共查询到18条相似文献,搜索用时 171 毫秒
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为了更好地准确识别轴承故障特征非线性分类问题,提出了一种基于IFOA-SVM的故障分类识别方法.使用变分模态分解方法对轴承振动信号进行分解处理,以模态分量的模糊近似熵和能量熵构成故障特征向量;基于"一对一"策略拓展设计了OVO-SVM多分类器,构造多项式核函数和径向基核函数组合的混合核函数,使用IFOA算法对SVM分类器的核函数比例系数λ、径向基核函数宽度参数σ、惩罚因子C等关键参数进行优化,构建IFOA-SVM故障分类识别模型;提出了轴承故障识别流程.结果表明,该方法可以实现对轴承故障特征准确高效的识别. 相似文献
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结合全矢谱和径向基概率神经网络的优点,提出一种故障诊断的新方法,该方法是以提取全矢幅值谱的特征输入到径向基概率神经网络分类器进行故障识别.试验结果表明,该方法与传统单通道相比故障正确识别率很高,把它应用于旋转机械故障诊断是有效的. 相似文献
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基于径向基函数神经网络的柴油机故障诊断 总被引:17,自引:0,他引:17
提出一种应用径向基函数(RBF)神经网络解决故障诊断问题的方法,并将其应用于柴油机故障诊断与识别。在RBF神经网络中采用了一种减聚类的学习算法来确定径向基函数的相应参数,从而使神经网络结构得到优化。实例仿真结果表明,RBF神经网络学习收敛较快,对故障识别性能好。 相似文献
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《工业仪表与自动化装置》2018,(6)
该文阐述了径向基函数(radial basis function,RBF)神经网络的基本原理和算法,并针对RBF神经网络存在的隐含层的隐层单元数目及中心向量、扩展参数难以确定的问题,利用减聚类算法进行RBF网络的改进,建立应用于滚动轴承故障诊断与识别的RBF神经网络智能识别模型,并通过实验与BP(back propagation)神经网络进行比较分析研究。结果表明,减聚类算法能够有效地确定网络参数,改进的RBF神经网络对预设滚动轴承故障能够准确诊断,并且具有训练速度快的特点。 相似文献
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提出一种基于径向基函数神经网络的铣刀磨损监控方法,径向基函数神经网络的输出是刀具磨损的具体值,这样有利于对刀具磨损进行各种实时补偿。实验表明,利用径向基函数神经网络进行状态识别可对小型立铣刀的磨损进行监控,能够取得良好的效果,同时证明RBF网络的训练速度优于BP网络。 相似文献
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故障诊断技术面临两大难题 ,第一如何“测量”故障的发育 ,第二如何预测一个有故障的机器或构件还能正常运行多久。本文用小波基函数神经网络技术解决了这两大课题。首先建立了小波基函数神经网络故障预后模型 ,用高斯基函数和Marr小波函数作为尺度函数 ,基函数中心的计算用二进展开函数和k次聚类函数。诊断实践表明 ,当轴承内表面产生间隙以后 ,应用训练后的小波基函数神经网络能够成功地对其间隙的发育进行预测。 相似文献
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Chih-Hao Chen Rong-Juin Shyu Chih-Kao Ma 《Journal of Mechanical Science and Technology》2007,21(7):1058-1065
This paper presents a new fault diagnosis procedure for rotating machinery using the wavelet packets-fractal technology and
a radial basis function neural network. The main purpose is to investigate different fault conditions for rotating machinery,
such as imbalance, misalignment, base looseness and combination of imbalance and misalignment. In this study, we measured
the non-stationary vibration signals induced by these fault conditions. Applying wavelet packets transform to these signals,
the fractal dimension of each frequency channel was extracted and the box counting dimension was used to depict the failure
characteristics of the fault conditions. The failure modes were then identified by a radial basis function neural network.
An experiment was conducted and the results showed that the proposed method can detect and recognize different kinds of fault
conditions. Therefore, it is concluded that the combination of wavelet packets-fractal technology and neural networks can
provide an effective method to diagnose fault conditions of rotating machinery. 相似文献
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研究了小波包分析与人工神经网络结合起来应用于轴承故障诊断的问题。采用小波包分析对其提取频域能量特征向量,利用径向基函数神经网络完成滚动轴承故障诊断。 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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APPROACH TO FAULT ON-LINE DETECTION AND DIAGNOSIS BASED ON NEURAL NETWORKS FOR ROBOT IN FMS 总被引:1,自引:0,他引:1
APPROACHTOFAULTONLINEDETECTIONANDDIAGNOSISBASEDONNEURALNETWORKSFORROBOTINFMSShiTianyunZhangZhijingWangXinyiZhuXiaoyanSchoolo... 相似文献
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基于RBF神经网络的齿轮箱故障诊断 总被引:1,自引:0,他引:1
阐述径向基函数(radial base function,RBF)神经网络的基本原理和算法,将其应用于齿轮箱故障诊断与识别,建立齿轮箱的BRF故障诊断模型,并与BP(back propagation)神经网络、学习率自适应BP神经网络进行对比分析研究。结果表明,RBF神经网络性能优于BP神经网络,具有较快的训练速度、较强的非线性映射能力和精度较高的故障识别能力,非常适用于齿轮箱的状态监测和故障诊断。但在具体应用中应当注意,RBF网络的训练样本必须含有一定的噪声,以提高网络的容噪性能;各类故障的训练样本数不能太少,否则RBF网络的故障分类能力很差。 相似文献