基于小波包和分形盒维数的滚动轴承故障诊断

为诊断滚动轴承不同部件产生的故障,针对轴承故障信号具有非线性、非平稳振动的特点,运用小波包和分形理论,定量计算了滚动轴承不同部件故障信号及小波包重构信号的盒维数。实验结果表明,滚动轴承不同的故障类型具有不同的盒维数。正常滚动轴承盒维数最大,依次为滚珠故障盒维数、内环故障盒维数,外环故障盒维数最小。分形盒维数能定量地识别滚动轴承不同部件的故障,提高滚动轴承故障诊断的准确率,为滚动轴承智能故障诊断提供可靠依据。     

基于分形理论的滚动轴承故障诊断研究

针对滚动轴承振动信号的特点,从分形原理出发,利用分形维数的概念,对由滚动轴承故障产生的非平稳、非线性信号进行了分形特征的定量描述。试验结果表明,滚动轴承不同故障出现时,其分形维数明显不同。因此,可以利用分形维数有效地诊断出滚动轴承的故障。     

基于分形的滚动轴承故障模式的识别

针对滚动轴承振动信号的特点，从分形原理出发，利用盒维数的概念，对滚动轴承故障产生的非平稳、非线性信号进行了定量描述。试验结果表明，由于滚动轴承不同故障的动力学产生的机制不同，其盒维数明显不同。因此利用盒维数可以准确地识别滚动轴承的故障模式。     

基于混沌理论的滚动轴承故障诊断研究

采用小波包分析的方法对滚动轴承振动信号进行处理,提取滚动轴承特征信号,进一步应用混沌与分形方法研究了故障信号的混沌性,通过计算信号混沌特征量—关联维数,找出了内圈、外圈及滚动体状态信号在正常状态、轻微磨损状态、中度磨损状态、严重磨损状态下关联维数对故障状态的反应。实验结果印证了混沌方法用于研究该类型故障信号的可行性。     

EEMD和分形组合技术对ECS涡轮轴承故障特征提取的研究

针对飞机环控涡轮轴承运行时的非线性动力学特性,为了更加准确地分析轴承的故障,从振动信号分析的角度,提出基于EEMD和分形维数相结合的轴承状态特征量提取方法。先对轴承正常、内圈故障、外圈故障和保持架故障等不同运行状态下的振动信号进行EEMD分解,滤除噪声信号,提高信噪比,以减小背景噪声对分形的不利影响。然后对去噪信号再进行相空间重构,计算其关联维数并进行对比分析。实验结果表明:关联维数作为非线性几何不变量可以作为环控涡轮轴承运行状态的特征量;该方法能够准确有效地识别轴承的运行状态。     

分形维数及其在滚动轴承故障诊断中的应用

将分形维数用于刻划滚动轴承在不同故障状态下表现的非线性行为,进而对故障分类。试验结果表明,滚动轴承振动信号在不同故障状态下的分形维数是不同的,可以将分形维数作为识别滚动轴承故障的特征量。     

基于小波降噪与分形理论的滚动轴承故障诊断

为了识别滚动轴承振动信号中包含的故障类型,运用小波对采集到的信号进行降噪,通过计算降噪后振动信号的关联维数,判断信号中是否包含故障。并以关联维数为特征量,通过计算各工况之间的距离函数,判断属于何种故障的信号。结果表明,运用分形理论进行故障诊断具有很强的实用价值。     

基于关联维数的铁路货车滚动轴承故障诊断

将关联维数用于定量刻划滚动轴承在不同工作状态下的振动特征,进而对故障分类。同时提出用小波分析对原始数据进行降噪处理。文中以铁路货车轴箱197 726双排圆锥滚子轴承为例,计算了正常轮对、滚子损伤和外圈剥离三种情况下降噪处理前后振动信号的关联维数,分析结果表明,滚动轴承不同工作状态下的关联维数有明显差别,因此可以将关联维数作为识别滚动轴承故障的特征量。     

滚动轴承外圈失效的盒维数与信息维数特征

旋转机械故障诊断的关键在于故障特征的提取,由于轴承振动信号的非线性与非平稳性,传统平稳信号处理方法在故障特征提取中存在不足,而多重分形等非平稳方法存在计算冗余。认为故障状态下轴承振动信号的空间的占布与概率密度均发生变化,因此可采用盒维数与信息维数描述故障特征。在此基础上,借助美国辛辛那提大学IMS中心公开的全寿命轴承实验数据,分别分析了滚动轴承外圈在正常和故障情况的信息维数和盒维数,认为:在故障状态下,轴承振动信号的信息维数与盒维数均有降低,这一特点有利于提高故障模式的可分性。     

关联维数分析方法在液压泵多故障诊断中的应用研究

针对液压泵故障信号非线性和非平稳性特征,提出了利用相空间重构技术和分形理论相结合的特征关联维数提取方法。该方法将液压泵不同故障模式下获取的一维振动信号重构到高维相空间,进行信息深层挖掘;通过对相空间特征信号关联维数变化规律的分析,找出对故障反映敏感的关联维数,由此进行故障识别。通过实验验证,该方法提取的关联维数能有效反映液压泵的故障特征,为液压泵多故障诊断方法的研究提供可靠的特征信息,具有良好的应用前景。     

基于EMD和分形维数的转子系统故障诊断

提出了一种基于EMD方法和分形维数的转子系统故障诊断方法。利用EMD方法将转子振动信号进行分解,得到若干个基本模式分量,然后将包含主要故障信息的几个基本模式分量相加得到降噪后的转子振动信号,求得降噪后的转子振动信号的分形维数。试验数据的分析结果表明,在不同的故障状态下,采用EMD方法对转子振动信号降噪后求得的分形维数是不同的,从而可以通过分形维数的大小有效地判断转子系统的工作状态和故障类型。     

Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension

The development of non-linear dynamic theory brought a new method for recognising and predicting the complex non-linear dynamic behaviour. Fractal dimension can quantitatively describe the non-linear behaviour of vibration signal. In the present paper, the capacity dimension, information dimension and correlation dimension are applied to classify various fault types and evaluate various fault conditions of rolling element bearing, and the classification performance of each fractal dimension and their combinations are evaluated by using SVMs. Experiments on 10 fault data sets showed that the classification performance of the single fractal dimension is quite poor on most data sets, and for a given data set, each fractal dimension exhibited different classification ability, this indicates that various fractal dimensions contain various fault information. Experiments on different combinations of the fractal dimensions demonstrated that the combination of all these three fractal dimensions gets the highest score, but the classification performance is still poor on some data sets. In order to improve the classification performance of the SVM further, 11 time-domain statistical features are introduced to train the SVM together with three fractal dimensions, and the classification performance of the SVM is improved significantly. At the same time, experimental results showed that the classification performance of the SVM trained with 11 time-domain statistical features in tandem with three fractal dimensions outperforms that of the SVM trained only with 11 time-domain statistical features or with three fractal dimensions.     

基于现代非线性理论的汽轮发电机组故障诊断技术研究

运用小波理论、分形理论和混沌理论等非线性理论,对汽轮发电机组转子故障进行了综合分析和研究。对 所测某28 MW发电机组转子在三种不同工作状态下的时间序列进行了关联维数计算、小波包分解以及最大李雅 普诺夫指数计算,并结合其相轨迹图和庞加莱截面进行了分析与研究。结果表明,小波包分解重构技术具有很强 的消噪和非平稳信号提取能力;发电机组转子在不同工作状态下其时间序列的关联维数、李雅普诺夫指数具有明 显差别,且两量值相互补充、相互对应。由此提出:关联维数、最大李雅普诺夫指数可以作为刻画发电机组机械 故障特征的综合量化指标。该研究为非线性运动系统的在线监测、故障诊断和状态预测开辟了有效途径。     

多重分形方法在耦合故障诊断分类中的应用研究

运用多重分形理论,提出广义维数最小二乘法的计算公式,对实测的时域信号进行了广义维数计算,得到广义维数序列值,并从广义维数中获取盒维数、信息维数、关联维数以及敏感维数。对故障样本进行功率谱分析、广义维数计算分析,找出谱能量与分形维数的关系,对用分形维数分析故障的强度提供了依据。另外运用广义维数序列和数学方法相结合提出分形诊断分类方法,用广义维数最大相关系数和广义维数序列单值优化逼近原理方法,对待检信号的耦合故障分别进行了试验数据与理论响应模拟数据的诊断、识别分类,收到了良好的一致效果。通过对转子系统故障诊断的实例说明从广义维数中提取的各分形维数都能较好地对故障状态进行诊断、识别;且耦合故障的分形诊断分类方法具有较好的实效性。     

Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM

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.     

混沌分形理论在往复压缩机气阀故障诊断中的应用

提出用关联维数来定量描述往复压缩机气阀的工作状态,进而对气阀进行故障诊断.首先采用基于经验模态分解和奇异谱分析的基本思想,对气阀不同状态下的信号进行降躁处理,然后计算了信号的关联维数.分析结果表明,气阀不同运行状况对应的关联维数明显不同,因此可以用关联维数作为气阀不同工作状态的特征参数,从而提高了设备故障诊断的准确率.     

Fault Feature Extraction of Diesel Engine Based on Bispectrum Image Fractal Dimension

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.     

A fractal-dimension-based signal-processing technique and its use for nondestructive testing

A fractal-dimension-based signal-processing technique has been extensively applied to various fields, but the use of the method to characterize discrete time-domain ultrasonic signals reflecting defects and any other structural-material inhomogeneities has not been fully investigated. The fractal features of the ultrasonic echoes with fractal dimensions and their implementation in nondestructive testing are investigated. In order to obtain a faithful representation of the fractal dimensions, two improved fractal dimension algorithms are presented: the box-counting method and the R/S (range/standard deviation) method. Their capabilities are evaluated with two kinds of fractal signals: the FBM (fractal Brownian motion) and WM (Weierstrass-Mandelbrot) signals. A new method to guarantee the feasibility of the calculated fractal dimensions is proposed on the basis of the analysis of the results simulated above. Then, the fractal dimensions of ultrasonic signals measured from a pipeline sample and from carbon-steel and aluminum specimens are calculated and statistically analyzed to find the fractal properties of the ultrasonic signals. The experimental results show that ultrasonic signals have the property of scale invariance that the fractal set possessed. The fractal dimension is indicative of the complexity and degree of irregularity of the waveform of an ultrasonic signal. The fractal dimensions of ultrasonic signals from various defects and microstructures are found to possess solid distribution intervals, which can be used to identify the presence of defects and the features of materials. The potential of the technique for testing defects and assessing the microstructure of materials via the use of ultrasonic echoes is revealed. The text was submitted by the authors in English.     

基于分形理论的机械故障诊断的应用研究

将分形的有关理论与机械故障诊断联系起来,论述了分形维数的基本概念,介绍了网格维数及其求取方法。通过对模拟信号、齿轮振动信号进行分形诊断表明,分形网格维数诊断可以模糊诊断出到底是哪一部分最有可能发生故障,证实了分形网格维数的普遍性和通用性。分形网格维数诊断方法能有效地诊断齿轮局部故障,具有广泛的应用前景。