【摘要】：小型無人機(jī)因其具有成本低、風(fēng)險(xiǎn)可控、機(jī)動(dòng)性高等特點(diǎn)被廣泛應(yīng)用在商業(yè)和軍事領(lǐng)域。在無人機(jī)系統(tǒng)中布設(shè)著數(shù)量眾多的傳感器,如垂直陀螺、角速率傳感器、加速度計(jì)等。無人機(jī)平臺(tái)上的傳感器工作環(huán)境特殊,誘發(fā)故障的因素率較多。傳感器一旦發(fā)生故障或不穩(wěn)定,嚴(yán)重時(shí)可能導(dǎo)致無人機(jī)失控墜毀。因此,開展無人機(jī)傳感器故障診斷研究具有重要的應(yīng)用價(jià)值。本文以國(guó)產(chǎn)某小型無人機(jī)為研究對(duì)象,以典型機(jī)載傳感器的故障診斷實(shí)用方法為研究目標(biāo),旨在提出一種診斷精確度高、泛化能力強(qiáng)的故障診斷方法。首先,總結(jié)了無人機(jī)傳感器故障診斷技術(shù)的國(guó)內(nèi)外研究現(xiàn)狀,并且對(duì)典型機(jī)載無人機(jī)傳感器作了簡(jiǎn)要介紹和故障分析。以某型無人機(jī)科研試驗(yàn)歷史數(shù)據(jù)為基礎(chǔ),針對(duì)無人機(jī)傳感器,研究基于模式識(shí)別的故障診斷方法。然后,將小波分析應(yīng)用于特征提取方法中。仿真實(shí)現(xiàn)了小波包系數(shù)特征提取方法和小波包能量特征提取方法。并針對(duì)其不足做出改進(jìn),提出一種小波包復(fù)合特征提取方法。實(shí)驗(yàn)證明,該方法明顯改善了算法性能,提高了特征向量的可分性。接著,研究基于決策樹的分類診斷方法。采用ID3算法和CART算法構(gòu)建分類模型,實(shí)現(xiàn)了對(duì)無人機(jī)傳感器故障信號(hào)的分類識(shí)別。為提高故障診斷精度,引入梯度提升決策樹(GBDT)算法,通過對(duì)弱分類模型的迭代與組合,構(gòu)成診斷精度高的強(qiáng)分類模型。經(jīng)參數(shù)調(diào)優(yōu)后,算法性能得到進(jìn)一步的提升。最后,基于上述研究成果,提出一種基于小波與GBDT的無人機(jī)傳感器故障診斷方法。設(shè)計(jì)故障診斷驗(yàn)證平臺(tái),以無人機(jī)傳感器地面測(cè)試模塊與科研歷史數(shù)據(jù)作為測(cè)試樣本,對(duì)其進(jìn)行仿真驗(yàn)證。實(shí)驗(yàn)結(jié)果表明,該方法具有診斷精確度高和泛化能力強(qiáng)的性能優(yōu)勢(shì)。
[Abstract]:Small UAVs are widely used in commercial and military fields because of their low cost, controllable risks and high mobility. There are many sensors in UAV system, such as vertical gyroscope, angular rate sensor, accelerometer and so on. The sensor working environment on UAV platform is special, and the factor rate of inducing malfunction is many. If the sensor fails or is unstable, it can cause the UAV to crash out of control. Therefore, the research of UAV sensor fault diagnosis has important application value. In this paper, a small UAV made in China is taken as the research object and the practical method of fault diagnosis of typical airborne sensors is taken as the research object. The purpose of this paper is to put forward a fault diagnosis method with high diagnostic accuracy and strong generalization ability. Firstly, the research status of UAV sensor fault diagnosis technology at home and abroad is summarized, and the typical airborne UAV sensor is briefly introduced and analyzed. A fault diagnosis method based on pattern recognition is studied for UAV sensors based on the historical data of scientific research and test of a certain type of UAV. Then, wavelet analysis is applied to feature extraction. The methods of wavelet packet coefficient feature extraction and wavelet packet energy feature extraction are realized by simulation. In order to improve the performance of wavelet packet, a new method of wavelet packet composite feature extraction is proposed. Experimental results show that the proposed method improves the performance of the algorithm and improves the separability of the eigenvector. Then, the classification and diagnosis method based on decision tree is studied. The classification model is constructed by using ID3 algorithm and CART algorithm to realize the classification and recognition of UAV sensor fault signals. In order to improve the accuracy of fault diagnosis, the gradient lifting decision tree (GBDT) algorithm is introduced. The strong classification model with high diagnostic accuracy is constructed by iterating and combining the weak classification model. After parameter tuning, the performance of the algorithm is further improved. Finally, based on the above research results, a fault diagnosis method for UAV sensors based on wavelet and GBDT is proposed. The fault diagnosis and verification platform is designed, and the UAV sensor ground test module and scientific research history data are used as test samples to simulate and verify it. The experimental results show that this method has the advantages of high diagnostic accuracy and strong generalization ability.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：V267;V279;TP212

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