,通過測量磁導(dǎo)率變化就能得出索力。但磁導(dǎo)率可能會(huì)隨著溫度的變化而變化,此時(shí)將影響到傳感器的測量結(jié)果。因此,磁彈效應(yīng)傳感器測量結(jié)果受溫度的影響,對(duì)磁彈效應(yīng)傳感器進(jìn)行溫度補(bǔ)償是很有必要的。溫度影響磁彈索力傳感器索力監(jiān)測這一問題的研究尚不足,對(duì)于整個(gè)系統(tǒng)的溫度影響機(jī)理,以及合理有效的溫度控制措施,還需進(jìn)一步系統(tǒng)的深入研究。本文采用的是BP神經(jīng)網(wǎng)絡(luò)法和RBF神經(jīng)網(wǎng)絡(luò)法降低溫度的干擾,還原真實(shí)的受力信號(hào)。用MATLAB對(duì)BP網(wǎng)絡(luò)法和RBF神經(jīng)網(wǎng)絡(luò)法兩種溫度算法進(jìn)行仿真,并比較二者的補(bǔ)償效果。結(jié)果表明:提出了 BP神經(jīng)網(wǎng)絡(luò)法和RBF神經(jīng)網(wǎng)絡(luò)法對(duì)磁彈效應(yīng)傳感器進(jìn)行溫度補(bǔ)償,其中BP神經(jīng)網(wǎng)絡(luò)補(bǔ)償精度最高可達(dá)98%,補(bǔ)償效果較好,提高了磁彈傳感器的準(zhǔn)確性和穩(wěn)定性。
[Abstract]:The bridge's cable force health is very important, it is the most important part of the long-span bridge's mechanical components, and the real-time measurement of the cable force is always one of the key problems of bridge safety. The cable force detection based on magnetoelastic effect is a new type of cable force detection. At present, although the cable force sensor based on magnetoelastic effect has been applied to the actual bridge cable force detection, the development of the cable force sensor and the cable force measurement are based on the statistics and analysis of a large number of experimental data. There is no systematic analysis of the magnetization characteristics, stress changes and temperature changes affecting cable force measurement in theory. According to the inverse magnetostriction effect of ferromagnetic material, ferromagnetic material will be magnetized in the magnetic field. If the ferromagnetic material is subjected to external force (such as pull force), the magnetization parameter (permeability 渭) of the material will change at the same time. We refer to this phenomenon as a magnetoelastic effect. The principle of magnetoelastic effect sensor is that there is a corresponding relationship between the change of permeability and the cable force, and the cable force can be obtained by measuring the change of permeability. However, the permeability may vary with the change of temperature, which will affect the measurement results of the sensor. Therefore, the results of magnetoelastic effect sensor are affected by temperature, so it is necessary to compensate the temperature of magnetoelastic effect sensor. The research on the temperature effect of the magnetoelastic cable force sensor is not enough. The mechanism of temperature influence and the reasonable and effective temperature control measures need to be further studied. In this paper, BP neural network and RBF neural network are used to reduce the interference of temperature and to reduce the real force signal. Two temperature algorithms, BP network method and RBF neural network method, are simulated by MATLAB and their compensation effects are compared. The results show that BP neural network method and RBF neural network method are proposed to compensate the temperature of magnetoelastic effect sensor. The compensation accuracy of BP neural network is up to 98, and the compensation effect is good, which improves the accuracy and stability of magnetoelastic sensor.
【學(xué)位授予單位】：安徽理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U446

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