共查询到19条相似文献,搜索用时 250 毫秒
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蒋永华程光明阚君武宣仲义马继杰张忠华 《仪器仪表学报》2013,(12):2684-2689
支持向量机(SVM)的分类性能受样本的特征以及SVM本身参数的选择影响较大。针对这种情况,基于Shannon能量熵、SVM和小生境遗传算法(NGA),提出了一种基于NGA优化SVM的滚动轴承故障诊断方法。该方法采用容错性强的Shannon能量熵作为特征参数,对信号进行EMD分解提取出前3个IMF分量作为特征信号,分别计算其Shannon能量熵作为特征向量得到样本集,作为多类别SVM的输入。在用样本训练SVM时,构造一种新的核函数,并采用NGA对SVM的核函数参数进行全局优化,使SVM获得最佳的分类性能,提高其分类识别的正确率。最后采用凯斯西储大学的滚动轴承故障样本进行了分类识别,并与其他几种方法进行了对比,结果表明该方法具有更好的可靠性和分类准确率。 相似文献
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齿轮故障信号具有不平稳特性,故障信号特征向量难提取,典型的齿轮故障数据样本少。针对这些问题,提出基于总体平均经验模式分解(EEMD)、模糊熵和支持向量机(SVM)相结合的诊断方法。首先通过传感器采集得到加速度信号,然后,通过EEMD降低模态混叠,并将加速度信号分解成多个稳定的本征模态函数信号(IMFs)。其次,利用模糊熵能够表现信号复杂程度并且稳定的性质,取多个稳定IMFs的前几项计算模糊熵。因为SVM能够在小样本集情况下建立决策规则,所以将IMFs的前几项模糊熵值作为特征向量输入SVM训练。最后,SVM算法与常用神经网络比较,对样本训练、测试并诊断故障,说明SVM算法优于神经网络。齿轮故障诊断实验结果表明,所提出的方法诊断准确率达92.5%,可实现齿轮故障信息提取和齿轮故障的有效诊断。 相似文献
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为了有效利用故障特征集中对故障敏感的特征进行故障诊断,提出基于独立特征选择(IFS)与局部保持投影(LPP)的故障诊断方法。该方法主要有三个步骤,一是从通过不同的特征提取方法,以此构建能够表征故障的混合特征集;二是对核线性判别分析(KLDA)进行改进,得到一种为每两类故障构建独立特征集的特征选择方法,在此基础上采用LPP对独立特征集进行进一步的融合;三是支持向量机(SVM)对构建的融合特征集进行识别,得到诊断结果。电机轴承故障诊断实例表明,所提方法具备较高的诊断准确率。 相似文献
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一种聚类分层决策的SVM模拟电路故障诊断方法 总被引:3,自引:1,他引:2
支持向量机用于模拟电路多种故障诊断时,其多分类扩展策略与诊断的效率和正确率密切相关。本文提出模糊聚类与支持向量机集成的算法,通过分析电路故障特征数据的空间分布特性,以多级二叉树结构的SVM实现故障的分级诊断。通过对各故障模式两种小波特征的逐次聚类二分获得二叉树,根据F测度为每个节点的SVM选择具有最大分类间隔的故障子类及特征,避免了不可分故障区域的出现,从而优化了SVM的组合策略。采用该方法组建的SVM结构简单,在滤波器电路的故障诊断中获得良好的效果。与几种常用的SVM方法相比,本文方法有效地提高了故障诊断的精度和效率。 相似文献
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针对环卫车辆驱动电机故障特征不明显且存在大量干扰因素等问题,为了提高故障诊断准确率,提出一种基于核主元分析和粒子群优化支持向量机(SVM)的故障诊断方法。该方法利用在电机故障状态下的振动信号构建时频域混合特征集,通过核主元分析,对特征集内的特征量进行降维处理;通过选择主元特征和利用粒子群算法,优化SVM的主要参数,将得到的特征量输入到优化后的SVM中进行计算,并与未进行核主元分析的SVM进行对比分析。计算结果表明:该方法能够显著提高扫路车驱动电机的故障诊断准确率。 相似文献
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针对目前集成电路测试复杂度的不断增加,导致测试成本不断攀升的问题,提出一种可靠而有效的测试集优化方法。通过k均值(K-means)聚类对原始测试集中的特征进行聚类筛选,然后采用改进的mRMR算法,分段式引入特征之间冗余性权重因子,用以权衡特征相关性和冗余性的度量,同时插入了SVM交叉验证,强化了测试模式选择的准确性。在保证故障覆盖率基本不变的情况下,达到减少原始测试集维数的目的。对ISCAS89电路实验表明,该文方法将原始测试集的测试模式进行大量的精简,既保证测试质量,也极大地优化了测试集,进行冗余消除和排序后的测试流程缩短了40.43%的测试时间,提升了测试效率,降低了测试成本。 相似文献
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《轴承》2021,(10)
为提高滚动轴承故障诊断的准确性,提出基于信息增益比的奇异谱分析(IGRSSA)与改进粒子群算法优化支持向量机(IPSO-SVM)的诊断模型。首先,引入信息增益比实现信号自适应重构;其次,采用动态惯性权重和梯度信息对粒子群算法进行改进并用于优化支持向量机;然后,用IGRSSA对滚动轴承外圈故障、钢球故障和正常3种状态的振动信号进行降噪并提取时域特征值,使用平均影响值(MIV)筛选出最优特征参量作为后续故障信号特征数据集;最后,将BP神经网络、RBF神经网络、交叉验证优化的SVM、遗传算法优化的SVM和粒子群优化的SVM作为对比算法用于轴承故障诊断。30次有放回的随机抽样诊断结果表明,IPSO-SVM的平均诊断准确率达到97.72%,波动性和收敛误差均优于其他方法。 相似文献
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Kadri Ouahab Mouss Leila Hayet Mouss Mohamed Djamel 《Journal of Mechanical Science and Technology》2012,26(2):601-608
This paper proposes a novel hybrid algorithm for fault diagnosis of rotary kiln based on a binary ant colony (BACO) and support
vector machine (SVM). The algorithm can find a subset selection which is attained through the elimination of the features
that produce noise or are strictly correlated with other already selected features. The BACO algorithm can improve classification
accuracy with an appropriate feature subset and optimal parameters of SVM. The proposed algorithm is easily implemented and
because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm
is evaluated through two real Rotary Cement kiln datasets. The results show that our algorithm outperforms existing algorithms. 相似文献
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During the condition monitoring of a planetary gearbox, features are extracted from raw data for a fault diagnosis.However, different features have different sensitivity for identifying different fault types, and thus, the selection of a sensitive feature subset from an entire feature set and retaining as much of the class discriminatory information as possible has a directly effect on the accuracy of the classification results. In this paper, an improved hybrid feature selection technique(IHFST) that combines a distance evaluation technique(DET), Pearson's correlation analysis, and an ad hoc technique is proposed. In IHFST, a temporary feature subset without irrelevant features is first selected according to the distance evaluation criterion of DET, and the Pearson's correlation analysis and ad hoc technique are then employed to find and remove redundant features in the temporary feature subset, respectively, and hence,a sensitive feature subset without irrelevant or redundant features is selected from the entire feature set. Further, the k-means clustering method is applied to classify the different kinds of health conditions. The effectiveness of the proposed method was validated through several experiments carried out on a planetary gearbox with incipient cracks seeded in the tooth root of the sun gear, planet gear, and ring gear. The results show that the proposed method can successfully distinguish the different health conditions of a planetary gearbox, and achieves a better classification performance than other methods. This study proposes a sensitive feature subset selection method that achieves an obvious improvement in terms of the accuracy of the fault classification. 相似文献
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Zhiliang Liu Jian Qu Ming J. Zuo Hong-bing Xu 《The International Journal of Advanced Manufacturing Technology》2013,67(5-8):1217-1230
This paper introduces a hybrid dimension reduction method that combines kernel feature selection and kernel Fisher discriminant analysis (KFDA). In the first stage, a kernel feature selection method is proposed to remove redundant and irrelevant features for two purposes: (1) reducing computation burden of the entire fault diagnosis system and (2) alleviating the impact of irrelevant features on KFDA. In the second stage, KFDA is used to establish a more compact feature subset by extracting a smaller number of features. We use Gaussian radial basis function as the kernel function for the two kernel stages in the proposed method. A parameter selection method for this kernel is proposed to select the optimal values for the proposed method. Experimental results on fault level diagnosis demonstrate that the proposed hybrid dimension reduction method has advantages over other approaches that use feature selection or KFDA separately. 相似文献
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《Mechanical Systems and Signal Processing》2007,21(4):1787-1798
In the fault diagnosis based on support vector machines (SVM), irrelevant variables in the fault samples spoil the performance of the SVM classifier and reduce the recognition accuracy. On the other hand, some SVM parameters are usually selected artificially, which hampers the efficiency of the SVM algorithm in practical applications. A new method that jointly optimises the feature selection and the SVM parameters with a modified discrete particle swarm optimisation is presented in this paper. A correct ratio based on a new evaluation method is used to estimate the performance of the SVM, and serves as the target function in the optimisation problem. A hybrid vector that describes both the fault features and the SVM parameters is taken as the constraint condition. This new method can select the best fault features in a shorter time, and improves the performance of the SVM classifier, and has fewer errors and a better real-time capacity than the method based on principal component analysis (PCA) and SVM, or the method based on Genetic Algorithm (GA) and SVM, as shown in the application of fault diagnosis of the turbo pump rotor. 相似文献
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Ying-Kui Gu Xiao-Qing Zhou Dong-Ping Yu Yan-Jun Shen 《Journal of Mechanical Science and Technology》2018,32(11):5079-5088
To effectively extract the fault feature information of rolling bearings and improve the performance of fault diagnosis, a fault diagnosis method based on principal component analysis and support vector machine was presented, and the rolling bearings signals with different fault states were collected. To address the limitation on effectively dealing with the raw vibration signals by the traditional signal processing technology based on Fourier transform, wavelet packet decomposition was employed to extract the features of bearing faults such as outer ring flaking, inner ring flaking, roller flaking and normal condition. Compared with the previous literature on fault diagnosis using principal component analysis (PCA) and support vector machine (SVM), one-to-one and one-to-many algorithms were taken into account. Additionally, the effect of four kernel functions, such as liner kernel function, polynomial kernel function, radial basis function and hyperbolic tangent kernel function, on the performance of SVM classifier was investigated, and the optimal hype-parameters of SVM classifier model were determined by genetic algorithm optimization. PCA was employed for dimension reduction, so as to reduce the computational complexity. The principal components that reached more than 95 % cumulative contribution rate were extracted by PCA and were input into SVM and BP neural network classifiers for identification. Results show that the fault feature dimensionality of the rolling bearing is reduced from 8-dimensions to 5-dimensions, which can still characterize the bearing status effectively, and the computational complexity is reduced as well. Compared with the raw feature set, PCA has a higher fault diagnosis accuracy (more than 97 %), and a shorter diagnosis time relatively. To better verify the superiority of the proposed method, SVM classification results were compared with the results of BP neural network. It is concluded that SVM classifier achieved a better performance than BP neural network classifier in terms of the classification accuracy and time-cost. 相似文献
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Gang-Min Lim Dong-Myung Bae Joo-Hyung Kim 《Journal of Mechanical Science and Technology》2014,28(8):2947-2952
Feature-based classification techniques consist of data acquisition, preprocessing, feature representation, feature calculation, feature selection, and classifiers. They are useful for online, real-time condition monitoring and fault diagnosis / features, which are now available with the development of information technologies and various measurement techniques. In this paper, an intelligent feature-based fault diagnosis is suggested, developed, and compared with vibration signals and thermal images. Fault diagnosis is performed using thermal imaging along with support vector machine (SVM) classification to simulate machinery faults, resulting in an accuracy level comparable to vibration signals. The observed results show that fault diagnosis using thermal images for rotating machines can be applied to industrial areas as a novel intelligent fault diagnostic method with plausible accuracy. It can be also proposed as a unique non-contact method to analyze rotating systems in mass production lines within a short time. 相似文献
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针对高速列车横向减振器故障振动信号具有非线性和非平稳特征、特征信号提取相对困难问题,提出了变分模态分解和多尺度熵结合的特征提取方法。原始信号经变分模态分解方法处理后,被分解为若干本征模态,利用互信息指标筛选有效模态,求多尺度熵组成特征向量,通过特征评价方法去除冗余特征,最终将最优特征子集输入支持向量机识别横向减振器的故障类型。实验结果表明,该方法能有效提取振动信号的特征,实现横向减振器故障的有效判别,验证了该方法在高速列车横向减振器故障诊断的可行性。 相似文献