共查询到18条相似文献,搜索用时 78 毫秒
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为了有效地利用卫星下传的海量遥测数据,在测试过程中对卫星进行实时的故障诊断,提出了一种基于BP神经网络的卫星故障诊断方法;该方法包括离线自主学习和实时在线故障诊断两部分;离线自主学习部分基于历史数据库和更新样本进行自主学习,学习获得神经网络模型存储于知识库;实时在线故障诊断部分依据相应的神经网络模型,对遥测数据进行实时在线的诊断;为了验证基于BP神经网络的卫星故障诊断方法的有效性和优越性,以现有型号三轴稳定近地卫星控制分系统为实验对象,利用该方法对具有代表性的红外地球敏感器和动量轮的相关遥测数据进行分析;通过将该方法的实验结果与基于Kalman滤波的方法的实验结果进行对比分析,表明该方法能够有效地对卫星的故障进行诊断。 相似文献
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在对诱发铀部件裂变信号的测量原理及特点分析的基础上,开展了基于BP神经网络的诱发铀部件裂变时间关联信号特征参量分析处理的研究工作。 采用无偏估计方法, 计算信号的自相关函数和互相关函数, 再利用比较法和导数法两种特征量提取方法, 提取出不同状态下裂变信号的特征参量, 借助于BP神经网络模式识别应用原理进行训练和预测。 理论分析和研究结果表明: 基于比较法和导数法获得的特征参量能较好地反映诱发铀部件裂变信号的特征; 用BP神经网络对裂变信号进行模式识别, 取得了较高的正确率, 验证了此方法的有效性和合理性。 The paper presents feature parameter analysis and processing in fission time dependent signal of induced uranium components based on BP Neural Networks through the analysis of the measuring principle and signal characteristics of induced uranium components fission signal. The auto correlation functions and cross correlation functions are calculated by using unbiased estimate, and then the feature parameters of fission signal in different status are extracted by using feature abstraction method, comparative method and derivative method, and then applied to training and prediction by means of BP neural networks based on pattern recognition. Theoretical analysis and the results show that, it is effective to obtain feature parameters of induced uranium component fission signal via comparative method and derivative method. UsingBP neural network to recognize patter of fission signal, we got good results that verified the effectiveness and reasonability of the method. 相似文献
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基于BP神经网络的数码相机特性化 总被引:4,自引:0,他引:4
由于数码相机的颜色空间是依赖于设备的,对于一个具体的数码相机,其光谱响应与设备独立的CIE标准观察者颜色匹配函数是一个非线性关系,因此不能真实复制场景的颜色。特性化彩色图像设备是提高图像的颜色复制质量的一个重要方法。介绍一种基于BP神经网络数码相机特性化方法。采用Munsell颜色系统作为目标色,大样本训练空间。测试了不同的网络结构和样本空间分布。训练样本平均色差为1.75CMC(1∶1)色差单位,测试样本为2.16。该方法在数码相机颜色测量、光谱重建等领域有广泛的应用前景。 相似文献
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轮胎中植入的RFID标签,可以长时间的很好的存储轮胎的型号、胎压、出厂日期等信息。RFID标签在空气中的阅读距离可以达到很大的距离,但是一旦植入轮胎中,很容易受到轮胎中的金属层和炭黑等电介质的影响,导致读取距离下降。所以,需要寻找合适的方法来预测不同RFID标签情况下的阅读器读取距离,就显得尤为重要了。
为了更加快捷方便的研究两者之间的关系,在天线长度、轮胎的介电常数、与钢丝层的距离都变化的情况下,利用FEKO电磁仿真软件建立了不同情况下的天线,并仿真得到反射系数S_11,然后利用弗林斯传输方程(Friis)计算得到仿真读取距离。MATLAB中有可供调用的神经网络工具箱,利用MATLAB强大的数据处理能力,建立BP神经网络预测模型,从而建立起标签天线长度、轮胎中标签与钢丝层的距离、轮胎介电常数和已得到的仿真读取距离之间的BP神经网络模型。实际测量值与训练后得到的预测仿真值在误差允许的范围内可以认定为实际测量距离。
因此,可以通过建立BP神经网络模型的方法,快速方便的在一定精度范围内预测阅读器的阅读距离。 相似文献
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智能灯光控制系统是智能家居控制系统的重要组成部分。在分析了目前智能灯光控制系统缺陷与不足的基础上,提出了BP神经网络在智能灯光控制系统的应用,将BP算法嵌入到智能灯光控制系统的数据处理模块,提高控制系统对于数据的处理能力。系统通过引入BP神经网络的自学习能力,改善了智能灯光控制系统智能化程度低的问题。通过实验分析,该系统能够提高智能灯光控制系统的智能性,给人们提供了一个舒适的居家灯光环境。同时,BP神经网络在智能灯光控制系统的应用,对于解决智能家居控制系统解决智能化程度低的问题也有一定的促进作用。 相似文献
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This study proposes a fully automated gearbox fault diagnosis approach that does not require knowledge about the specific gearbox construction and its load. The proposed approach is based on evaluating an adaptive filter’s prediction error. The obtained prediction error’s standard deviation is further processed with a support-vector machine to classify the gearbox’s condition. The proposed method was cross-validated on a public dataset, segmented into 1760 test samples, against two other reference methods. The accuracy achieved by the proposed method was better than the accuracies of the reference methods. The accuracy of the proposed method was on average 9% higher compared to both reference methods for different support vector settings. 相似文献
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在复杂环境下齿轮箱信号往往会淹没在噪声信号中,特征向量难以提取;为了有效地进行故障诊断,提出了基于最大相关反褶积(MCKD)总体平均经验模态分解(EEMD)近似熵和双子支持向量机(TWSVM)的齿轮箱故障诊断方法;首先采用MCKD方法对强噪声信号进行滤波处理,在采用EEMD方法对齿轮箱信号进行分解,分解后得到本征模函数(IMF)分量进行近似熵求解,得到齿轮特征向量,最后将其输入到TWSVM分类器中进行故障识别;仿真实验表明,采用MCKD-EEMD方法能够有效地提取原始信号,与其他分类器相比,TWSVM的计算时间短,分类效果好等优点。 相似文献
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The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods. 相似文献
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基于径向基神经网络对民用高涵道比航空发动机风扇、增压级、高压压气机、高压涡轮、低压涡轮5大气路部件的效率降低故障进行诊断。采用Gasturb进行故障训练样本和测试样本库的生成,诊断结果显示,采用径向基神经网络进行航空发动机气路故障诊断的计算时间短、精度较高,不仅能定性的定位故障部位,而且在大多数情况下可以定量的给出该部件的性能衰退程度。某些情况下诊断结果与测试样本不尽一致,但都是方程的合理解,这是因为航空发动机的数学模型是一个多解的复杂方程,一个总性能的衰减对应着多组部件性能衰退的组合。随噪声幅值加大,诊断精度变差,同时研究发现诊断精度受噪声影响的敏感系数在不同的噪声幅值水平下是不同的。 相似文献
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A method for gearbox fault diagnosis consists of feature extraction and fault identification. Many methods for feature extraction have been devised for exposing nature of vibration data of a defective gearbox. In addition, features extracted from gearbox vibration data are identified by various classifiers. However, existing literatures leave much to be desired in assessing performance of different combinatorial methods for gearbox fault diagnosis. To this end, this paper evaluated performance of several typical combinatorial methods for gearbox fault diagnosis by associating each of multifractal detrended fluctuation analysis (MFDFA), empirical mode decomposition (EMD) and wavelet transform (WT) with each of neural network (NN), Mahalanobis distance decision rules (MDDR) and support vector machine (SVM). Following this, performance of different combinatorial methods was compared using a group of gearbox vibration data containing slightly different fault patterns. The results indicate that MFDFA performs better in feature extraction of gearbox vibration data and SVM does the same in fault identification. Naturally, the method associating MFDFA with SVM
shows huge potential for fault diagnosis of gearboxes. As a result, this paper can provide some useful information on construction of a method for gearbox fault diagnosis. 相似文献
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对食用合成色素日落黄的荧光光谱进行研究,发现在最佳激发波长370nm紫外光的激励下,荧光峰值波位于576nm;经分析认为,日落黄溶液之所以能产生荧光是因为分子中偶氮键将一个苯环和一个萘环连接在一起,形成大共轭结构,并且取代基与—SO3Na与—OH处于萘环的对位,大大增强了日落黄分子的共轭程度,使其具有强的吸光功能,发出强荧光。另外,结合径向基神经网络和BP神经网络对未知样本进行浓度预测,结果精确,平均相对误差分别为3.51%和5.45%,RSD分别为1.83%和2.95%。该方法有望成为对食用合成色素进行高效检测的有效方法。 相似文献