The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform

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.     

齿轮故障诊断技术现状与展望

介绍了齿轮故障理论及诊断技术的现状;对齿轮故障机理研究、齿轮故障简易诊断技术、精密诊断技术、诊断技术最新发展进行了分类阐述,并对齿轮故障诊断技术的未来发展方向提出了看法。     

虚拟样机技术及其在ADAMS中的实践

运用虚拟样机技术，可以大大简化机械产品的设计开发过程，大幅度缩短产品开发周期和降低开发费用，提高产品的系统级性能，获得最优化和创新的设计产品。ADAMS是目前著名的虚拟样机分析软件。运用ADAMS软件，用户可以很方便地对虚拟样机进行静力学、运动学和动力学分析。这里阐述了虚拟样机技术的概念、体系结构和关键技术，并对ADAMS软件及应用步骤进行了介绍。最后通过7s16汽车空调压缩机给出了ADAMS的一个具体应用实例。     

指数加权主元分析法及其在故障诊断中的应用

在研究主元分析法(PCA)理论的基础上,提出指数加权主元分析(EWPCA)算法。这种算法通过不断更新相关矩阵来实时监视动态生产过程中的超时趋势和设定点改变等状态。实验结果表明,该方法可以较好地反映生产过程中的实时信息,并能有效检测出系统的异常状况,具有广阔的实际应用前景。     

矢Wigner高阶谱在齿轮故障诊断中的研究

全矢谱技术能够有效的融合同源双通道的信息,融合后的信息无论从结构上还是能量上都能够真实的反映转子的实际运行状态。针对Wigner高阶谱只能处理单通道信息这一缺点,将全矢谱技术与Wigner高阶谱相结合,提出矢Wigner高阶谱分析方法,给出了其定义与算法,并将其应用到齿轮故障诊断中。仿真研究及实例验证结果表明,矢Wigner高阶谱能够克服基于单通道信息的Wigner高阶谱的片面性,并且该分析方法同时具备两者的优点,为齿轮故障诊断提供更可靠的依据。     

基于非线性主成分分析的车身制造偏差源诊断

对主成分分析在轿车车身制造偏差源诊断方面的不足进行了分析,进而在其基础上提出采用非线性主成分分析,从而更有效、更准确地来对轿车车身制造偏差源进行诊断,并给出了计算实例。实例证明该方法优于主成分分析,并在工程上有较大的可用性。     

空间滚柱凸轮曲面建模及虚拟样机计算处理方法

若无行之有效的计算和处理方法，直接应用大型CAD及CAE软件进行空间滚柱凸轮曲面的三维建模，及凸轮机构虚拟样机的运动学、动力学分析是很难的。论文应用广义矢量微分函数来描述展开后的滚轮凸轮机构，解决的空间滚轮凸轮机构凸轮曲线的正、反面设计问题，并在实际应用中取得了显著的效果。     

A new adaptive cascaded stochastic resonance method for impact features extraction in gear fault diagnosis

Gearboxes are widely used in engineering machinery, but tough operation environments often make them subject to failure. And the emergence of periodic impact components is generally associated with gear failure in vibration analysis. However, effective extraction of weak impact features submerged in strong noise has remained a major challenge. Therefore, the paper presents a new adaptive cascaded stochastic resonance (SR) method for impact features extraction in gear fault diagnosis. Through the multi-filtered procession of cascaded SR, the weak impact features can be further enhanced to be more evident in the time domain. By analyzing the characteristics of non-dimensional index for impact signal detection, new measurement indexes are constructed, and can further promote the extraction capability of SR for impact features by combining the data segmentation algorithm via sliding window. Simulation and application have confirmed the effectiveness and superiority of the proposed method in gear fault diagnosis.     

基于虚拟样机技术的渐开线少齿数齿轮传动性能分析

齿轮传动是一种常见的机械传动形式。其中齿数少于10的少齿数齿轮是一种特殊齿轮,已经形成了一系列设计理论和方法。随着CAD/CAM技术的发展,分析少齿轮传动的平稳性和可靠性的方法除了物理样机试验外,在设计早期,虚拟样机技术是一种方便快捷、低成本的方法。提出基于虚拟样机技术对设计的渐开线少齿数齿轮传动进行运动和动力性能分析,提高设计成功率的思想。通过建立精确的三维模型,设置仿真运行环境,仿真后分析,确保传动过程连续、平稳。     

基于DataSocket和小波消噪的齿轮故障远程监测与诊断

介绍利用LabVIEW平台检测齿轮故障信号 ,叙述DataSocket协议和使用DataSocket技术进行远程监控的方法 ,给出在LabVIEW的环境内 ,使用MATLAB脚本节点对齿轮振动信号进行小波消噪和分解 ,提取齿轮故障特征信息 ,实现齿轮故障的远程诊断的方案。     

An improvement EMD method based on the optimized rational Hermite interpolation approach and its application to gear fault diagnosis

A demodulation technique based on improvement empirical mode decomposition (EMD) is investigated in this paper. Firstly, the problem of the envelope line in EMD is introduced and the drawbacks of two classic interpolation methods, cubic spline interpolation method and cubic Hermite interpolation method are discussed; then a new envelope interpolation method called optimized rational Hermite interpolation method (O-EMD) is proposed, which has a shape controlling parameter compared with the cubic Hermite interpolation algorithm. At the same time, in order to improve the envelope approximation accuracy of local mean, the parameter determining criterion is put forward and an optimization with Genetic Algorithm (GA) is applied to automatic select the suitable shape controlling parameter in each sifting process. The effectiveness of O-EMD method is validated by the numerical simulations and an application to gear fault diagnosis. Results demonstrate that O-EMD method can improve the reliability and accuracy significantly compared with traditional EMD method.     

A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM

In the condition monitoring of gear reducer, the labeled fault samples are sparse and expensive, while the unlabeled samples are plentiful and cheap. How to diagnose the faults occurring in complex and special gear reducer effectively becomes a troublesome problem in case of insufficient labeled samples or excess unlabeled samples. This paper presents a novel model for fault diagnosis based on empirical mode decomposition (EMD) and multi-class transductive support vector machine (TSVM), which is applied to diagnose the faults of the gear reducer. The experimental results obtain a very high diagnosis accuracy. Even though the number of unlabeled samples is 50 times as that of labeled samples, the mean of testing accuracy of the proposed novel method can reach at 91.62%, which distinctly precedes the testing success rates of the other similar models in the same experimental condition.     

机车走行部故障在线诊断的特征分析方法研究

在讨论特征分析方法原理的基础上，针对机车走行部故障在线监测过程中存在的信号分析与处理问题，运用整周期等角度采样方法将时域振动信号转换为角域信号，采用FFT变换将角域信号变换为对应的特征频谱，通过谱估计、谱图分析得到机车走行部各零部件的故障特征谱值，再根据该特征谱值识别机车走行部各零部件的故障。然后，根据机车走行部故障诊断的实际需要，设计了一套基于特征分析方法的机车走行部故障在线诊断系统。实验结果表明，该方法能准确、可靠地识别机车走行部故障。     

基于虚拟样机技术的无级变速器设计与研究

无级变速器(CVT)能够在一定范围内连续实现速度改变,适用范围宽,应用前景广。利用虚拟样机技术实现无级变速器的设计与研发,具有研发周期短,成本低,并且能够实现产品全生命周期管理。通过在多体系统优化仿真软件RecurDyn中建立CVT虚拟样机模型进行运动学和动力学分析和仿真,全面了解CVT各方面性能,并将仿真结果与理论计算结果对比,从而证明虚拟样机技术的可行性和先进性,并具有很大的推广价值。     

齿轮系机构结构分解和型综合方法研究

通过对齿轮系机构结构分析,给出了齿轮系机构运动链结构的基本组成单元——基本结构链和基本结构补链,从而可以将任意齿轮系机构运动链分解成基本齿轮系单元运动链与一组有序的基本结构链和基本结构补链,实现齿轮系机构结构分解;如此对应,通过运用基本结构链与低级齿轮系机构上对应的基本结构补链的和运算,可得到更高一级齿轮系机构,实现齿轮系机构类型的综合与设计。     

基于RBF神经网络的非线性主元分析方法

本文在分析非线性主成分曲线性质基础上,提出了基于聚类、线性主成分分析、神经网络技术的非线性主成分分析方法.该方法与以往方法比较,在概念上具有和线性主成分分析相同的解释,并且给出了非线性主成分得分和负载的计算方法;在结构上较为简单,采用的神经网络结构为3层,训练容易.网络训练的数据样本采用聚类和线性主成分分析方法获得,解决了以往方法缺乏训练数据的问题.数字仿真和三水箱实验验证了提出方法的有效性.     

基于虚拟样机与有限元技术的自卸车举升机构设计

运用虚拟样机技术对自卸车举升机构的位置布置进行设计，在完成对举升机构零部件的形位参数设计后，对举升机构零部件三角板进行受力分析，结合有限元技术，对三角板进行了最大载荷工况下的结构应力分析。验证举升机构零部件的设计合理，提出举升机构新的设计方法。     

多分辨率能量波动-近似熵方法的设备故障诊断研究

结合小波包分解(WPT)方法和近似熵(ApEn)方法,提出一种多分辨率能量波动-近似熵方法的设备故障诊断技术.首先利用小波包分解将振动信号分解为各频率成分正交、无冗余和无遗漏的子信号,然后对子信号进行能量波动检验,给出故障的存在性诊断;对于存在故障的子信号进行近似熵计算,根据近似熵值的大小,分析信号的复杂性程度,即故障的严重程度.仿真及工程验证结果表明,提出的方法可以有效诊断设备故障的存在性及严重程度.     

Fault diagnosis method of rolling bearing using principal component analysis and support vector machine

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.     

基于主元分析确定多变量系统控制极限的方法

针对存在多个稳定状态的多变量统计过程控制的控制极限问题,提出先分析系统变量的历史数据得到其经验分布,确定该变量的不同稳定状态及对应的状态域;将数据样本按不同的状态分组标准化后,分别进行主元分析,得到不同稳定状态下的Hotellings T2及平方预测误差SPE（Q）控制极限。用本方法和普通主元分析法对某钢铁公司局部蒸汽管网的流量数据模拟监控的效果进行对比,表明本方法由于区分不同状态,确定的Hotellings T2及平方预测误差SPE（Q）控制极限更精确,能有效降低漏报警和误报警的概率。