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滚动轴承性能退化评估是预诊断的提前和基础,对在役滚动轴承实施在线状态监测和性能退化评估具有重要意义。针对概率相似度量评估方法存在模型复杂、容易过早饱和等现象,提出一种基于自回归时序 (autoregressive model,简称AR)模型和多元状态估计(multivariate state estimation technique, 简称MSET)的滚动轴承性能在线评估方法,其中AR模型用于提取轴承振动信号的状态特征,MSET模型用于重构AR模型系数。首先,提取正常运行状态下振动信号的AR模型系数构建MSET模型的历史记忆矩阵;其次,将待测信号的AR系数作为观测向量输入MSET模型中得到重构后的估计向量;最后,由原始AR系数和重构AR系数分别构造自回归模型,并各自完成对待测信号的时序建模,将两自回归模型所得残差序列的均方根值之差作为性能劣化程度指标。离散实验数据和全寿命疲劳实验数据分析结果表明,该方法能够有效检测早期故障,且具有与轴承故障发展趋势一致性更好等优点。 相似文献
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滚动轴承在长期的工作过程中其性能会出现不同程度的退化,如果能对滚动轴承的退化状态进行识别就可以做好维护措施。用自回归模型(Autoregressive model, AR)对滚动轴承全寿命周期的振动信号提取其系数及残差,用正常样本和失效样本特征建立模糊C均值模型(Fuzzy C Mean, FCM),用轴承正常样本的特征数据建立隐马尔科夫(Hidden Markov model, HMM)模型,将轴承的测试样本信号输入建立的FCM和HMM模型得到的两个退化指标,再将其作为特征矩阵输入到FCM模型,得到融合方法的性能退化曲线,结果表明该方法集中了空间统计距离模型和概率统计模型两者的优势,最后用IEEE PHM2012实验数据进行验证,表明所述方法与滚动轴承性能退化趋势保持一致并且可以提早发现早期故障。 相似文献
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针对滚动轴承早期故障易受噪声影响难以准确提取特征信息的问题,提出了一种基于最大相关峭度解卷积(MCKD)和变分模态分解(VMD)关联维数的故障诊断AR模型.采用MCKD对滚动轴承振动信号进行降噪处理,滤除噪声影响;对降噪后的信号进行VMD分解,选择对故障特征敏感的IMF分量进行信号重构,并对重构信号建立AR模型,获取自回归参数;计算在指定嵌入维数上自回归参数的关联维数,对滚动轴承的故障进行诊断.实验结果表明,所提方法能够有效提取故障信号中的特征信息,证明了方法的有效性. 相似文献
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《Mechanical Systems and Signal Processing》2007,21(5):1953-1982
A novel technique for detection of gearbox deterioration is proposed in Part I of this study. The proposed technique makes use of a time-varying autoregressive (AR) model and establishes a compromised AR model based on healthy gear motion residual (GMR) signals under varying load conditions and employs the Kolmogorov–Smirnov (K–S) goodness-of-fit (GOF) test statistic as a measure of gear condition. The order of the time-varying AR model is selected using a novel model order selection technique with the aid of hypothesis tests. The principal criterion for the selection of AR model order requires that the normality of the AR model residuals of the non-stationary healthy GMR signals under varying load conditions can be guaranteed. In the case where such orders are available, the one that results in the statistically least variance of the gear condition indicator, i.e. the K–S test statistic, is selected with the aid of the Bartlett's test. In the case where, under all considered orders, the normality condition cannot be met for all non-stationary healthy GMR signals, the order that results in the least violation against the normality condition can be identified with the aid of the Satterthwaite's t′-test. The coefficients of the time-varying AR model are estimated by means of a noise-adaptive Kalman filter.Validation of the proposed technique is carried out by using two sets of simulated entire lifetime gear vibration signals, i.e. clean and contaminated signals, to simulate the cases of sufficient and insufficient removal of background noise, respectively. The simulated tests demonstrate that the proposed technique possesses appealing effectiveness in identifying the optimum AR model order for robust gear condition detection under varying load conditions. The optimum performance of this technique is further confirmed by examining alternative orders. 相似文献
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针对平稳自回归模型无法准确描述滚动轴承振动信号的非平稳性,提出一种结合小波包分解与自回归模型的故障特征提取方法,以提取能准确反映轴承运行状态的特征向量。首先,通过小波包变换对滚动轴承运行时产生的非平稳振动信号进行分解,得到一系列刻画原始信号特征的系数;然后,利用自相关算法对各系数建立自回归模型,并将自回归模型的参数作为特征向量;最后,采用支持向量机分类器对提取的特征向量进行故障分类,从而实现滚动轴承的智能故障诊断。仿真结果表明该方法的有效性。 相似文献
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Effective fault location classification and especially performance degradation assessment of a roller bearing have been the subject extensive research, which can reduce costs and the nonscheduled down time. In this paper, a new fault diagnosis method based on multiple features, kernel principal component analysis (KPCA) and particle swarm optimization-support vector machine (PSO-SVM) is put forward. First, traditional features of the vibration signals in time-domain and frequency-domain are calculated, and then two types of features referred to as singular values and AR model parameters based on ensemble empirical mode decomposition (EEMD) are introduced. After that, the original feature vectors are mapped into higher dimensional space and the kernel principal components are extracted as new feature vectors, which are used as inputs to PSO-SVM. The experimental results show that the new diagnosis approach proposed in this paper can identify not only the fault locations but also the performance degradation of the roller bearing. 相似文献
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提出了一种小波包-AR谱估计和计算散度相结合的汽车变速器轴承故障特征提取方法.将6种不同磨损状况下的变速器轴承振动信号进行小波包分解,重构各频段信号并进行自回归(auto regressive,简称AR)谱估计,最后计算各故障轴承到新轴承之间的散度值.试验结果表明,不论是轴承的轴向间隙,还是径向间隙差异及疲劳剥落,在小波包-AR谱的谱图上均有明显的反映,该方法可以有效提取出汽车变速器轴承振动信号中的故障特征. 相似文献
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Planetary gear set is the critical component in helicopter transmission train,and an important problem in condition monitoring and health management of planetary gear set is quantitative damage detection.In order to resolve this problem,an approach based on physical models is presented to detect damage quantitatively in planetary gear set.A particular emphasis is put on a feature generation and selection method,which is used for sun gear tooth breakage damage detection quantitatively in planetary gear box of helicopter transmission system.In this feature generation procedure,the pure torsional dynamical models of 2K-H planetary gear set is established for healthy case and sun gear tooth-breakage case.Then,a feature based on the spectrum of simulation signals of the dynamical models is generated.Aiming at selecting the best feature suitable for quantitative damage detection,a two-sample Z-test procedure is used to analyze the performance of features on damage evolution tracing.A feature named SR,which had better performance in tracking damage,is proposed to detect damage in planetary gear set.Meanwhile,the sun gear tooth-chipped seeded experiments with different severity are designed to validate the method above,and then the test vibration signal is picked up and used for damage detection.With the results of several experiments for quantitative damage detection,the feasibility and the effect of this approach are verified.The proposed method can supply an effective tool for degradation state identification in condition monitoring and health management of helicopter transmission system. 相似文献
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基于自回归模型的齿轮轴破损诊断 总被引:5,自引:0,他引:5
齿轮轴失效引起的齿轮箱振动行为与轮齿失效引起的齿轮箱振动行为不同.传统的齿轮故障诊断方法大多针对于轮齿破损,难以有效识别齿轮轴破损.用自回归模型拟合正常齿轮振动的时域同步平均信号,利用Akaike判据获得自回归模型的阶数,用Levinson-Durbin递归算法求解Yule-Waker方程获得自回归模型的系数.将建立的自回归模型作为线性滤波器处理齿轮箱振动信号,获得预测误差信号.之后对预测误差信号进行两样本Kolmogorov-Smimov检验,获得正常齿轮轴振动信号和待处理齿轮轴振动信号预测误差的K-S统计距离和相似概率,并将其作为齿轮轴破损特征指标量.实际试验表明这一特征指标的有效性. 相似文献
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针对滚动轴承在长期工作过程中性能会出现不同程度的退化,提出一种融合FCM-SVDD模型的方法。利用自回归模型(AR)对轴承全寿命周期数据进行特征提取,再将提取的特征参数经过归一化处理后,用正常和失效样本特征建立模糊C均值(FCM)模型,用正常样本的特征数据建立支持向量数据描述(SVDD)模型,再将测试样本特征输入建立的FCM和SVDD模型得到的两个退化指标,将得到的退化指标作为特征矩阵输入到FCM模型,得到融合方法的性能退化曲线。描绘性能退化曲线,并对信号进行包络谱分析,验证初始故障位置。结果表明该方法对轴承初始故障点更加敏感,退化趋势更加明显,利用美国辛辛那提大学智能维护中心的轴承全寿命周期数据验证该方法的有效性和优越性。 相似文献