An improved multiscale noise tuning of stochastic resonance for identifying multiple transient faults in rolling element bearings

Stochastic resonance (SR), a noise-assisted tool, has been proved to be very powerful in weak signal detection. The multiscale noise tuning SR (MSTSR), which breaks the restriction of the requirement of small parameters and white noise in classical SR, has been applied to identify the characteristic frequency of a bearing. However, the multiscale noise tuning (MST), which is originally based on discrete wavelet transform (DWT), limits the signal-to-noise ratio (SNR) improvement of SR and the performance in identifying multiple bearing faults. In this paper, the wavelet packet transform (WPT) is developed and incorporated into the MSTSR method to overcome its shortcomings and to further enhance its capability in multiple faults detection of bearings. The WPT-based MST can achieve a finer tuning of multiscale noise and aims at detecting multiple target frequencies separately. By introducing WPT into the MST of SR, this paper proposes an improved SR method particularly suited for the identification of multiple transient faults in rolling element bearings. Simulated and practical bearing signals carrying multiple characteristic frequencies are employed to validate the performance improvement of the proposed method as compared to the original DWT-based MSTSR method. The results confirm the good capability of the proposed method in multi-fault diagnosis of rolling element bearings.     

Roller bearing acoustic signature extraction by wavelet packet transform,applications in fault detection and size estimation

Continuous online monitoring of rotating machines is necessary to assess real-time health conditions so as to enable early detection of operation problems and thus reduce the possibility of downtime. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, fault diagnostics, defect size calculation, and prognostics. In this paper the effect of defect size, operating speed, and loading conditions on statistical parameters of acoustic emission (AE) signals, using design of experiment method (DOE), have been investigated to select the most sensitive parameters for diagnosing incipient faults and defect growth on rolling element bearings. A modified and effective signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals masked by background noise under different operating conditions. To experimentally measure the defect size on rolling element bearings using acoustic emission technique, the proposed method along with spectrum of squared Hilbert transform are performed under different rotating speeds, loading conditions, and defect sizes to measure the time difference between the double AE impulses. Measurement results show the power of the proposed method for experimentally measuring size of different fault shapes using acoustic emission signals.     

IDENTIFICATION OF MULTIPLE FAULTS IN ROTOR SYSTEMS

Many papers are available in the literature about identification of faults in rotor systems. However, they generally deal only with a single fault, usually an unbalance. Instead, in real machines, the case of multiple faults is quite common: the simultaneous presence of a bow (due to several different causes) and an unbalance or a coupling misalignment occurs often in rotor systems. In this paper, a model-based identification method for multiple faults is presented. The method requires the definition of the models of the elements that compose the system, i.e., the rotor, the bearings and the foundation, as well as the models of the faults, which can be represented by harmonic components of equivalent force or moment systems. The identification of multiple faults is made by a least-squares fitting approach in the frequency domain, by means of the minimization of a multi-dimensional residual between the vibrations in some measuring planes on the machine and the calculated vibrations due to the acting faults. Some numerical applications are reported for two simultaneous faults and some experimental results obtained on a test-rig are used to validate the identification procedure. The accuracy and limits of the proposed procedure have been evaluated.     

Integrated modulation intensity distribution as a practical tool for condition monitoring

Modulations present in vibration signals generated by rotating machinery might carry a lot of useful information about objects’ technical condition. It has been proven that both gearboxes and rolling element bearing (REB) faults manifest themselves as modulations. The paper describes a technique for detection of modulations in vibroacoustic signals, called modulation intensity distribution (MID), which is a function that combines multiple spectral correlation densities in one way or another, depending on the application. Additionally, the paper describes a functional obtained by integrating an MID (denoted by IMID) that has the advantage of being a function of only one frequency variable instead of two. The paper investigates the utility of the MID as an indicator for detection of the presence of rolling element bearing faults in high noise environments. For the purpose of testing, a wind turbine that suffered both advanced gearbox fault and early stage of bearing fault was chosen. Additionally, the paper undertakes the problem of application of the proposed tool in an industrial condition-monitoring system. In order to show the behavior of cyclic components generated by the turbine under study over a long period of time, the set of MIDs integrated over full range of potential carrier signals was presented as a cascade plot.     

基于粒子滤波状态估计的滚动轴承故障识别方法

提出了一种基于粒子滤波状态估计的滚动轴承故障识别方法,该方法主要包括故障模型建立和故障识别两个步骤。在故障模型建立部分,首先依据滚动轴承不同故障状态下的振动信号,建立对应的自回归模型,作为故障模型;在故障识别部分,将正常状态下对应的模型,转化为状态空间模型,设计粒子滤波器,然后对不同的故障状态进行估计,提取其残差的相关特征,并结合模型参数特征应用BP神经网络识别算法进行故障识别。最后以美国凯斯西储大学的滚动轴承振动数据为例,验证了该方法的有效性。     

Feature extraction for rolling element bearing fault diagnosis utilizing generalized S transform and two-dimensional non-negative matrix factorization

This paper presents a novel feature extraction scheme for roller bearing fault diagnosis utilizing generalized S transform and two-dimensional non-negative matrix factorization (2DNMF). The generalized S transform, which can make up the poor energy concentration of the standard S transform, is introduced to generate the time-frequency representation (TFR). Experiment results on simulated signal and vibration signals measured from rolling element bearings have revealed that the generalized S transform can obtain a more satisfactory TFR than other similar techniques. Furthermore, a new technique called two-dimensional non-negative matrix factorization (2DNMF), which can reduce the computation cost and preserve more structure information hiding in original 2D matrices compared to the NMF, is developed to extract more informative features from the time-frequency matrixes for accurate fault classification. Experimental results on bearing faults classification have demonstrated that the proposed feature extraction scheme has an advantage over other similar feature extraction approaches.     

Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal

Rolling bearing faults are one of the major reasons for breakdown of industrial machinery and bearing diagnosing is one of the most important topics in machine condition monitoring.The main problem in industrial application of bearing vibration diagnostics is the masking of informative bearing signal by machine noise. The vibration signal of the rolling bearing is often covered or concealed by other structural vibrations sources, such as gears. Although a number of vibration diagnostic techniques have been developed over the last several years, in many cases these methods are quite complicated in use or only effective at later stages of damage development. This paper presents an EMD-based rolling bearing diagnosing method that shows potential for bearing damage detection at a much earlier stage of damage development.By using EMD a raw vibration signal is decomposed into a number of Intrinsic Mode Functions (IMFs). Then, a new method of IMFs aggregation into three Combined Mode Functions (CMFs) is applied and finally the vibration signal is divided into three parts of signal: noise-only part, signal-only part and trend-only part. To further bearing fault-related feature extraction from resultant signals, the spectral analysis of the empirically determined local amplitude is used. To validate the proposed method, raw vibration signals generated by complex mechanical systems employed in the industry (driving units of belt conveyors), including normal and fault bearing vibration data, are used in two case studies. The results show that the proposed rolling bearing diagnosing method can identify bearing faults at early stages of their development.     

Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).     

Acoustic multipath structure in direct zone of deep water and bearing estimation of tow ship noise of towed line array

In the towed line array sonar system, the tow ship noise is the main factor that affects the sonar performance. Conventional noise cancelling methods assume that the noise is towards the endfire direction of the array. An acoustic experiment employing a towed line array is conducted in the western Pacific Ocean, and a strange bearing-splitting phenomenon of the tow ship noise is observed in the array. The tow ship noise is split into multiple noise signals whose bearings are distributed between 10° and 90° deviating from the endfire direction. The multiple interferences increase the difficulty in recognizing the target for the sonar operator and noise cancellation. Therefore, making the mechanism clear and putting forward the tow ship noise splitting bearing estimation method are imperative. In this paper, the acoustic multi-path structure of the tow ship in deep water is analyzed. Then it is pointed out that the bearing-splitting phenomenon is caused by the main lobe of direct rays and bottom-reflected rays, as well as several side lobes of direct rays. Meanwhile, the indistinguishability between the elevation angle and the bearing angle due to the axial symmetry of a strict horizontal line array causes the bearing to deviate from the endfire direction. Based on the theory above, a method of estimating bearing of the tow ship noise in deep water is proposed. The theoretical analysis results accord with the experimental results, which helps to identify the target and provide correct initial bearing guidance for noise cancelation methods.     

滚动轴承声信号特征提取和诊断试验研究

为解决振动检测方法不能有效识别低速旋转机械滚动轴承故障问题,利用声发射检测方法,建立了滚动轴承低速声发射信号采集试验装置,对模拟人工缺陷滚动轴承声发射信号进行了采集,进而对滚动轴承声发射信号进行总体平均经验模式分解,结合能量矩及相关系数法综合判断分解后各模态分量的真伪,据此提取出特征信号并做出其局部Hilbert边际谱,最后对滚动轴承各种故障模式进行诊断。试验结果表明该诊断方法能准确识别滚动轴承声发射信号故障频率,依据特征频率及幅值大小可对低速滚动轴承故障进行有效诊断。     

基于神经网络的零件缺陷机器视觉识别系统

零件缺陷检测是保证零件使用安全的重要手段。传统的零件缺陷检测法需要有操作人员参与其中,易受主观因素影响,检测的效率及精度得不到良好的保证。而采用机器视觉技术的检测法可实现实时在线的自动检测,无需人工参与,这就极大的提高了生产效率。本文以小轴承表面为研究对象,针对微小轴承的表面结构、尺寸、检测精度和缺陷特征,设计了基于BP神经网络的零件缺陷机器视觉在线自动检测系统,其采用机器视觉技术,构建了BP神经网络检测识别模型,采用进行图像特征提取的间接识别方法,对微小轴承缺陷进行实时检测。实验结果证明了人工神经网络模型的检测能力的可靠性。     

QUASI-PERIODIC VIBRATION OF CRACKED ROTOR ON FLEXIBLE BEARINGS

A cracked rotor on flexible bearings is studied in this paper. The vibration of such a system has many complexities because of the crack and bearing flexibility. However, if the properties of the bearings are known, the system can be simplified by supposing that, the vibration due to weight is dominant. Equations of motion are derived, and a linear system in which the crack has been considered as an external disturbance described by a series of trigonometric functions is obtained. Consequently, the quasi-periodic vibrations of the rotor and bearings are established by harmonic balance method and approximate values of the vibration determined by truncating the higher order terms. It is believed that the simulated results will be useful for crack detection in the case of weight-dominant rotors.     

Maximum cross-correlated kurtosis-based unsaturated stochastic resonance and its application to bearing fault diagnosis

Considering the random impulses of mechanical noise and the limitations involved while identifying mechanical fault impulse signals via traditional measurement indices of signal-to-noise ratio, which require the characteristic frequency to be known in advance, this study proposes an adaptive unsaturated stochastic resonance method employing maximum cross-correlated kurtosis as the signal detection index. The proposed method combines the features of a cross-correlated coefficient to indicate periodic fault transients and those of spectrum kurtosis to locate these transients in the frequency domain. Actual vibration signals collected from motor and gear bearings subjected to heavy noise are used to demonstrate the effectiveness of the proposed method. Through a coarse tree-based machine learning method, the proposed method is verified to be more suitable for explaining the periodic impulse components of bearing signals, as compared to the ensemble empirical mode decomposition denoising method and unsaturated stochastic resonance using the kurtosis-intercorrelation index.     

基于规范化样本拆分的轴承缺陷检测

表面缺陷对轴承的性能和寿命存在严重影响。近年来,深度学习在缺陷检测中发挥了重要的作用,然而对于轴承检测而言,缺陷样本的采集耗时耗力。选择轴承内径作为研究对象,根据轴承的对称性特性提出一种规范化样本拆分方法,可有效扩充轴承样本数据集。分别采用不同的样本处理方法,而后利用ResNet网络训练轴承缺陷检测模型,进行多组对比实验,实验结果表明:直接采用原始图像进行网络训练,检测效果较差,模型的AUC (area under the curve)仅为0.558 0;对原始图像进行样本拆分,训练出的模型检测效果有所提升,其模型AUC提升为0.632 6;将原始图像进行4点透视变换校正后再进行网络训练,检测效果同样有所提升,其模型AUC提升为0.661 3;将原始图像进行透视变换校正且规范化样本拆分后,检测效果最好,其模型AUC增加为0.849 6。     

Wind turbine fault detection using multiwavelet denoising with the data-driven block threshold

Rapid expansion of wind turbines has drawn attention to reduce the operation and maintenance costs. Continuous condition monitoring of wind turbines allows for early detection of the generator faults, facilitating a proactive response, minimizing downtime and maximizing productivity. However, the weak features of incipient faults in wind turbines are always immersed in noises of the equipment and the environment. Wavelet denoising is a useful tool for incipient fault detection and its effect mainly depends on the feature separation and the noise elimination. Multiwavelets have two or more multiscaling functions and multiwavelet functions. They possess the properties of orthogonality, symmetry, compact support and high vanishing moments simultaneously. The data-driven block threshold selected the optimal block length and threshold at different decomposition levels by using the minimum Stein’s unbiased risk estimate. A multiwavelet denoising technique with the data-driven block threshold was proposed in this paper. The simulation experiment and the feature detection of a rolling bearing with a slight inner race defect indicated that the proposed method successfully detected the weak features of incipient faults.     

水下多目标方位的联合检测与跟踪

针对水下多目标方位跟踪及航迹关联问题,提出了一种粒子滤波的联合检测与跟踪方法.该方法在状态滤波过程中不需要方位观测值的输入,直接根据波束能量评估粒子的似然函数;利用交叉和变异算子进化小权值样本,通过低差异性序列的重采样提高子代粒子多样性。实现了多目标的跟踪并避免了方位观测量与多目标航迹关联的问题。仿真结果表明,在航迹断续和航迹交叉的情况下,该方法能够连续准确地跟踪目标方位。利用水下无人平台舷侧线阵的试验数据对算法性能进行了验证,正横方向的跟踪误差在3°以内;在目标运动模型失配时仍可以收敛到正确的方位航迹,没有出现错跟与失跟现象,可提高对交叉、汇聚及分离的多目标方位航迹的连续检测与跟踪能力.      

Granular packings and fault zones

The failure of a two-dimensional packing of elastic grains is analyzed using a numerical model. The packing fails through formation of shear bands or faults. During failure there is a separation of the system into two grain-packing states. In a shear band, local "rotating bearings" are spontaneously formed. The bearing state is favored in a shear band because it has a low stiffness against shearing. The "seismic activity" distribution in the packing has the same characteristics as that of the earthquake distribution in tectonic faults. The directions of the principal stresses in a bearing are reminiscent of those found at the San Andreas Fault.     

Statistical model of electrostatic discharge hazard in bearings of induction motor fed by inverter

In this paper, a statistical approach has been applied to determine the influence of inverter drive parameters on the rate and the amplitude of flashover currents in bearings of AC motors. The main cause of bearing currents is a charge accumulated on the shaft as a result of temporarily electric asymmetry at the output of the inverter in a presence of parasitic capacitive couplings inside the motor. The proposed statistical method could be the basis for comparative analyses of bearing damage hazard in a various drive configurations.     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.     

A novel structure of permanent-magnet-biased radial hybrid magnetic bearing

The paper proposes a novel structure for a permanent-magnet-biased radial hybrid magnetic bearing. Based on the air gap between the rotor and stator of traditional radial hybrid magnetic bearings, a subsidiary air gap is first constructed between the permanent magnets and the inner magnetic parts. Radial magnetic bearing makes X and Y magnetic fields independent of each other with separate stator poles, and the subsidiary air gap makes control flux to a close loop. As a result, magnetic field coupling of the X and Y channels is decreased significantly by the radial hybrid magnetic bearing and makes it easier to design control systems. Then an external rotor structure is designed into the radial hybrid magnetic bearing. The working principle of the radial hybrid magnetic bearing and its mathematical model is discussed. Finally, a non-linear magnetic network method is proposed to analyze the radial hybrid magnetic bearing. Simulation results indicate that magnetic fields in the two channels of the proposed radial hybrid magnetic bearing decouple well from each other.