利用农闲将农用车交流永磁发电机改装成简易风力发电机

在幅员辽阔的北方和西部地区,风力资源雄厚,本人用河南产的12V、90W交流永磁发电机改装成风力发电的方法,介绍如下。一、12V、90W交流永磁发电机结构定子的槽数为12,转子有6对磁极,输出额定电压12V,转速为500转/分与风力发电转速450转/分相近。12V、90W发电机改装有两种方法:第一是原机各绕组不变,为三路并联,见展开图1。第二种是6个绕圈串联见图2。如果电     

摩托车大灯附加电路

市面上流行的90、100、110系列四冲程摩托车前大灯均采用发电机交流供电方式,其亮度随发动机转速而变化。正常行驶过程中车速常达4000转/分左右,发电机输出交流电压约12V,亮度稳定,     

精密自动车床攻丝套扣尾座轴高低速转换装置

如图所示,采用二台电磁离合器来控制攻丝套扣尾座轴的转速,不仅精度非常高,而且可以频繁转换。攻丝套扣是一个由低速加工到快速退刀的过程。先由主轴(7000转/分)以低速(6000转/分)旋转,用板牙套扣或者丝锥攻丝,待完成规定的丝扣之后,再转到快速(8000转/分)档。由6000转/分变到8000转/分的这一变速过程是由电磁离合器完成的。攻丝套扣的加工精度,由变速时的转换速度决定。如果响应性差或响应时间出现离散性,则会加工不出所规定     

基于符号序列Shannon熵的轴承故障趋势分析

讨论了时间序列转化为符号序列的信号粗粒化问题,给出二进制符号化和概率统计符号化两种规则.根据符号序列Shannon熵计算方法以及符号序列编码方法,得出了滚动轴承振动信号的熵值.结果表明,描述符号序列总体特征的Shannon熵能够反映轴承的故障趋势.     

覆冰导线振动数据降噪及状态识别

为准确识别输电线路覆冰运动状态,提出基于SSA-VMD和E-KL散度的覆冰导线振动数据降噪及状态识别的方法。首先利用微惯性测量组合(MIMU)采集导线运动三轴加速度和三轴角速度原始振动数据,构建排列熵、包络熵和相关系数结合的适应度函数,利用麻雀搜索算法(SSA)优化变分模态分解(VMD)参数。用优化后的变分模态分解方法将真实振动信号与噪声分离,选取真实振动信号重构信号,完成振动信号降噪预处理,并通过仿真实验验证降噪算法对覆冰导线振动数据的适用性。搭建覆冰导线模拟系统采集数据,降噪处理后计算导线各维能量,构建E-KL散度对覆冰导线状态识别,估计覆冰导线幅值和频率变异程度。实验结果表明可有效识别覆冰导线状态并进行幅频估计。     

基于变分模态分解和支持向量机的滚动轴承品质评估

针对滚动轴承品质评估过程中振动信号代表性特征提取不充分且模式识别方法精度低等不足,提出了基于变分模态分解(VMD)和支持向量机(SVM)的滚动轴承品质评估方法,首先,对3个品质等级的轴承样品进行振动信号的采集;其次,计算滚动轴承振动信号的有效值、峰值和峭度值3个时域指标(TDI),并采用VMD方法将信号分解为4个有限带宽模态函数(BIMF),并分别计算其排列熵(PE)值;最后,将3个时域指标和4个排列熵值共计7个特征作为SVM的输入变量构建轴承品质等级预测评估模型.实验结果表明:与TDI–PE–SVM模型相比, TDI–VMD–PE–SVM轴承品质评估模型更优,识别率由83.33%提高到93.33%, VMD方法有效地提高了振动信号的分辨率,有利于轴承振动信号细节特征信息的提取.     

飞机发电机故障诊断的多特征参数组合分析

针对飞机发电机振动特征参数多、故障特征参数难以准确识别飞机发电机健康状况的现状,设计了发电机振动信号实时采样装置对飞机发电机转动时的多种频域参数及幅域参数进行采样,并引入小波分析计算各频带能量值,构建神经网络进行故障判定,选用不同的振动特征参数组合对检验样本进行验证以期获得指向性较好的飞机发电机故障特征参数.诊断结果表明,利用RBF网络对发电机故障诊断,采用基于幅值域的特征参数峭度指标、峰值因子、脉冲指标、裕度指标、歪度和基于频域的重心频率、均方根频率、频率标准差,再考虑进小波包分频带能量值作为神经网络的输入参数指标,可取得良好的诊断准确率.     

基于DAGSVM的轴系扭振故障诊断方法

针对船舶轴系扭振故障小样本事件,基于小波包Shannon熵与二叉决策树支持向量机(DAGSVM)理论建立一种轴系扭振故障诊断模型.首先通过船舶轴系扭振实验平台提取轴系扭振四种模式信号;然后利用小波包变换提取Shannon熵值,作为故障输入特征向量;最后利用K-CV交叉验证法提升支持向量机,对故障进行建模识别.试验表明,...     

某型飞机发电机控制器智能测试台的研制

研究一种对飞机发电机控制器(GCU)的智能测试方法;整个系统通过51系列单片机实现对GCU过压/欠压,过频/欠频,差分保护等17个项目的自动测试工作;单片机在对多个测试项目启控点控制的同时,也对电压,频率,时延等检测信号进行传输和处理;通过计算机对单片机发出测试控制信号和接受单片机上传的测试结果数据,保存测试报告;实践证明,通过该方案设计的GCU智能测试系统满足GCU元件维修维护手册(CMM)的要求,这种测试方法是切实可行的。     

基于混合互信息的医学图像配准

通常的互信息测度是基于Shannon熵的,对Renyi熵进行分析,根据某些参数下的Renyi熵可以消除局部极值、而Shannon熵对于局部极值具有很强吸引域的特点,提出一种使用Renyi熵和Shannon熵的混合互信息测度,将两种测度分别用于不同的搜索阶段,首先使用全局搜索算法寻找基于Renyi熵的归一化互信息测度的局部极值,再通过局部优化方法对当前的局部最优解进行局部寻优以找到全局最优解,在局部优化阶段使用基于Shannon熵的归一化互信息测度作为目标函数。实验表明,这种配准算法比单纯使用Shannon熵能够取得更准确的配准结果,而且求解速度得到提高。     

An entropy fusion method for feature extraction of EEG

Feature extraction is a vital part in EEG classification. Among the various feature extraction methods, entropy reflects the complexity of the signal. Different entropies reflect the characteristics of the signal from different views. In this paper, we propose a feature extraction method using the fusion of different entropies. The fusion can be a more complete expression of the characteristic of EEG. Four entropies, namely a measure for amplitude based on Shannon entropy, a measure for phase synchronization based on Shannon entropy, wavelet entropy and sample entropy, are firstly extracted from the collected EEG signals. Support vector machine and principal component analysis are then used for classification and dimensionality reduction, respectively. We employ BCI competition 2003 dataset III to evaluate the method. The experimental results show that our method based on four entropies fusion can achieve better classification performance, and the accuracy approximately reaches 88.36 %. Finally, it comes to the conclusion that our method has achieved good performance for feature extraction in EEG classification.

     

基于多变量符号转移熵的癫痫脑电分析

大脑神经元细胞群的异常同步放电是癫痫的病因,这种异常放电是目前诊断癫痫的重要依据。利用复杂度理 论来分析癫痫信号已经成为研究热点,而符号转移熵是反应系统混乱程度的一种非线性指标,在研究癫痫脑电信号特征的提取中有重要的作用。符号转移熵一般都是用来衡量两 个变量之间的动力学特征及方向性信息,忽略了多个变量之间相互作用。本文基于多变量符号转移熵研究分析了癫痫脑电信号,实验中将原始信号符号化后通过数值分析,对导联信号及信号长度的选取以及稳健性分析,表明该方法能够对正常人与癫痫病人的脑电信号进行显著区分,且该算法稳健可靠,该研究结果对临床辅助诊断有帮助。     

Decision-relative discernibility matrices in the sense of entropies

Abstract

In rough set theory, attribute reduction is a basic issue, which aims to hold the discernibility of the attribute set. To obtain all of the reducts of an information system or a decision table, researchers have introduced many discernibility matrices based reduction methods. However, the reducts in the sense of positive region can only be obtained by using the existing discernibility matrices. In this paper, we introduce two discernibility matrices in the sense of entropies (Shannon’s entropy and complement entropy). By means of the two discernibility matrices, we can achieve all of the reducts in the sense of Shannon’s entropy and all of the reducts in the sense of complement entropy, respectively. Furthermore, we discover the relationships among the reducts in the sense of preserving positive region, Shannon’s entropy and complement entorpy. The experimental studies show that by the proposed decision-relative discernibility matrices based reduction methods, all the reducts of a decision table in sense of entropies can be obtained.     

Rényi entropy rate for Gaussian processes

In this paper, we introduce the definition of the conditional Rényi entropy for continuous random variables and show that the so-called chain rule holds. Then, we use this rule to obtain another relation for getting the rate of Rényi entropy. Using this relation and properties of the Rényi entropy we obtain the Rényi entropy rate for stationary Gaussian processes. Finally, we show that the bound for the Rényi entropy rate is simply the Shannon entropy rate and that the Rényi entropy rate reduces to the Shannon entropy rate as α→1.     

Efficiency analysis of information theoretic measures in image registration

Efficiency analysis of some information theoretic measures that can be used in image registration as objective functions is carried out. Shannon mutual information, Renyi and Tsallis entropy are examined using synthesized images with correlation function, intensity and noise distributions close to Gaussian. Results show that Renyi entropy potentially provides a faster convergence rate and lower variance of parameters’ estimates when using recurrent image registration algorithms. According to these criteria, Tsallis entropy provides a little worse results; however, it has a larger effective range. Shannon mutual information loses to both entropy measures. Moreover, it is more sensitive to noise. Nevertheless, Shannon mutual information is more effective in terms of computational complexity.     

基于统计特征和熵特征融合的心肌梗死辅助诊断方法

针对心肌梗死临床诊断过程中临床实用性和准确率不高的问题,提出一种基于12导联心电图（ECG）的心肌梗死的辅助诊断方法。首先,对12导联ECG信号进行去噪和数据增强处理;其次,分别对各导联ECG信号提取包含标准差、峰度系数、偏度系数的统计特征,以此反映信号的形态特征;同时,提取包含香农熵、样本熵、模糊熵、近似熵和排列熵的熵特征,以此表征ECG信号时间序列的时间与频谱复杂性、新模式产生的概率、规律性和不可预测性以及检测ECG信号的微小变化;然后,融合ECG信号的统计特征和熵特征;最后,基于随机森林算法在病人内和病人间两种模式下对算法进行分析和验证,并通过交叉验证防止过拟合。实验结果表明,病人内模式下算法准确率和F1值分别为99.98%和99.99%,病人间模式下算法准确率和F1值分别为94.56%和97.05%;与基于单导联ECG的诊断方法相比,采用12导联ECG诊断心肌梗死更符合医生临床诊断逻辑。     

K值优化的VMD在轴承故障诊断中的应用

由于机械设备实际运行状态下环境噪声的影响,轴承早期非平稳振动信号的故障特征难以有效提取。为此,将K值优化的变分模态分解引入轴承的早期故障诊断方法中。首先利用小波包降噪法对轴承实际振动信号进行降噪;然后利用K值优化的VMD算法,通过合理设置参数K,将降噪信号分解为若干本征模态分量,利用峭度值选取最佳分量;最后提取最佳分量的样本熵和排列熵组成特征向量,利用模糊C聚类识别轴承的故障类型。实验结果表明,该方法避免了信号的过分解,能有效提取振动信号特征,实现轴承的早期故障诊断。     

A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning

The localized faults of rolling bearings can be diagnosed by its vibration impulsive signals. However, it is always a challenge to extract the impulsive feature under background noise and non-stationary conditions. This paper investigates impulsive signals detection of a single-point defect rolling bearing and presents a novel data-driven detection approach based on dictionary learning. To overcome the effects harmonic and noise components, we propose an autoregressive-minimum entropy deconvolution model to separate harmonic and deconvolve the effect of the transmission path. To address the shortcomings of conventional sparse representation under the changeable operation environment, we propose an approach that combines K-clustering with singular value decomposition (K-SVD) and split-Bregman to extract impulsive components precisely. Via experiments on synthetic signals and real run-to-failure signals, the excellent performance for different impulsive signals detection verifies the effectiveness and robustness of the proposed approach. Meanwhile, a comparison with the state-of-the-art methods is illustrated, which shows that the proposed approach can provide more accurate detected impulsive signals.      

小波包变换在重叠地震信号分离中的应用

地震资料处理中常会遇到重叠地震信号,而如何将这些重叠数据分离开,是一个有待研究的课题。论文利用小波包同时在频域和时域具有良好的局部化性质,采用Shannon熵标准来选取最优小波包基,确定了重叠爆破的延迟时间,从而成功地对重叠地震信号进行了分离。