Fault detection and location in gears by the smoothed instantaneous power spectrum distribution

Time–frequency methods are effective tools for analysing diagnostics signals and have been widely used to describe machine condition. This paper introduces a time–frequency distribution, called the smoothed instantaneous power spectrum (SIPS) distribution, and demonstrates its use in the detection and location of local tooth defects in gears. The SIPS distribution is derived from the frequency domain definition of the instantaneous power spectrum (IPS) distribution, but has the added advantage that provides a considerable reduction in the ringing effect of the IPS transform, which results in a smoother and clearer time–frequency representation. A simulated gear vibration signal is used to show the capabilities of the proposed method over the IPS distribution and spectrogram. Healthy and faulty vibration signals monitored from a gear test rig are analysed, the results of which show that a local gear tooth defect can clearly be detected by the SIPS distribution.     

Time–energy density analysis based on wavelet transform

Energy is an important physical variable in signal analysis. The distribution of energy with the change of time and frequency can show the characteristics of a signal. A time–energy density analysis approach based on wavelet transform is proposed in this paper. This method can analyze the energy distribution of signal with the change of time in different frequency bands. Simulation and practical application of the proposed method to roller bearing with faults show that the time–energy density analysis approach can extract the fault characteristics from vibration signal efficiently.     

Processing of transient signals from damage in CFRP composite materials monitored with embedded intensity-modulated fiber optic sensors

In this research study, intensity-modulated fiber optic sensors, whose working principle is based on the microbending concept, are used to monitor the damage in C/epoxy laminates during tensile loading. The use of advanced signal processing techniques based on time–frequency analysis is explained in order to get information on the damage developing in the composite. The signal Short Time Fourier Transform (STFT) has been computed and several robust noise reduction algorithms have been applied. Principally, Wiener adaptive filtering, improved spectral subtraction filtering, minimum-phase FIR (Finite Impulse Response) filtering and Singular Value Decomposition (SVD)-based filtering have been used. An energy and frequency-based detection criterion is introduced to detect transient signals that can be correlated with the Modal Acoustic Emission (MAE) results and thus damage in the composite material. Hints are that time–frequency analysis and Hankel Total Least Square (HTLS) method can also be used for damage characterisation (delamination, matrix cracking and fiber breaking).     

Forced vibration behaviour and crack detection of cracked beams using instantaneous frequency

In this paper, a new method for crack detection in beams based on instantaneous frequency and empirical mode decomposition is proposed. The dynamic behaviour of a cantilever beam with a breathing crack under harmonic excitation is investigated both theoretically and experimentally. A simple single-degree-of-freedom system with varying stiffness is employed to simulate the dynamic behaviour of the beam. The time-varying stiffness is modelled using a simple periodic function. Both simulated and experimental response data are analysed by applying empirical mode decomposition and Hilbert transform and the instantaneous frequency of each oscillatory mode is obtained. It is shown that the instantaneous frequency oscillates between frequencies corresponding to the open and closed states revealing the breathing of the crack. The variation of the instantaneous frequency increases with increasing crack depth following a polynomial law and consequently can be used for estimation of crack size. Using the intrinsic modes of the system, the harmonic distortion of the distorted sinusoidal response is calculated. It follows that the harmonic distortion increases with crack depth following definite trends and can be also used as an effective indicator for crack size. The proposed time–frequency approach is superior compared to Fourier analysis and can be used to improve the effectiveness of vibration-based crack detection techniques.     

Development of an advanced noise reduction method for vibration analysis based on singular value decomposition

The paper developed a reasonable and practical method for identifying the useful information from the signal that has been contaminated by noise, so that to provide a feasible tool for vibration analysis. A new concept namely the Singular Entropy (SE) was proposed based on the singular value decomposition technique. With the aid of the SE, a series of investigations were done for discovering the distribution characteristics of noise contaminated and pure signals, and consequently an advanced noise reduction method was developed. The experiments showed that the proposed method was not only applied for dealing with the stationary signals but also applied for dealing with the non-stationary signals.     

Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition

Due to the importance of rolling bearings as the most widely used machine elements, it is necessary to establish a suitable condition monitoring procedure to prevent malfunctions and breakages during operation. This paper presents a new method for detecting localized bearing defects based on wavelet transform. Bearing race faults have been detected by using discrete wavelet transform (DWT). Vibration signals from ball bearings having single and multiple point defects on inner race, outer race, ball fault and combination of these faults have been considered for analysis. Wavelet transform provides a variable resolution time–frequency distribution from which periodic structural ringing due to repetitive force impulses, generated upon the passing of each rolling element over the defect, are detected. It is found that the impulses appear periodically with a time period corresponding to characteristic defect frequencies. In this study, the diagnoses of ball bearing race faults have been investigated using wavelet transform. These results are compared with feature extraction data and results from spectrum analysis. It has been clearly shown that DWT can be used as an effective tool for detecting single and multiple faults in ball bearings. This paper also presents a new method of pattern recognition for bearing fault monitoring using hidden Markov Models (HMMs). Experimental results show that successful bearing fault detection rates as high as 99% can be achieved by this approach.     

Rolling element bearing fault diagnosis using wavelet packets

A method is proposed for the analysis of vibration signals resulting from bearings with localized defects using the wavelet packet transform (WPT) as a systematic tool. A time–frequency decomposition of vibration signals is provided and the components carrying the important diagnostic information are selected for further processing. The proposed method is designed in such a way that it can exploit the underlying physical concepts of the modulation mechanism, present in the vibration response of faulty bearings. The flexibility of the WPT and the systematic parameter selection criteria, help in the minimization of interventions by the end user. The method is evaluated using simulated and experimental signals.     

Determination of the group and phase velocities from time–frequency representation of Wigner–Ville

The experimental measurement of the group and phase velocities of some circumferential waves propagating around a thin elastic tube is a still complex operation. In this study, we show that the dispersion velocity can be determined from a time–frequency representation. We use the Wigner–Ville method by virtue of its interesting properties. On some time–frequency images, the symmetric (S0) and antisymmetric (A1) circumferential waves are identified. The group velocity dispersion estimated from these images is compared with that computed by the proper mode theory method. A good agreement is obtained. The phase velocity is also determined from the group velocity.     

Ultrasonic NDE of composite material structures using wavelet coefficients

A wavelet-based method is proposed to perform the analysis of NDE ultrasonic signals received during the inspection of reinforced composite materials. The non-homogenous nature of such materials induces a very high level of structural noise which greatly complicates the interpretation of the NDE signals. By combining the time domain and the classical Fourier analysis, the wavelet transform provides simultaneously spectral representation and temporal order of the signal decomposition components. To construct a C-scan image from the wavelet transform of the A-scan signals, we propose a selection process of the wavelet coefficients, followed by an interpretation procedure based on a windowing process in the time–frequency domain. The proposed NDE method is tested on cryogenic glass/epoxy hydrogen reservoir samples.     

The application of the conditional moments analysis to gearbox fault detection—a comparative study using the spectrogram and scalogram

Time–frequency methods, which can lead to the clear identification of the nature of faults, are widely used to describe machine condition. Capabilities of time–frequency distributions in the detection of any abnormality can further be improved when their low-order frequency moments (or time-dependent parameters), which characterise dynamic behaviour of the observed signal with few parameters, are considered. This paper presents the applications of four time-dependent parameters (e.g. the instantaneous energy, mean and median frequencies, and bandwidth) based upon the use of spectrogram and scalogram, and compares their abilities in the detection and diagnosis of localised and wear gear failures. It has been found that scalogram based parameters are superior to those of a spectrogram in the detection and location of a local tooth defect even when the gear load is small, as they result in equally useful parameters in the revelation of gear wear. Moreover, the global values of these time-dependent parameters are found to be very useful and provide a very good basis for reflecting not only the presence of gear damage, but also any change in operating gear load.     

On-line detection of the breakage of small diameter drills using current signature wavelet transform

This paper presents on-line tool breakage detection of small diameter drills by monitoring the AC servo motor current. The continuous wavelet transform was used to decompose the spindle AC servo motor current signal and the discrete wavelet transform was used to decompose the feed AC servo motor current signal in time–frequency domain. The tool breakage features were extracted from the decomposed signals. Experimental results show that the proposed monitoring system possessed an excellent on-line capability; in addition, it had a low sensitivity to change of the cutting conditions and high success rate for the detection of the breakage of small diameter drills.     

Transient dynamical analysis of strain signals in sheet metal stamping processes

On-line monitoring of stamping processes can be carried out based on various sensors, such as force, strain, acceleration, proximity, and acoustic emission sensors. The strain sensor signal is the most favourite because of its effectiveness and acquisition cost as well as it contains rich information about the stamping process. The key problem of stamping monitoring is how to extract features from the strain signal to effectively detect the faults. The strain signal, however, is a transient signal that depends on many factors. In this paper, it is intended to address some new methods to analyse the transient strain signal with the objective of decomposing it in order to understand the dynamics of the stamping process and extract a malfunction signal for fault detection. A latent process model method, which is a combination of a time-varying auto-regression model and a dynamic linear model, is initially presented. Continuous wavelet transforms and a new discrete wavelet transform (maximum overlap discrete wavelet transform) are then addressed to project the transient signal into a time scale plan to represent the dynamical behaviour in a different way. Empirical mode decomposition is finally employed to decompose the transient signal into a finite and often small number of intrinsic mode functions (IMF). The advantage of this new method is that it is adaptive and highly efficient. The performance of the methods employed in this paper is reviewed using two real strain signals in a sheet metal stamping process. It is found that these methods can efficiently provide the energy–frequency–time distribution of the transient strain signal.     

Optimization of antenna configuration in multiple-frequency ground penetrating radar system for railroad substructure assessment

This paper discusses the use of ground penetrating radar (GPR) to rapidly, effectively, and continuously assess railroad track substructure conditions, especially ballast. To overcome the limited electromagnetic waves penetration for high-frequency antennae and the low resolution of low-frequency antennae, this study uses a multiple-frequency GPR system to assess railroad substructure conditions. High-frequency antennae were used to detect the scattering pattern, which is related to air void volume in railroad ballast, and low-frequency antennae are used to assess deeper substructure conditions. Considering the scattering energy attenuation is highly frequency and material dependent, a time–frequency method based on tracking the frequency spectrum and energy change over depth can be used to extract ballast fouling conditions. From GPR field collected data, ground-truth observation, and ballast gradation analysis, the multiple-frequency GPR system demonstrates a promising capability to assess railroad track substructure condition.     

Ultrasonic non-destructive evaluation with spatial combination of Wigner–Ville Transforms

This paper introduces a novel ultrasonic signal combination technique to be applied in detection systems based on multiple transducers. The technique uses a spatial combination approach that considers the specimen inspection from several apertures located in different planes. Information received from transducers is fused in a common integrated pattern with a signal to noise ratio (SNR) improvement. The result of the combination is a high quality image of the inspected material obtained from simple A-scans.The method is based on digital signal processing techniques, more concretely time–frequency analysis. Combination is performed by means of the Wigner–Ville Transform preserving temporal and frequencial information. Temporal techniques for combination are presented and the results obtained from both techniques are compared using the SNR.     

Feature extraction of machine sound using wavelet and its application in fault diagnosis

Machine sound always carries information about the working of the machine. But in many cases, the sound has a very low SNR. To obtain correct information, the background noise has to be removed or the sound must be purified. A de-noising method is given in this paper and is successfully used in feature sound extraction. We can easily diagnose a machine using the purified sound. This de-noising method is based on the wavelet technique and uses the Morlet wavelet as the mother wavelet, because its time–frequency resolution can be adjusted to adapt to the signal to be analyzed. The method is used for extracting the sound of some vehicle engines with different types of failure. The feature sound is extracted successfully.     

Determination of the cutoff frequency of an acoustic circumferential wave using a time–frequency analysis

In this article, time–frequency representation of Wigner–Ville is used to analyse the acoustic signal backscattered by a thin elastic tube of radii ratio b/a. This analysis allows to determine the reduced cutoff frequency of the circumferential antisymmetric wave A1 propagating around the tube. The evolution of this reduced cutoff frequency in function of b/a is reported. The values obtained of reduced cutoff frequency are compared to the values computed from the proper modes method.     

Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion

In a fully automated manufacturing environment, instant detection of the cutting tool condition is essential for the improved productivity and cost effectiveness. This paper studies a tool condition monitoring system (TCM) via machine learning (ML) and machine ensemble (ME) approach to investigate the effectiveness of multisensor fusion technique when machining 4340 steel with multilayer coated and multiflute carbide end mill cutter. In this study, 135 different features are extracted from multiple sensor signals of force, vibration, acoustic emission and spindle power in the time and frequency domain by using data acquisition and signal processing module. Then, a correlation-based feature selection technique (CFS) evaluates the significance of these features along with machining parameters collected from machining experiments. Next, an optimal feature subset is computed for various assorted combinations of sensors. Finally, machine ensemble methods based on majority voting and stacked generalization are studied for the selected features to classify not only flank wear but also breakage and chipping. It has been found in this paper that the stacked generalization ensemble can ensure the highest accuracy in tool condition monitoring. In addition, it has been shown that the support vector machine (SVM) outperforms other ML algorithms in most cases tested.     

基于EEMD多维特征的旋转机械故障识别方法研究

为有效诊断旋转机械故障,提出基于集合经验模态分解（EEMD）的多维特征提取故障诊断识别方法。利用EEMD将原始振动信号分解为若干个本征模态函数（IMF）,分别计算原始信号和IMF分量的时域指标;将时域指标进行奇异值分解,得到奇异值特征向量,计算原始信号频率带能量比和IMF分量能量比;将IMF分量能量比、奇异值特征向量、频率带能量比组合为故障特征向量,作为神经网络的输入,对转子的工作状态进行诊断识别。结果表明：多维特征向量的识别效果优于EEMD能量特征,能更充分反映出转子的故障特征。     

基于改进CEEMDAN和t-SNE的故障特征提取方法

针对非线性、非稳定振动信号难以提取有效故障特征的问题,提出一种基于改进自适应噪声完备集合经验模态分解（CEEMDAN）和t-分布随机邻域嵌入（t-SNE）算法相结合的故障特征提取方法。利用三次Hermite插值代替三次样条插值构造包络线,提高传统CEEMDAN对非平稳信号的分解精度;利用改进后的CEEMDAN对原始信号分解并通过相关系数筛选出有效固有模态分量（IMF）,提取有效IMF分量的时频特征、奇异值和能量值构建高维混合域特征集;最后,通过t-SNE算法挖掘高维混合域特征信息得到低维敏感特征,并将其输入到支持向量机中进行分类,以分类准确率作为特征提取效果评价指标。在齿轮箱故障模拟实验台进行实验验证,结果表明该方法能够准确地提取故障特征,为故障特征提取提供新思路。     

VMD复合熵值法在齿轮故障诊断中的应用

针对齿轮实际工况复杂、故障特征难以提取的问题,提出一种基于变分模态分解(Variational Mode Decomposition,VMD)复合熵值法的故障诊断方法.首先,采用VMD方法对不同工况下齿轮振动信号进行分解,并对分解过程中关键参数的选择进行了研究;其次,根据频域互相关系数准则筛选出可有效表征齿轮状态特征的...