共查询到19条相似文献,搜索用时 109 毫秒
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提出了基于小波分析和修正指数分布(modifiedexponentialdistribution,MED)的齿轮故障诊断方法,该方法采用小波包将齿轮振动信号分解为若干个频率段,然后选择合适的频率段进行小波包重构,对重构后的信号进行MED分析,得到齿轮振动信号的小波包时-频分布,进而从中提取齿轮振动信号故障的故障特征.对具有裂纹的齿轮振动信号分析结果表明了基于小波分析和MED的齿轮故障诊断方法的有效性. 相似文献
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小波-神经网络在齿轮故障诊断中的应用 总被引:1,自引:0,他引:1
基于齿轮箱故障齿轮的特征提取,提出了将小波包分析与神经网络结合的齿轮故障诊断方法.对齿轮信号进行3层小波包分解,构造小波包特征向量作为故障样本.用训练好的BP神经网络进行故障诊断,实验结果表明该方法能够有效地诊断出齿轮的故障类型. 相似文献
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《机械传动》2016,(4):33-37
针对表征齿轮故障特征信息难提取与极限学习机输入权值与隐含层节点阈值随机选取,致使齿轮故障分类模型泛化能力弱、精度差的问题,提出一种基于小波包最优节点能量特征的BA-ELM齿轮故障诊断方法。该方法首先将齿轮振动信号经过小波包分解,再利用分解所得各节点信号与原信号的相关系数选取出最优节点并计算其能量特征;其次,利用蝙蝠算法优化极限学习机的输入权值与隐含层节点阈值,建立BA-ELM的齿轮故障分类模型;最后,将所得小波包最优节点能量特征向量作为模型输入进行齿轮不同故障状态的分类识别。实验结果表明,与基于SVM和ELM的故障分类方法相比,基于小波包最优节点能量特征的BA-ELM齿轮故障诊断方法具有更高的分类精度,更强的泛化能力。 相似文献
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为了研究采煤机摇臂传动齿轮的振动分析方法并进行实机振源定位验证,首先,采用小波分析对采煤机摇臂振动信号进行降噪处理和频谱分析,依据特征频率下的振幅结果确定故障齿轮的啮合频率;然后,通过Morlet小波包络解调分析获取边频带信号频谱特征,依据边频带特征频率下的振幅结果确定故障齿轮的转动频率;最后,对频谱分析和Morlet小波包络解调分析的结果进行综合分析,锁定故障齿轮的准确位置。对一台国产采煤机摇臂齿轮传动系统进行了振动测试与信号分析,结果表明,基于小波分析、频谱分析和Morlet小波包络解调分析相结合的振动分析方法可以实现对采煤机摇臂故障齿轮的准确定位,为强噪声环境下复杂齿轮传动系统的故障快速定位和现场定点维修提供了方法支持。 相似文献
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《Measurement》2014
The vibration signal of a gear system is selected as the original information of fault diagnosis and the gear system vibration equipment is established. The vibration acceleration signals of the normal gear, gear with tooth root crack fault, gear with pitch crack fault, gear with tooth wear fault and gear with multi-fault (tooth root crack & tooth wear fault) is collected in four kinds of speed conditions such as 300 rpm, 900 rpm, 1200 rpm and 1500 rpm. Using the method of wavelet threshold de-noising to denoise the original signal and decomposing the denoising signal utilizing the wavelet packet transform, then 16 frequency bands of decomposed signal are got. After restructuring the decomposing signal and obtaining the signal energy in each frequency band, the signal energy of the 16 bands is as the shortlisted fault characteristic data. Based on this, using the methods of principal component analysis (short for PCA) and kernel principal component analysis (short for KPCA) to extract the feature from the fault features of shortlisted 16-dimensional data feature, then the effect of reducing dimension analysis are compared. The fault classifications are displayed through the information that got from the first and the second principal component and kernel principal component, and these demonstrate they have a different and good effect of classification. Meanwhile, the article discusses the effect of feature extraction and classification that caused by the kernel function and the different options of its parameters. These provide a new method for a gear system fault feature extraction and classification. 相似文献
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基于复Morlet小波和系数相关的齿轮故障特征提取 总被引:2,自引:1,他引:1
针对大型机械测取的振动信号信噪比低,故障特征不明显,故障定位难度大,提出了基于复Morlet小波和系数相关的齿轮故障特征提取方法。该方法利用了复Morlet小波的幅值、相位组合信息对信号突变点具有更好的敏感特性和小波系数相关降噪特性,对被测信号进行复Morlet小波变换,再分别将小波系数的实部和虚部进行自相关处理,并将相关后系数的幅值和相位进行组合。该方法在对齿轮传动弱故障信号特征提取的试验结果表明,该方法与直接的复Morlet小波变换相比,能够有效去除噪声,更好地突出故障特征,对故障特征点进行更精确地定位。 相似文献
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Xihui Chen Gang Cheng Hongyu Li Yong Li 《Journal of Mechanical Science and Technology》2017,31(3):1035-1047
Planetary gear is widely used in large-scale complex mechanical systems. However, because of the particularity of planetary gear transmission, serious wear and fatigue crack failures often occur in the sun gear, planet gears, and inner gear ring. In addition, every type of fault will experience different degradation processes. Improving the operation reliability of mechanical equipment through fault diagnosis of planetary gears and monitoring their degradation process is beneficial. This paper proposes a planetary gear fault identification method based on Dual-tree Complex wavelet transform (DT-CWT) threshold denoising and Laplacian eigenmaps (LE). The noise reduction processing of the original signal is achieved by the DT-CWT threshold denoising method, which takes full advantage of DT-CWT and is combined with the wavelet threshold of rigrsure principle. The original high-dimensional feature set, including the time domain features, frequency domain features, permutation entropy, and fractal box dimension of the denoised signal, is constructed from multi-angles. To solve the problems of excessively large feature dimension and the existence of redundant information, the LE algorithm is used to reduce the dimension of the original high-dimensional feature set, and the low-dimensional sensitive features are obtained. Through the above method, the effective identification of different fault states and different degradation states of the planetary gear are achieved. 相似文献
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针对最佳小波参数的设定和齿轮裂纹故障振动信号频率成分复杂、信噪比低等问题,将遗传优化算法、小波脊线解调与局部特征尺度分解(local characteristic-scale decomposition,简称LCD)相结合,提出了基于LCD的自适应小波脊线解调方法。首先,采用LCD方法将原始信号分解为若干个内禀尺度分量(intrinsic scale component,简称ISC),并通过选择蕴含特征信息的ISC来实现信号降噪;然后,以小波能量熵为目标函数,采用遗传算法优化小波参数,得到自适应小波;最后,通过自适应小波分析提取ISC的小波脊线,从而实现对原始信号的解调分析。通过齿轮裂纹故障诊断实例验证了该方法的有效性和优越性。 相似文献
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NEW METHOD FOR WEAK FAULT FEATURE EXTRACTION BASED ON SECOND GENERATION WAVELET TRANSFORM AND ITS APPLICATION 总被引:5,自引:0,他引:5
Duan Chendong He ZhengjiaJiang HongkaiSchool of Mechanical Engineering Xi''''an Jiaotong University Xi''''an China 《机械工程学报(英文版)》2004,17(4):543-547
A new time-domain analysis method that uses second generation wavelet transform (SGWT) for weak fault feature extraction is proposed. To extract incipient fault feature, a biorthogonal wavelet with the characteristics of impact is constructed by using SGWT. Processing detail signal of SGWT with a sliding window devised on the basis of rotating operation cycle, and extracting modulus maximum from each window, fault features in time-domain are highlighted. To make further analysis on the reason of the fault, wavelet package transform based on SGWT is used to process vibration data again. Calculating the energy of each frequency-band, the energy distribution features of the signal are attained. Then taking account of the fault features and the energy distribution, the reason of the fault is worked out. An early impact-rub fault caused by axis misalignment and rotor imbalance is successfully detected by using this method in an oil refinery. 相似文献