共查询到20条相似文献,搜索用时 31 毫秒
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为了更有效地提取滚动轴承各状态振动信号的特征,该文提出了一种基于集合经验模态分解(EEMD)的敏感固有模态函数(IMF)选择算法。该算法对振动信号经EEMD分解后得到的固有模态函数采用峭度值、相关系数相结合的方法自动提取其敏感分量,以此获得振动信号的初始特征。再运用奇异值分解和自回归(AR)模型方法得到滚动轴承各状态振动信号的特征向量,并将其输入到改进的超球多类支持向量机中进行智能识别,从而实现滚动轴承的正常状态,不同故障类型及不同性能退化程度的各状态识别。实验结果表明,相比基于经验模态分解结合自回归模型或奇异值分解的特征提取方法,该方法可更有效地提取滚动轴承故障特征信息,且识别精度更高。 相似文献
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希尔伯特黄变换是由经验模态分解和希尔伯特变换所组成的,在非线性、非稳态信号处理方面具有独特的优势。本文首先对脑电波信号进行模态分解,然后根据希尔伯特变换求得各本征模态函数的瞬时频率并依此计算出均值、方差及其核心频率区间等特征,并选取若干个本征模态函数的频率特征组成一个长的特征向量,称之为希尔伯特黄频率特征环。根据该特征向量,本文进一步采用支持向量机对癫痫和非癫痫脑电波信号进行学习和分类,并采用格点搜索的方法来选取支持向量机中的最优参数。通过在典型癫痫脑电波数据集上的5重交叉验证得出本文所提出的新方法在分类准确率上已经超越或接近现有的分类方法。 相似文献
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The vibration signals of mechanical components with faults are non-stationary and the feature frequencies of faulty bearings and gears are difficult to be extracted. This paper presents a new approach that combines the fast ensemble empirical mode decomposition (EEMD) to decompose the non-stationary signal into stationary components, the random decrement technique (RDT) to extract the impulse signals of stationary components, and Hilbert envelope spectrum to demodulate the impulse signals to detect faults in bearings and gears. The proposed approach uses the fast EEMD algorithm to extract intrinsic mode functions (IMFs) from vibration signals able to tack the feature frequency of bearings and gears. IMF1 is further extracted by the RDT, and the feature frequencies are determined by analysing the signals using Hilbert envelope spectrum. Numerical simulations and experimental data collected from faulty bearings and gears are used to validate the proposed approach. The results show that the use of the EEMD, the RDT, and the Hilbert envelope spectrum is a suitable strategy to detect faults of mechanical components. 相似文献
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柴油机表面振动信号包含着设备运行的大量特征信息,是一种典型的非平稳时变信号,因此信号分析方法是柴油机故障诊断技术的难点。作者通过模拟柴油机气阀机构的气阀漏气和气门间隙异常两种主要故障,采集气缸缸盖表面的振动信号,应用时间序列分析方法建立缸盖振动信号的AR模型,然后利用欧氏距离判别函数进行故障状态识别。结果表明该方法是可行的,诊断效果比较好,同时对往复机械设备的在线实时监测与故障诊断做了有益研究。 相似文献
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针对运动想象脑电信号(EEG)的非线性、非平稳特点,该文提出一种结合条件经验模式分解(CEMD)和串并行卷积神经网络(SPCNN)的脑电信号识别方法。在CEMD过程中,采用各阶固有模式分量(IMF)与原始信号的相关性系数作为第1个IMF筛选条件,在此基础上,提出各阶IMF之间的相对能量占有率作为第2个IMF筛选条件。此外,为了考虑脑电信号各个通道之间的特征和突出每个通道内的特征,该文提出SPCNN网络模型对进行CEMD过程后的脑电信号进行分类。实验结果表明,在自行采集的脑电数据集上平均识别率达到94.58%。在公开数据集BCI competition IV 2b上平均识别率达到82.13%,比卷积神经网络提高了3.85%。最后,在自行设计的智能轮椅脑电控制平台上进行了轮椅前进、左转和右转在线控制实验,验证了该文算法对脑电信号识别的有效性。
相似文献8.
从故障诊断的角度分析印刷电路板的原理图,建立了电路板的诊断信息流模型。模型反映了试验之间及试验与故障之间的观察关系,基于该模型讨论了印刷电路板的故障诊断问题,实例说明了模型的有效性。同时该模型具有一定处理多故障的能力。 相似文献
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A Novel Method for the Diagnosis of the Incipient Faults in Analog Circuits Based on LDA and HMM 总被引:1,自引:0,他引:1
Lijia Xu Jianguo Huang Houjun Wang Bing Long 《Circuits, Systems, and Signal Processing》2010,29(4):577-600
Diagnosis of incipient faults for electronic systems, especially for analog circuits, is very important, yet very difficult.
The methods reported in the literature are only effective on hard faults, i.e., short-circuit or open-circuit of the components.
For a soft fault, the fault can only be diagnosed under the occurrence of large variation of component parameters. In this
paper, a novel method based on linear discriminant analysis (LDA) and hidden Markov model (HMM) is proposed for the diagnosis
of incipient faults in analog circuits. Numerical simulations show that the proposed method can significantly improve the
recognition performance. First, to include more fault information, three kinds of original feature vectors, i.e., voltage,
autoregression-moving average (ARMA), and wavelet, are extracted from the analog circuits. Subsequently, LDA is used to reduce
the dimensions of the original feature vectors and remove their redundancy, and thus, the processed feature vectors are obtained.
The LDA is further used to project three kinds of the processed feature vectors together, to obtain the hybrid feature vectors.
Finally, the hybrid feature vectors are used to form the observation sequences, which are sent to HMM to accomplish the diagnosis
of the incipient faults. The performance of the proposed method is tested, and it indicates that the method has better recognition
capability than the popularly used backpropagation (BP) network. 相似文献
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A radial-basis-function (RBF) neural-network-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an RBF-type neural network for fault identification and classification. The optimal network architecture of the RBF network is determined automatically by our proposed cell-splitting grid algorithm. This facilitates the conventional laborious trial-and-error procedure in establishing an optimal architecture. In this paper, the proposed RBF machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults, but the system is also able to estimate the extent of faults. 相似文献
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针对高压隔膜泵单向阀的故障振动信号特征难以提取及诊断的问题,文中采用KPCA和LSSVM相结合的方法进行故障诊断研究。对单向阀各状态信号运用双稳SR方法和DEMD算法进行信号预处理,并利用K-L散度选择分解后的主分量进行时频域特征参数的提取以构建特征向量集。运用KPCA对向量集进行二次特征提取,并将提取的特征向量输入到LSSVM诊断系统中,以完成单向阀故障诊断及分类。经实验验证,该方法的故障诊断率可达到90%,能够较好的诊断出单向阀故障特征。 相似文献
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经验模态分解(EMD)是希尔伯特?黄变换(HHT)中的关键步骤,并伴有过冲和端点效应的产生.利用遗传算法(GA)对支持向量机(SVM)中的未知参数:惩罚函数 C和高斯核函数中的预设参数σ进行优化选取,运用 GA-SVM 对信号进行端点延拓来处理端点效应问题并提出采用分段三次 Hermite多项式插值进行包络线拟合;为了机械设备早期故障频率的特征提取,采用小波包降噪预处理,结合改进的 Hilbert?Huang变换进行轴承故障特征频率的提取实验;实验表明该方法提高了故障频率提取的准确性. 相似文献
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一种基于CEMD和融合的多视点图像编码方法 总被引:1,自引:0,他引:1
该文提出了一种新的基于相邻视点融合的多视点图像编码方法,通过融合与拆分对非同源图像同时进行编码。编码时,原始图像经过CEMD(Complex Empirical Mode Decomposition)同步分解成2维的固有模态函数和余量图像并分别融合,再对融合图像进行基于EMD的压缩编码。解码时,将融合图像拆分,重构出原始图像。实验结果表明,该方法具有失真度小和压缩比高的优势,具有实践意义。 相似文献
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张毅 《太赫兹科学与电子信息学报》2015,13(5):794-798
在图像、语音识别或故障诊断等领域,特征提取是关键技术。在研究了小波变换和经验模态分解之后,深入分析了两者在特征提取上的优势和不足,提出了一种将两者优势有效结合来提取特征信息的方法。该方法先将信号做经验模态分解(EMD)得到平稳化单模态分量,再对单模态分量做小波包(WP)分析。最后,通过仿真和实例将本方法和已有文献中的方法进行对比,结果表明该方法不仅具有较高的可行性,而且可以准确地提取特征信息。 相似文献
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为提高模拟电路参变故障的诊断率,提出基于多特征向量提取和随机森林(RF)算法的模拟电路故障诊断新方法。采用时域和频域特征向量组合的多维特征向量以反映不同故障特征,经RF算法进行决策,并对决策树棵数及候选特征向量个数进行优化。故障诊断实验结果表明,所提方法能较好地实现容差模拟电路故障诊断,与支持向量机(SVM)方法相比,表现出更好的分类性能;与小波(包)特征提取方法相比,简化了多维数据特征提取步骤,易于实现在线故障诊断。 相似文献
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Data-fused method of fault diagnosis for analog circuits 总被引:1,自引:0,他引:1
Yanghong Tan Yigang He Yichuang Sun Hui Yang Meirong Liu 《Analog Integrated Circuits and Signal Processing》2009,61(1):87-92
A data-fused fault diagnosis method based on wavelet packet decomposition of voltages of test nodes and current signals of
the terminals stimulated is proposed in the paper. For the faults difficult to detect merely from voltages of test nodes,
the current signals of the terminals which contain sufficient information with various faults are fused with the sampled node
voltages of the circuit stimulated by the sources selected according to the principles proposed to make up for the insufficiency
of the node voltages. This results in the maximization of feature vectors, more accurate classification of the faults and
correct identification of the fuzzy sets of faults. 相似文献
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Hua Su Kil To Chong 《Industrial Electronics, IEEE Transactions on》2007,54(1):241-249
Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Model-based methods are efficient monitoring systems for providing warning and predicting certain faults at early stages. However, the conventional methods must work with explicit motor models, and cannot be applied effectively for vibration signal diagnosis due to their nonadaptation and the random nature of vibration signal. In this paper, an analytical redundancy method using neural network modeling of the induction motor in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform is used to process the quasi-steady vibration signals to continuous spectra for the neural network model training. The faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results, and it is shown that a robust and automatic induction machine condition monitoring system has been produced 相似文献
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小波-神经网络在故障诊断中的应用 总被引:1,自引:0,他引:1
根据旋转机械振动信号特点,提出了小波分析和概率神经网络相结合的故障诊断方法。该诊断方法利用小波分析进行预处理-获取机械故障特征向量,概率神经网络应用该特征及对应的故障类型建立非线性映射,实现故障诊断。通过计算机仿真和试验的结果,表明该方法运算速度快、对样本噪声有较强的鲁棒形,结构简单,工程上易于实现,为旋转机械故障诊断提供了实践方法。 相似文献