共查询到19条相似文献,搜索用时 109 毫秒
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分形维数在内燃机振动诊断中的应用 总被引:4,自引:0,他引:4
将分形理论引入内燃机的振动诊断中,根据内燃机的配气定时,着重研究了缸盖振动信号中对应燃烧段的数据,计算其关联维数,将关联维数用于刻划内燃机缸盖在气门不同状态时表现的非线性行为,从而进行故障诊断与分类。结果表明,当气门在不同状态时,缺盖振动信号中对应燃烧段数据的关联维数是不同的,可以将其作为判断气门漏气的一个诊断特征量。 相似文献
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振动监测技术是当前柴油机状态监测和故障诊断的基本方法,本文通过分析柴油机缸盖振动信号的吸引子图来定性刻画其振动的复杂度,并采用抽区间分析法,利用二进小波变换对柴油机燃烧段缸盖振动信号分解,提取其高频信息,结合G-P算法计算其高频信息的关联维数.研究表明,计算柴油机缸盖振动信号的关联维数是一种诊断柴油机气门故障的有效方法. 相似文献
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将分形应用在刀具状态监测中,随着刀具磨损量的增加,刀具与工件之间的磨损加剧,振动信号的波形变化越来越不规则,信号的分形维数逐渐增大.盒维数和信息维数变化较小,但变化趋势明显;关联维数的变化相对较大,新刀的关联维数最小,报废刀的关联维数明显增大.识别结果表明,刀具在整个磨损历程中振动信号分形维数的变化规律,其大小能较好地反映刀具不同磨损状态,运用振动信号的分形维数可以有效实现刀具磨损状态的监测. 相似文献
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Chengdong Wang Youyun Zhang Zhenyuan Zhong 《Mechanical Systems and Signal Processing》2008,22(8):1981-1993
In this paper, the Wigner–Ville distributions (WVD) of vibration acceleration signals which were acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images; and the probabilistic neural networks (PNN) were directly used to classify the time–frequency images after the images were normalized. By this way, the fault diagnosis of valve train was transferred to the classification of time–frequency images. As there is no need to extract further fault features (such as eigenvalues or symptom parameters) from time–frequency distributions before classification, the fault diagnosis process is highly simplified. The experimental results show that the faults of diesel valve trains can be classified accurately by the proposed methods. 相似文献
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内燃机气阀漏气故障的高阶谱分析 总被引:2,自引:0,他引:2
将高阶谱理论引入内燃机的振动诊断中,分析了不同状态时缸盖表面振动信号的三阶谱特性,并计算出三阶谱的峰值,用于刻划各状态时缸盖系统的非线性行为。结果表明:正常状态时缸盖表面振动信号的三阶谱接近为零,可以认为缸盖系统是线性系统;当气阀发生漏气故障时,缸盖表面振动信号的三阶谱就会出现较大的峰值,而且不同状态时所对应的峰值也存在着较大差别,说明不同气阀漏气状态时缸盖系统表现出不同程度的非线性。可以将三阶谱的峰值作为判断气阀是否漏气的一个诊断特征量,同时也为诊断内燃机气阀的早期漏气故障提供了依据。 相似文献
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Wang Qinghua Zhang Youyun Cai Lei Zhu Yongsheng 《Mechanical Systems and Signal Processing》2009,23(5):1683-1695
It is well known that the vibration signals are unstable when there is some failure in machinery. So in this paper, the cone-shaped kernel distributions (CKD) of vibration acceleration signals acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images. Meanwhile, non-negative matrix factorization (NMF) was used to decompose multivariate data, and neural network ensemble (NNE), which is of better generalization capability for classification than a single neural network, was used to perform intelligent diagnosis without further fault feature (such as eigenvalues or symptom parameters) extraction from time–frequency distributions. It is shown by the experimental results that the faults of diesel valve trains can be accurately classified by the proposed method. 相似文献
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Jing Ya-Bing Liu Chang-Wen Bi Feng-Rong Bi Xiao-Yang Wang Xia Shao Kang 《机械工程学报(英文版)》2017,30(4):991-1007
Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying features. To investigate the fault diagnosis of diesel engines,fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification.The results demonstrate that FastICA-SVM achieves higher classification accuracy and makes better generalization performance in small samples recognition. Besides,the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier Fast ICASVM and could produce the excellent classification results.The proposed methodology improves the accuracy of feature extraction and the fault diagnosis of diesel engines. 相似文献
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图像处理在内燃机故障诊断中的应用研究 总被引:1,自引:0,他引:1
在应用图像处理进行故障诊断的基础上 ,探讨了应用这一技术进行内燃机故障诊断与状态监测时应注意的一些问题。指出利用缸盖振动信号小波包分解后的时 -频分布图的灰度直方图进行故障诊断的效果并不好 ,并分析了原因 ;进行了信号加噪声与不加噪声的诊断效果对比 ,发现噪声对基于图像处理的气阀机构故障诊断的影响不大 ,验证了这一方法的工程实用性和可行性 相似文献
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针对内燃机气阀机构的故障诊断问题,提出一种将离散广义S变换和双向二维主成分分析(TD-2DPCA)相结合的诊断方法。该方法首先利用离散广义S变换将内燃机缸盖振动信号生成振动谱图像,然后利用TD-2DPCA对图像进行特征提取,有效减小特征系数矩阵的维数,最后,通过最近邻分类器进行分类识别。将该方法应用于内燃机气阀机构8种工况的诊断实例中,对比不同时频表征及特征提取方法的计算效率和识别精度,结果表明该方法可为内燃机故障诊断提供一条新途径。 相似文献
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Fault feature extraction has a positive effect on accurate diagnosis of diesel engine. Currently, studies of fault feature extraction have focused on the time domain or the frequency domain of signals. However, early fault signals are mostly weak energy signals, and time domain or frequency domain features will be overwhelmed by strong back?ground noise. In order consistent features to be extracted that accurately represent the state of the engine, bispectrum estimation is used to analyze the nonlinearity, non?Gaussianity and quadratic phase coupling(QPC) information of the engine vibration signals under different conditions. Digital image processing and fractal theory is used to extract the fractal features of the bispectrum pictures. The outcomes demonstrate that the diesel engine vibration signal bispectrum under different working conditions shows an obvious differences and the most complicated bispectrum is in the normal state. The fractal dimension of various invalid signs is novel and diverse fractal parameters were utilized to separate and characterize them. The value of the fractal dimension is consistent with the non?Gaussian intensity of the signal, so it can be used as an eigenvalue of fault diagnosis, and also be used as a non?Gaussian signal strength indicator. Consequently, a symptomatic approach in view of the hypothetical outcome is inferred and checked by the examination of vibration signals from the diesel motor. The proposed research provides the basis for on?line monitoring and diagnosis of valve train faults. 相似文献