共查询到20条相似文献,搜索用时 139 毫秒
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Xiao Bin He Yu Pu Yang Ya Hong Yang 《Chemometrics and Intelligent Laboratory Systems》2008,93(1):27-33
In this paper, a new fault diagnosis approach with variable-weighted kernel Fisher discriminant analysis (VW-KFDA) is proposed. The approach incorporates the variable weighting into KFDA. The variable weighting finds out the weight vector of each fault by maximizing separation between the normal and each fault data. With continuous non-negative values, each element of the weight vector represents the corresponding variable's contribution to a special fault. After all fault data are weighted by the corresponding weight vectors, KFDA is performed on these weighted fault data. These weight vectors offer important supplemental classification information to KFDA and effectively improve its multi-classification performance. The proposed approach is applied to the Tennessee Eastman process (TEP). The results show superior capability for fault diagnosis to KFDA and FDA. 相似文献
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针对旋转机械高维故障特征集识别精度低的问题,提出基于核监督局部保留投影(Kernel Supervised Locality Preserving Projection, KSLPP)与ReliefF特征加权的K近邻(ReliefF Weighted K-Nearest Neighbor, RWKNN)分类器相结合的维数约简故障诊断方法。该方法首先应用KSLPP提取故障特征集中的非线性信息,同时在降维投影过程中充分利用类别信息,使降维后最小化类内散度,最大化类间分离度;随后,将降维后得到的低维敏感特征集输入RWKNN进行模式识别,RWKNN能够突出不同特征对分类的贡献率,强化敏感特征,弱化不相关特征,提升了分类精度和鲁棒性。最后,通过典型转子实验台的故障特征集验证了该方法的有效性。 相似文献
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摘 要:针对如何降低传感器网络中采集的非平稳、非线性信号的数据传输量,提出了一种基于灰色Morlet小波核偏最小二乘(GMWKPLS)的预测融合模型。该模型把灰色模型预测的思想融入到核偏最小二乘(KPLS)中,采用构造的Morlet小波核函数进行数据变换,将输入映射到高维非线性的特征空间,在特征空间中,利用线性偏最小二乘方法构造预测融合模型。通过对齿轮箱断齿工况升速过程中的振动信号进行分析,结果表明,该模型使用滑动窗方法不断更新建模数据进行动态预测,预测精度高,可大大降低数据传输量,获得显著的节能收益。通过与灰色RBF核偏最小二乘(GRBFKPLS)和RBF核偏最小二乘(RBFKPLS)预测模型对比,GMWKPLS性能最佳,预测误差范围在±0.4%以内。 相似文献
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提出一种基于Hilbert谱奇异值的故障特征提取方法,将其与支持向量机结合应用于轴承故障诊断。利用小波阈值降噪的方法对拾取的轴承故障振动信号进行滤波降噪,然后利用经验模式分解将降噪信号分解为若干个IMF分量之和,对每个IMF分量进行Hilbert变换得到振动信号的Hilbert谱,对Hilbert谱进行奇异值分解得到反映轴承状态特征的奇异值序列,再利用奇异值作为特征向量,应用支持向量机进行轴承故障诊断,并对不同核函数的诊断结果进行了分析比较。对正常轴承、内圈故障、外圈故障、滚动体故障的实际信号的诊断验证了该方法可的有效性。 相似文献
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Traffic sign recognition (TSR), as a critical task to automated driving and driver assistance systems, is challenging due to the color fading, motion blur, and occlusion. Traditional methods based on convolutional neural network (CNN) only use an end-layer feature as the input to TSR that requires massive data for network training. The computation-intensive network training process results in an inaccurate or delayed classification. Thereby, the current state-of-the-art methods have limited applications. This paper proposes a new TSR method integrating multi-layer feature and kernel extreme learning machine (ELM) classifier. The proposed method applies CNN to extract the multi-layer features of traffic signs, which can present sufficient details and semantically abstract information of multi-layer feature maps. The extraction of multi-scale features of traffic signs is effective against object scale variation by applying a new multi-scale pooling operation. Further, the extracted features are combined into a multi-scale multi-attribute vector, which can enhance the feature presentation ability for TSR. To efficiently handle nonlinear sampling problems in TSR, the kernel ELM classifier is adopted for efficient TSR. The kernel ELM has a more powerful function approximation capability, which can achieve an optimal and generalized solution for multiclass TSR. Experimental results demonstrate that the proposed method can improve the recognition accuracy, efficiency, and adaptivity to complex travel environments in TSR. 相似文献
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Yicheng Zhou Zhenzhou Lu Kai Cheng 《International journal for numerical methods in engineering》2019,120(4):498-516
Surrogate modeling techniques have been increasingly developed for optimization and uncertainty quantification problems in many engineering fields. The development of surrogates requires modeling high-dimensional and nonsmooth functions with limited information. To this end, the hybrid surrogate modeling method, where different surrogate models are combined, offers an effective solution. In this paper, a new hybrid modeling technique is proposed by combining polynomial chaos expansion and kernel function in a sparse Bayesian learning framework. The proposed hybrid model possesses both the global characteristic advantage of polynomial chaos expansion and the local characteristic advantage of the Gaussian kernel. The parameterized priors are utilized to encourage the sparsity of the model. Moreover, an optimization algorithm aiming at maximizing Bayesian evidence is proposed for parameter optimization. To assess the performance of the proposed method, a detailed comparison is made with the well-established PC-Kriging technique. The results show that the proposed method is superior in terms of accuracy and robustness. 相似文献
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Patra J.C. Panda G. Baliarsingh R. 《IEEE transactions on instrumentation and measurement》1994,43(6):874-881
A new approach to pressure sensor modeling based on a simple functional link artificial neural network (FLANN) is proposed. The response of the sensor is expressed in terms of its input by a power series. In the direct modeling, using a FLANN trained by a simple neural algorithm, the unknown coefficients of the power series are estimated accurately. The FLANN-based inverse model of the sensor can estimate the applied pressure accurately. The maximum error between the measured and estimated values is found to be only ±2%. The existing techniques utilize ROM or nonlinear schemes for linearization of the sensor response. However, the proposed inverse model approach automatically compensates the effect of the associated nonlinearity to estimate the applied pressure. Frequent modification of the ROM or nonlinear coding data is required for correct readout during changing environmental conditions. Under such conditions, in the proposed technique, for correct readout, the FLANN is to be retrained and a new set of coefficients is entered into the plug-in module. Thus this modeling technique provides greater flexibility and accuracy in a changing environment 相似文献
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With the rapid development of mechanical equipment, mechanical health monitoring field has entered the era of big data. Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities, this also brings influence to the mechanical fault diagnosis field. Therefore, according to the characteristics of motor vibration signals (nonstationary and difficult to deal with) and mechanical ‘big data’, combined with deep learning, a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed. The frequency domain signals obtained by the Fourier transform are used as input to the network. This method can extract features adaptively and unsupervised, and get rid of the dependence of traditional machine learning methods on human extraction features. A supervised fine tuning of the model is then carried out by backpropagation. The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object, the effectiveness of the proposed method was verified by a large number of data, and research on visualization of network output, the results shown that the SDAE method is more efficient and more intelligent. 相似文献
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发动机是车辆的核心部件,及时有效地发现并排除故障,对降低维修费用,减少经济损失,增加发动机工作时的可靠性,避免事故发生具有重大的意义。以某型号发动机为研究对象,运用测试技术、信号处理、小波分析、神经网络和模糊控制理论,提出了自适应模糊神经网络发动机故障诊断。首先建立了发动机故障信号采集试验台,在试验台上人工模拟四种工况,通过加速度传感器采集正常工况和异常工况的振动信号。再利用小波理论对采集到的振动信号进行消噪处理,提高信噪比,并提取出故障信号的特征值,作为网络训练和测试的样本数据。用样本数据训练和检测自适应模糊神经网络,即对发动机故障进行模式识别。通过仿真分析,取得了很好的诊断效果;同时与传统的BP神经网络故障诊断方法进行对比,无论在诊断精度上还是学习速度上,模糊神经网络在故障诊断中更具有优势。 相似文献
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目的针对包装机械设备中动力机轴承的故障诊断识别率低的问题,提出一种基于参数寻优的故障识别方法。方法首先通过主元分析算法对包装设备动力机的振动数据进行主成分特征提取,减少各数据间的相关性,然后采用LSSVM对各类数据样本进行故障识别。为了克服LSSVM惩罚因子和核函数参数易出现局部最优、收敛精度差等问题,提出一种ICS算法优化LSSVM的状态参数,提高包装机械动力机轴承故障诊断的识别率,以实测糖果厂包装机械振动数据为例验证所提方法的有效性。结果实验结果表明,在包装机械动力机轴承故障类别确定的情况下,算法能够高精度地识别各类动力机故障。结论该算法实现了分类器参数的自适应选择,为提高包装机械动力机轴承故障诊断的识别率提供了可靠的方法。 相似文献
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Muzamil Ahmed Muhammad Ramzan Hikmat Ullah Khan Saqib Iqbal Muhammad Attique Khan Jung-In Choi Yunyoung Nam Seifedine Kadry 《计算机、材料和连续体(英文)》2021,69(2):2217-2230
Violence recognition is crucial because of its applications in activities related to security and law enforcement. Existing semi-automated systems have issues such as tedious manual surveillances, which causes human errors and makes these systems less effective. Several approaches have been proposed using trajectory-based, non-object-centric, and deep-learning-based methods. Previous studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other methods. However, the their performance must be improved. This study explores the state-of-the-art deep learning architecture of convolutional neural networks (CNNs) and inception V4 to detect and recognize violence using video data. In the proposed framework, the keyframe extraction technique eliminates duplicate consecutive frames. This keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate frames. For feature selection and classification tasks, the applied sequential CNN uses one kernel size, whereas the inception v4 CNN uses multiple kernels for different layers of the architecture. For empirical analysis, four widely used standard datasets are used with diverse activities. The results confirm that the proposed approach attains 98% accuracy, reduces the computational cost, and outperforms the existing techniques of violence detection and recognition. 相似文献
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MOURAD TALBI 《Sadhana》2014,39(4):921-937
In this paper, we propose a new technique of Electrocardiogram (ECG) signal de-noising based on thresholding of the coefficients obtained from the application of the Forward Wavelet Transform Translation Invariant (FWT_TI) to each Bionic Wavelet coefficient. The De-noise De-noised ECG is obtained from the application of the inverse of BWT (B W T ?1) to the de-noise de-noised bionic wavelet coefficients. For evaluating this new proposed de-noising technique, we have compared it to a thresholding technique in the FWT_TI domain. Preliminary tests of the application of the two de-noising techniques were constructed on a number of ECG signals taken from MIT-BIH database. The obtained results from Signal to Noise Ratio (SNR) and Mean Square Error (MSE) computations showed that our proposed de-noising technique outperforms the second technique. We have also compared the proposed technique to the thresholding technique in the bionic wavelet domain and this comparison was performed by SNR improvement computing. The obtained results from this evaluation showed that the proposed technique also outperforms the de-noising technique based on bionic wavelet coefficients thresholding. 相似文献
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目的针对包装产品外壳上黑白QR码易受到污渍侵蚀损坏,长期磨损易模糊,以及图像采集过程易出现失焦模糊、运动模糊,导致无法完成识别需求,提出一种基于栈式降噪自编码器的受损QR码恢复的预处理方法,达到显著修复包装产品上受损的QR码图像并提高其识别率的目的。方法通过深度学习模型栈式降噪自编码器,可以将受到噪声干扰的像素点根据受损像素数据映射到以标准数据为参照的高概率数值点,实现整个受损QR码基于像素点的重构恢复,从而提高识别率。结果通过对实验QR码进行高斯模糊、随机污渍侵蚀等多种方式的损坏,能够将识别率较低或完全不能识别的测试图像集恢复出高质量的QR码图像,显著地提高了识别率,并且速度快、可重复性好。结论采用基于栈式降噪自编码器的受损QR码恢复的预处理方法,能够重建受损的QR码,并可以广泛应用于包装产品QR码识别前的预处理,以提高识别率。 相似文献
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基于二维EMD和小波阈值的掌纹图像去噪 总被引:1,自引:0,他引:1
为有效抑制掌纹图像中含有的噪声、提高特征提取的精度,提出一种基于二维经验模式分解和小波阈值去噪相结合的掌纹图像去噪新方法。首先,对含有噪声的掌纹图像进行二维EMD分解,得到不同特征尺度的本征模函数子图像;然后对中高频成分的IMF进行小波多阈值去噪;最后将去噪处理后的各IMF与残差图像通过加和进行重构。实验结果表明,该方法与单独的二维EMD滤波及小波阈值去噪相比,去噪效果更明显,提取的主线和细节特征更清晰,因而均方误差最小、峰值信噪比最高。 相似文献
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针对机械振动信号的故障特征提取问题,提出了基于独立变分模态分解与多尺度非线性动力学参数的特征提取方法。①提出频谱循环相干系数选取匹配波形对机械振动信号进行端点延拓后再进行VMD分解得到不同频率尺度的IMF分量;②根据互相关准则选取有效的IMF分量进行核独立成分分析,分离出相互独立的有效故障特征频带分量;③计算各独立分量的复合多尺度模糊熵偏均值,并利用正交变换将独立分量正交化后构造多维超体,进而利用多维超体体积定义并计算信号的双测度分形维数,从而获得多尺度非线性动力学特征参数,实现机械故障诊断。仿真和实验结果表明:所提方法可有效抑制VMD分解的端点效应和模态混叠,信号分解效果好,特征参数分类精度高,极大地提高了机械故障诊断准确率。 相似文献
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《IEEE transactions on instrumentation and measurement》2009,58(8):2433-2443