共查询到17条相似文献,搜索用时 78 毫秒
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基于Volterra级数时域频域混合模型,提出了辨识非线性模拟电路频域核的故障诊断方法.利用混合模型辨识算法和范德蒙特法估计各种故障状态下电路响应的前3阶频域核,提取故障特征并与相应的故障模式一起构成特征样本集,借助于支持向量机多分类器进行分类识别,实现非线性模拟电路的故障诊断.阐述了诊断原理及诊断步骤,并给出了诊断实例.仿真结果表明,该方法的故障识别率较高,便于计算机计算.
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针对非线性模拟电路软故障诊断的难题,基于Volterra核二次型分布—–Wigner Ville分布(WVD),提出一种故障特征提取的新方法。首先计算待测试电路(CUT)的Volterra核;然后对该Volterra核进行WVD转换,得到WVD函数,从中提取出故障特征,完成非线性模拟电路的软故障诊断。仿真实验结果表明,该方法能有效地解决故障混叠难题,提升故障元件检测和定位的能力。 相似文献
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陈文华 《计算机测量与控制》2014,22(6):1673-1675
核方法通过非线性映射将原始数据嵌入到高维特征空间,然后进行线性分析和处理,为基于知识的数据分析带来新的方法和模式;传统方法无法解决故障特征数据维数高、在故障样本交叠严重时多分类性能较差的问题,因此在电路故障特征数据预处理阶段,提出了分步骤分别在时域对电路输出电压波形进行小波包分析和在频域测量电路幅频特性的方法来提取电路故障特征;预处理后的故障特征向量只是8维向量,减少了SVM的训练时间;将该方法应用于国际标准电路中的CTSV滤波器电路的故障诊断,结果表明:该方法能突出不同故障的特性,故障诊断正确率达到98.57%(414/420)。 相似文献
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针对模拟电路的固有复杂性及其传统故障检测方法延时大和正确识别率低的问题,借鉴基于隐马尔科夫模型改进最小二乘支持向量机以及Volterra级数原理,将二者组合进行故障诊断。该方法首先采用Volterra级数频域核对电路故障特征进行提取,再利用经隐马尔科夫模型改进的最小二乘支持向量机进行模态分类,最终完成故障诊断。仿真结果表明,与目前使用的BP神经网络诊断方法和LSSVM诊断方法相比,该方法不仅提高了系统故障辨识能力,还提高了系统故障诊断的速度。 相似文献
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针对模拟电路的固有复杂性及其传统故障检测方法存在延时大和正确识别率低的问题,提出基于最小二乘支持向量机和Volterra级数的故障诊断方法。采用Volterra级数频域核对电路故障特征进行提取,利用最小二乘支持向量机进行模态分类,最终完成故障诊断。仿真结果表明,该方法与BP神经网络相比提高了系统故障辨识能力与系统故障诊断速度。 相似文献
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Fault detection and diagnosis (FDD) in chemical process systems is an important tool for effective process monitoring to ensure the safety of a process. Multi-scale classification offers various advantages for monitoring chemical processes generally driven by events in different time and frequency domains. However, there are issues when dealing with highly interrelated, complex, and noisy databases with large dimensionality. Therefore, a new method for the FDD framework is proposed based on wavelet analysis, kernel Fisher discriminant analysis (KFDA), and support vector machine (SVM) classifiers. The main objective of this work was to combine the advantages of these tools to enhance the performance of the diagnosis on a chemical process system. Initially, a discrete wavelet transform (DWT) was applied to extract the dynamics of the process at different scales. The wavelet coefficients obtained during the analysis were reconstructed using the inverse discrete wavelet transform (IDWT) method, which were then fed into the KFDA to produce discriminant vectors. Finally, the discriminant vectors were used as inputs for the SVM classification task. The SVM classifiers were utilized to classify the feature sets extracted by the proposed method. The performance of the proposed multi-scale KFDA-SVM method for fault classification and diagnosis was analysed and compared using a simulated Tennessee Eastman process as a benchmark. The results showed the improvements of the proposed multiscale KFDA-SVM framework with an average 96.79% of classification accuracy over the multi-scale KFDA-GMM (84.94%), and the established independent component analysis-SVM method (95.78%) of the faults in the Tennessee Eastman process. 相似文献
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Z.Q. Lang Author Vitae S.A. Billings Author VitaeAuthor Vitae J. Li Author Vitae 《Automatica》2007,43(5):805-816
An expression for the output frequency response function (OFRF), which defines the explicit analytical relationship between the output spectrum and the system parameters, is derived for nonlinear systems which can be described by a polynomial form differential equation model. An effective algorithm is developed to determine the OFRF directly from system simulation or experimental data. Simulation studies demonstrate the significance of the OFRF concept, and verify the effectiveness of the algorithm which evaluates the OFRF numerically. These new results provide an important basis for the analytical study and design of a wide class of nonlinear systems in the frequency domain. 相似文献
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针对模拟电路故障诊断中特征提取以及模型训练时间较长的难题,采用了一种基于深度极限学习机的模拟电路故障诊断算法。该算法将深度学习中自编码器的思想引入到极限学习机中,构建深度网络,将底层的故障特征转换更加抽象的高级特征,能自主地学习数据特征,避免了繁琐的特征提取和选择。最终通过Sallen-Key和四运放双二次高通滤波2个模拟电路进行仿真研究,实验结果验证了算法在模拟电路故障诊断上的可行性,也表明模型学习速度快、泛化能力好,具有较强的诊断能力,故障诊断分类准确率可以达到100%,诊断时间在0.3 s左右。 相似文献