A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings

A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings is presented in this paper. Detection of both localized and distributed categories of defect has been considered. An explanation for the vibration and noise generation in bearings is given. Vibration measurement in both time and frequency domains along with signal processing techniques such as the high-frequency resonance technique have been covered. Other acoustic measurement techniques such as sound pressure, sound intensity and acoustic emission have been reviewed. Recent trends in research on the detection of defects in bearings, such as the wavelet transform method and automated data processing, have also been included.     

基于EMD和Hilbert解调的齿轮箱故障诊断

针对复杂的齿轮箱振动信号难以提取出故障特征频率的问题,提出了一种将希尔伯特包络解调技术与经验模式分解(EMD)相结合的分析方法。首先对齿轮箱的故障信号进行了EMD分解,得到了本征模态函数(IMF分量),再对IMF分量进行了包络解调,得到了其调制信号,结合调制信号的频率成分可初步判断出齿轮箱中出现故障的齿轮;然后根据IMF分量与初始信号之间相关系数的大小,选择相关系数较大的分量重构信号,相当于对初始信号进行滤波;最后对重构的信号以啮合频率及其倍频为中心频率进行了带通滤波,对得到的信号进行了包络解调分析,再次进行了故障诊断,以验证故障诊断的准确性。整个过程通过对齿轮箱实测故障信号的分析加以验证。研究结果表明,该方法能够准确地提取出齿轮箱的故障特征频率,从而可以对齿轮箱故障进行有效地诊断。     

基于EMD与倒谱分析的轴承故障诊断

提出了一种基于经验模态分解与幅值倒频谱分析的轴承故障诊断方法。该方法首先对外圈故障信号作传统的傅里叶幅值谱和幅值倒频谱分析,未能明显地找到故障特征;然后对故障信号做经验模态分解,并对分解出来的第一层本征模函数作倒频谱分析,有效地提取出了故障特征;最后,用该方法分别对具有内圈故障和滚动体故障的轴承故障信号作分析,也有效地提取出了故障特征。实验结果表明,通过联合经验模态分解和倒频谱分析,能有效并且准确地提取出轴承的故障特征频率。     

Application of computed order tracking, Vold-Kalman filtering and EMD in rotating machine vibration

This paper presents a study on rotating machine vibration signals by using computed order tracking, Vold-Kalman filtering and intrinsic mode functions from the empirical mode decomposition method. Through the sequential use of intrinsic mode function and order tracking methods, both speed synchronous and non-synchronous vibrations that modulate orders in rotating machine vibrations are distinguished, which is difficult when using each of the techniques in isolation alone. Simulation and experimental studies demonstrate the ability of extracting vibrations that modulate order signals through combining the techniques.     

基于谱峭度和CEEMD的滚动轴承声信号故障诊断研究

使用声信号来诊断轴承故障越来越受到重视.针对滚动轴承故障信号的强背景噪声特点,提出一种基于谱峭度和互补集合经验模态分解(CEEMD)的故障特征提取方法.该方法首先对滚动轴承声信号进行快速谱峭度计算并进行带通滤波预处理,使滚动轴承声信号变得简单且噪声小,故障冲击成分明显;然后利用CEEMD将滤波信号进行分解运算,得到一系...     

关联维数和基于内禀模态函数的AR模型在滚动轴承故障诊断中的应用

提出一种基于内禀模态函数(Intrinsic Mode Function,IMF)、自回归(Auto-Regressive,AR)模型和关联维数的滚动轴承故障诊断方法.该方法首先采用经验模态分解(Empirical Mode Decomposition,EMD)将滚动轴承振动信号分解成若干个IMF,然后对包含主要故障信息的IMF分量建立AR模型,计算AR模型自回归参数的关联维数,并以关联维数作为特征向量输入神经网络分类器,最后通过网络的输出结果来识别轴承的工作状态和故障类型.对实验数据的分析结果表明,该方法能有效地应用于滚动轴承的故障诊断.     

机械转子动静碰摩故障的EMD分解Hilbert包络谱分析

为避免碰摩故障对旋转机械的影响,针对转子系统局部碰摩的特征,提出一种基于EMD分解Hilbert包络谱分析方法。该珐利用EMD方法分解含有碰摩故障的振动信号,提取出的IMF分量有明显的调幅特征,再对其中突出的IMF分量进行Hilbert包络谱分析提取出故障特征频率。与倒谱分析相比,得到的碰摩故障信息更加精确;与小波分析相比,能更容易提取出真实的故障特征。     

基于中值滤波-SVD和EMD的声发射信号特征提取

针对随机噪声和脉冲干扰对经验模态分解(EMD)质量的影响,提出中值滤波和奇异值分解(SVD)联合降噪方法,并将其与EMD分解相结合形成一种新的声发射(AE)信号特征提取方法.首先对原始AE信号进行中值滤波,去除幅值较大的异常值;其次对去除异常值的信号序列进行相空间重构和SVD分解,并针对难以确定重构阶数这一问题,提出奇异值能量差分谱概念,利用谱峰的较大值位置来确定重构阶数,以进一步降噪;最后对降噪信号进行EMD分解,以本征模态函数(IMF)的能量占比作为表征各损伤信号的特征向量.数值仿真和5层胶合板损伤的实测数据表明,该方法不仅能够滤除噪声干扰,提高EMD分解的时效性和准确性,而且能够有效地提取出胶合板AE信号特征,对其损伤类型进行有效地识别.     

Model-based fault detection and isolation in steer-by-wire vehicle using sliding mode observer

Steer-by-Wire system (SbW), in which the conventional mechanical linkages between the steering wheel and the front wheel are removed, is suited to active steering control, improving vehicle stability, dynamics and maneuverability. And SbW is implemented to autonomous steering control to assist the driver. However, the SbW vehicle contains unsolved important problems about fault tolerant function. For example, it is the detection of sensor fault and multiplicative fault simultaneously. Fault detection and isolation (FDI) is essential in fault-tolerant problems, and conventional FDI for SbW was based on Kalman filter. But this method has weak robustness and cannot detect sensor fault and multiplicative fault simultaneously. We propose a novel model-based fault detection and isolation method using sliding mode observer in the SbW vehicle, which contains measurement of sensor fault and multiplicative fault. The effectiveness of the proposed method is verified by simulations. This paper was recommended for publication in revised form by Associate Editor Kyoungsu Yi Jae-Sung Im was born in Busan, Korea in 1978. He received his B.S. and M.S. degrees in Mechanical Engineering from Pukyong National University, Korea, in 2003 and 2005, respectively. He then received his Ph.D. degree from Kumamoto University, Japan, in 2009. His interests are in vehicle dynamics, robust control, fault detection and isolation, and man-machine interface. Fuminori Ozaki received the B.S. and M.S. degrees from the Department of Computer Science, Kumamoto University, Japan, in 1998 and 2000. In 2000, he joined OMRON Corporation, Kyoto, Japan, where he developed semiconductor manufacturing equipment. His current interests include EPS control and KANSEI engineering. Tae-Kyeong Yue received the B.S. and M.S. degrees from Pukyong National University, Korea, in 1998 and 2000, respectively. He received the Ph.D. degree from Kumamoto University, Kumamoto, Japan in 2003. He is working in the Korea Ocean Research and Development Institute (KORDI), Korea. His interests are fault detection and isolation, decentralized control and control of deep-sea mining system. Shigeyasu Kawaji received his Master of Engineering in Electrical Engineering and Doctor of Engineering in Control Engineering from Kumamoto University and Tokyo Institute of Technology, Japan, in 1969 and 1980, respectively. He joined the Department of Electronic Engineering of Kumamoto University, Japan, where he is presently as a full professor. He is the Director of System Integration Laboratory. He is presently the President of Advanced Health Laboratory Ltd. His current research interest includes robust control, intelligent control mechatronics and robotics, fusion of medicine and engineering, and automotive mechatronic systems.     

A novel measurement method for transient detection based in wavelets entropy and the spectral kurtosis: An application to vibrations and acoustic emission signals from termite activity

This paper presents a novel non-destructive method for termite detection that uses the entropy of the continuous wavelet transform of the acoustic emission signals as an uncertainty measurement, to achieve selective frequency separation in complex impulsive-like noisy scenarios, with the aid of the spectral kurtosis as a validating tool. The goal consists of detecting relevant frequencies, by looking up the minima in the curve associated to the entropy of the difference between the raw data and the wavelet-based reconstructed version. By measuring the signal’s uncertainty, the scales corresponding to the entropy minima, or pseudo-frequencies, manage to target three main types of emissions generated by termites: the modulating components (enveloping curve), the carrier signals (activity, feeding and excavating), and the communicating impulses bursts (alarms). The spectral kurtosis corroborates the location of the entropy minima (optimum uncertainty) matching them to its maxima, associated to frequencies with the highest amplitude variability, and consequently minimizing the measurement uncertainty. The method is primarily conceived to cover the acoustic-range, in order to acquire signals via standard sound cards; a broaden high-frequency study is developed for the assessment, and with the added value of discovering new and higher frequency components of the species emissions. The potential of the method makes it useful for myriads of applications in the frame of nondestructive transient detection.     

基于高速以太网的基金会现场总线功能块应用进程模态分析

介绍了基金会现场总线中高速以太网(HSE)功能块的参数状态和工作模式的基本结构,总结了参数状态传播和工作模式切换的基本规则,并且以液体温度控制系统的两种典型的控制方案为例,分析了其中功能块的工作模式切换和参数状态传递的具体方法。     

A study on the fault diagnosis of roller-shape using frequency analysis of tension signals and artificial neural networks based approach in a web transport system

Rollers in the continuous process systems are ones of key components that determine the quality of web products. The condition of rollers (e.g. eccentricity, runout) should be consistently monitored in order to maintain the process conditions (e.g. tension, edge position) within a required specification. In this paper, a new diagnosis algorithm is suggested to detect the defective rollers based on the frequency analysis of web tension signals. The kernel of this technique is to use the characteristic features (RMS, Peak value, Power spectral density) of tension signals which allow the identification of the faulty rollers and the diagnosis of the degree of fault in the rollers. The characteristic features could be used to train an artificial neural network which could classify roller conditions into three groups (normal, warning, and faulty conditions). The simulation and experimental results showed that the suggested diagnosis algorithm can be successfully used to identify the defective rollers as well as to diagnose the degree of the defect of those rollers.     

Defect detection in deep groove ball bearing in presence of external vibration using envelope analysis and Duffing oscillator

This paper deals with detection of local defects existing on races of deep groove ball bearing in the presence of external vibrations using envelope analysis and Duffing oscillator. Experiments have been carried out using a test rig for capturing the vibration signals of test bearing. The external vibration has been imparted to the housing of the test bearing through electromechanical shaker. In envelope analysis the centre frequency has been selected using the spectral kurtosis for the filters length of 32 and 64 for different bandwidths. Through this study, it has been revisited and confirmed that the defect detection in envelope analysis mainly depends on the selection of centre frequency and bandwidth. The spectra of selected centre frequency with several bandwidths have been studied and compared for identification of defective frequency. The system defined by the Duffing equation entered into the periodical state from the chaotic state at the critical value of disturbing periodic force in the presence of defective bearing signal. The state change has been identified using the phase plane trajectories and Lyapunov exponents of Duffing equation. It is worth to mention here that envelope spectrum reveals the information about the defect frequencies and their harmonics. However, the Duffing oscillator only confirms the presence of defect frequencies by indicating closed phase plane trajectories and negative Lyapunov exponents. Authors believe that for speedy assessment about the presence of defects on races of rolling element bearings, the use of Duffing oscillator may be preferred.     

A revised Hilbert–Huang transformation based on the neural networks and its application in vibration signal analysis of a deployable structure

A revised Hilbert–Huang method is proposed to deal with the non-linear and non-stationary signals generated from any kinds of the sensors, in order to overcome shortcomings of the Hilbert–Huang method, such as the end swings problem and the undesired intrinsic mode functions (IMFs) at the low-frequency range. Firstly, a radial basis function neural network is used as a pre-processor to extend the length of the signal at the both ends. Secondly, the empirical mode decomposition is applied to obtain IMFs. Thirdly, the selection process is employed to select the optimal IMFs. Finally, an energy–frequency–time distribution can be gained after the Hilbert transformation. Two simulated signals are analyzed to explain the pre- and the post-processor, respectively, by using the above two techniques. The efficiencies of the different bases are compared, and the length of signal extended is analyzed. The correlation coefficients between the analyzed signal and the IMFs are introduced to eliminate the undesired IMFs. In this paper, the revised HHT method has been applied to analyze vibration signals of a deployable structure. A simulated solar array setup is built, which contains six parts: the basal body, a locked mechanism, the synchronism mechanism, the connection joints, the driven parts, and two simulated panels. Vibration signals of the solar array setup in the deployed case that is knocked by a single impulse on the middle of the second panel are estimated, and the results show that the revised Hilbert–Huang method is efficient for non-linear and non-stationary signal analysis.