Diagnosis of bearing defects using tunable Q-wavelet transform

Defects in rolling element bearings are foremost cause of failure in rotating machines. The accurate and fast diagnosis of bearing defects like spall, dents, pits, cracks etc. on the various component of bearing can be accomplished by analysis of vibration signals using various advanced signal processing techniques. In this work, a new technique for the diagnosis of bearing defects using tunable Q-wavelet transform and fractal based features has been presented. The vibration signals have been recorded experimentally. These signals are decomposed into a number of sub-bands using tunable Q-wavelet transform for effective feature extraction. Classical statistical features and fractal dimension based features such as Higuchi fractal dimensions and Katz fractal dimensions are computed for each decomposed sub-band. These features obtained using tunable Q-wavelet transform of vibration signal are having better capability to classify defects through various machine learning algorithms.     

Analysis of Ultrasound Propagation in a Metal Rod by Using Finite Element Method

A metal rod is used in the high temperature testing for ultrasonic propagation and heat output,but the trailing echoes generated by ultrasonic penetration through the metal rod seriously affect the recognition and extraction of characteristic signals.According to the phenomenon,the Finite Element Method(FEM)is used to analyze ultrasonic penetration through a metal rod,the reason of the trailing echoes and the regularity of ultrasonic signals.The motion equation of ultrasonic propagation in a metal rod is established and calculated,then the simulation signals and instantaneous cartographies of the process are obtained.Based on the results of the analysis,it can be concluded that the intervals of the trailing echoes are determined by the rod’s diameter and wave velocity.In practical applications,the FEM is used to analyze ultrasonic propagation in the designed buffer rod at first.Based on the characteristics of the simulation signals,the material and dimension are adjusted and selected,aiming to identify the characteristic echo and trailing echoes in time domain and extract characteristic echo from the ultrasonic signals available.     

金属材料小缺陷超声反射信号建模及识别

为了能从含噪声金属材料超声检测信号中有效识别出微小缺陷回波,建立了金属材料超声反射信号模型并提出了基于相关系数的微小缺陷回波识别方法。对含微小缺陷金属材料超声脉冲反射信号的成分进行分析,建立了基于散射声场与高斯回波理论的优化超声回波模型。设计了超声缺陷回波位置识别方法。该方法对超声脉冲反射信号去噪后,取探头发射脉冲信号为参考信号;然后与去噪后的信号逐段求解相关系数;最后对该相关系数序列进行阈值化处理,获得缺陷回波在超声回波信号中的位置。将利用上述优化超声回波模型生成的超声反射信号及其频谱与实验获得的金属材料超声反射信号及其频谱进行了对比,结果表明:两者的时频域特征具有一致性。当将阈值设定为相关系数序列最大值的60%时,能够有效从超声背散射信号中识别出金属材料微小缺陷回波。     

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。     

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.     

基于动态光散射信号分形的颗粒测量技术研究

颗粒的动态光散射信号具有分形特征，通过对其特征值的分析，可以得到颗粒粒径信息。采用100nm、200nm、500nm和1000nm4种亚微米粒径范围的标准聚苯乙烯乳胶颗粒样品，分别求取了它们的动态光散射信号的分形维数。实验结果表明，颗粒粒径越小，对应的动态散射光分形维数越大；粒径越大，分形维数越小。从而证明了用分形维数表征颗粒粒径的可行性。与相关方法相比，分形方法具有所需的采样数据少、计算简便、适于实时测量等特点。     

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。     

Noise suppression and flaw detection of ultrasonic signals via empirical mode decomposition

In ultrasonic nondestructive testing, the precise detection of flaw echoes buried in backscattering noise caused by highly scattering materials is a problem of great importance. In this paper, a new signal decomposition method for analyzing nonstationary or nonlinear data, empirical mode decomposition, is proposed to deal with ultrasonic signals. A new denoising technique that combines empirical mode decomposition and filtering simultaneously in the time domain and frequency domain is designed to suppress noise and enhance flaw signals. Synthetic and experimental signals are denoised with this EMD-based filtering technique. Simulated results are presented and analyzed, showing that the proposed technique has an excellent performance even when the signal-to-noise ratio is very low (−23 dB). The improvement in flaw detection was experimentally verified on a pipeline sample with artificial flaws. The text was submitted by the authors in English.     

Ultrasonic Flaw Echoes Detection Based on Generalized S-Transform

Micro defects usually lead to performance degradation of materials. Pulse-echo technique is an effective method of revealing micro defects in materials, and booming in which has been witnessed over the years. However, detection of flaw echoes submerged in intensive noise is challenging. In this paper, a method of flaw echoes detection based on generalized S-transform is proposed. The original signals are transformed to time-frequency domain by generalized S-transform in the first stage. Subsequently, the time-frequency spectrums are enhanced through image segmentation techniques, including global thresholding and dilation. Finally, clear flaw echoes are obtained by inverse generalized S-transform, contributing to further investigation of defects. Both the simulation and experimental results show effectiveness of the flaw echoes detection method.     

非金属夹杂物分形特征研究

材料的宏观力学性能与材料的微观组织密切相关，以显微组织中离散的点状、线状分布的典型夹杂物为研究对象，用计盒维数测量了标准图谱中不同等级的非金属夹杂物图形的分形维数，考察了夹杂物等级与分维之间的关系。结果表明，非金属夹杂物可用分形维数定量描述；随着夹杂物等级的上升，分形维数变大。     

Using fractal dimensions to characterize atomic force microscope images of epoxy resin fracture surfaces

Atomic force microscopy was used to obtain images of the fracture surface of a tri-ethylene-tetramine and 4,4 (methyl thylidene) epoxy resin. Images were obtained in the mirror, mist, and hackle regions of each sample. Fractal dimensions were calculated from the images using the box dimension and contour analysis method. The box dimension fractal dimension increment for all regions on the fracture surfaces were determined to average 0.26 ± 0.06 and the contour analysis fractal dimension increment were determined to average 0.46 ± 0.05. The box dimension technique is shown to provide the “true” fractal dimension of the surface. The fractal dimension measurements for all three regions indicated that the fracture surface was self-affine and possibly self-similar.     

Detection method of spot welding based on multi-information fusion and fractal

A novel detection method of support vector machine （SVM） based on fractal dimension of signals is presented. And models of SVM are made based on nugget size defects of spot welding. Classification using these trained SVM models is done to signals of spot welding. It is shown from effect of different SVM models that these models with different inputs. In detection of defects, these models with inputs including sound signal have a high percentage of accuracy, the detection accuracy of these models with inputs including voltage signal will reduce. So the SVM models based on fractal dimensions of sound are some optimal nondestructive detection ones. At last a comparison between SVM detection model and ANNS detection model is researched which indicates that SVM is a more effective measure than Artificial neural networks in detection of nugget size defects during spot welding.     

Two approaches to the solution of problems of ultrasonic flaw metering: Analysis of a high-quality image of reflectors and correlation analysis of measured echo signals

The problem of ultrasonic flaw metering consists in the obtainment of information on the reflector type, its dimensions, and the coordinates of its location. As the accuracy of determining the parameters of a discontinuity increases, the results of ultrasonic testing become more reliable. A highquality image of reflectors, which is reconstructed using the C-SAFT method with consideration for multiple reflections of pulses from the boundaries of a test object that makes it possible to determine the types and dimensions of reflectors, can be considered as one of the tools of flaw metering. However, the C-SAFT method disregards the features of reflections from discontinuities with nonuniform scattering indicatrices. As a development of flaw-metering methods, it is proposed to determine the parameters of a discontinuity from the results of comparison of the measured echo signals and estimated echo signals, which are calculated as functions of the discontinuity parameters. The results of using the proposed methods of ultrasonic flaw metering in numerical and model experiments, which demonstrate the working capacity of the proposed approach, are presented.     

基于盲反卷积和参数化模型的超声参数估计

在超声检测中,往往需要获得传播时间(TOF)、回波个数、中心频率、幅值等全面的信息,来综合评判缺陷的位置、大小和类型。通过建立多回波的卷积模型和参数化模型,给出一种结合最小熵盲反卷积(MED)和期望值最大(EM)算法思想的超声回波参数估计方法。首先基于卷积模型,采用最小熵反卷积,实现了重叠多回波信号的有效分离;再基于参数化模型和所获取的回波个数,给出了基于期望值最大算法思想的参数估计算法;最终实现了重叠多回波超声信号TOF、回波个数、中心频率、幅值等参数的精确估计。仿真和实验验证了该方法的有效性和优点。     

分形特征在湿式离合器磨合状态预测中的运用

针对湿式离合器试验过程中磨合状态的预测问题,提出利用摩擦转矩信号分形特征来预测和评价磨合状态的新方法.首先,分别阐述了摩擦转矩信号盒子维数和关联维数的计算方法;然后,在湿式离合器试验台上提取了磨合试验的摩擦转矩信号,并对信号进行了自相似性分析;最后,应用盒子维数和关联维数分别对摩擦转矩信号进行了分形研究.分析结果表明:盒子维数和关联维数都能定量描述试验过程中摩擦转矩信号的分形特征,随着磨合次数的增加,摩擦转矩信号的盒子维数和关联维数的波动范围都逐渐减小,并且最终收敛于较小的波动范围.因此,利用试验中摩擦转矩信号的分形特征能够在不开箱条件下定量地预测和评价湿式离合器的磨合状态.     

广义分形维数在旋转机械故障诊断中的应用研究

运用分形理论，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，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，提出谱能量与分形维数的关系，为用分形维数来分析故障的强度提供了依据。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，并通过实例诊断、识别故障及其故障程度，说明该方法具有较好的实效性。     

不同材料的准分子激光修饰表面形貌的分形表征

采用准分子激光加工方法,在相同的工艺参数下加工了脆性材料（A1203陶瓷）和塑性材料（不锈钢、Q235钢）试件.采用分形理论研究了准分子激光加工的不同材料表面的分形特征,通过结构函数法计算了表面微观形貌的分形维数.结果表明：准分子激光加工的不同材料的表面形貌具有不同的分形特性,在相同的加工条件下A1203陶瓷材料表面的分形维数大于不锈钢、Q235钢等塑性材料表面的分形维数,表明脆性材料和塑性材料具有不同的准分子激光加工属性;准分子激光加工表面的分形维数随着激光扫描速度的增大而减小,随放大器电压和脉冲频率的增大而增大,表明可以通过改变加工参数得到不同的分形维数表面.     

Internal defect detection using laser-generated longitudinal waves in ablation regime

Laser ultrasonics could be an attractive solution for the nondestructive testing of structures in harsh environments. Longitudinal waves generated in the ablation regime are especially well suited to internal defect detection because they provide a higher signal-to-noise ratio in comparison to ultrasonic waves generated under a thermo-elastic regime, while their propagation direction normal to the surface enables the simplified interpretation processing of received signals when the defect echoes are analyzed. The internal defect detection using laser-generated longitudinal waves in the ablation regime was investigated numerically and experimentally, and a numerical model to simulate the generation and propagation of ultrasonic waves in the ablation regime was developed. This model was based on the simulation of ultrasonic generation and propagation caused by the net reaction force that was directly converted from the laser intensity absorbed onto the surface. This model was also extended as a model for internal defect detection. A steel specimen containing artificial internal defects was fabricated and inspected by using the through-transmission (T-scan) and pulse-echo (B-scan) modes. Clear amplitude reduction was observed in the transmitted waves at the defect positions in T-scan images, while B-scan images clearly showed the defect echoes arriving at different times depending on the depth location of internal defects. These results demonstrate that longitudinal waves excited in the ablation regime can be effectively used for internal defect detection.     

摩擦学系统的混沌特性

为了充分认识摩擦学系统的复杂性质,对摩擦学行为的混沌性进行了定量研究。在销—盘试验机上试验提取摩擦力和摩擦振动的时序信号,以此为研究对象,运用功率谱、分形维数和Lyapunov指数三种方法联合证明了这两种摩擦信号的混沌特性。研究表明,摩擦力和摩擦振动信号具有连续宽带的功率谱、分数维数和正的Lyapunov指数,即具有混沌行为的基本特征,从而证明了摩擦学系统的混沌性质。     

基于机械故障诊断的高阶谱耦合性能分析

为了有效进行故障诊断,利用容量维数作为工具,计算了实数信号三谱对角切片、复数信号两种不同定义形式的212维谱的容量维数。212维谱具有16种不同的定义形式,按照定义形式的不同,其中的两种分别包含参加耦合的信号信息和耦合后的信号信息。这种不同的耦合性质使得它们的形状体现出不同的自相似性。利用采集的减压阀振动的正常信号和故障信号,首先得出不同的212谱,然后进行容量维的计算,并根据计算结果进行故障诊断,得出了所研究的两种定义形式的212维谱其自相似性与实数信号三谱对角切片相比,在故障诊断中更为有效的结论。