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.     

Detection of weak fault using sparse empirical wavelet transform for cyclic fault

The successful prediction of the remaining useful life of rolling element bearings depends on the capability of early fault detection. A critical step in fault diagnosis is to use the correct signal processing techniques to extract the fault signal. This paper proposes a newly developed diagnostic model using a sparse-based empirical wavelet transform (EWT) to enhance the fault signal to noise ratio. The unprocessed signal is first analyzed using the kurtogram to locate the fault frequency band and filter out the system noise. Then, the preprocessed signal is filtered using the EWT. The l_q-regularized sparse regression is implemented to obtain a sparse solution of the defect signal in the frequency domain. The proposed method demonstrates a significant improvement of the signal to noise ratio and is applicable for detection of cyclic fault, which includes the extraction of the fault signatures of bearings and gearboxes.     

Threshold Maps for Inclusion-Initiated Micro-Cracks and White Etching Areas in Bearing Steel: The Role of Impact Loading and Surface Sliding

Wind turbine gearbox (WTG) bearings can fail prematurely, significantly affecting wind turbine operational availability and the cost of energy production. The current most commonly accepted theory of failure mechanism is that the bearing subsurface is weakened by white etching crack (WEC) networks that eventually lead to the flaking away of material from the bearing surface. Subsurface damage due to rolling contact fatigue (RCF) is thought to be the main cause of premature failure, resulting from the initiation of micro-cracks, often at non-metallic inclusions or other material defects, which then propagate to the bearing surface. This study proposes a hypothesis that impact loading together with high levels of surface traction and contact pressure are important factors contributing to the initiation of micro-cracks and white etching areas (WEAs) at non-metallic inclusions which may lead to the formation of WEC networks. Both repeated impact and twin-disc RCF tests were designed to investigate inclusion-initiated micro-cracks and WEAs at subsurface in order to test this hypothesis. This led to the recreation of inclusion-initiated micro-cracks and WEAs in tested specimens, similar to the subsurface damage observed at inclusions in failed WTG bearing raceways. Tests were carried out to determine threshold levels of contact pressure, surface traction, and impact loading required for the formation of inclusion-initiated micro-cracks and WEAs.     

基于谱峭度和AR模型的滚动轴承故障诊断

提出基于自回归(Autoregressive,简称AR)预测滤波的谱峭度分析方法,将其应用于滚动轴承的早期故障诊断。通过结合AR预测滤波器提取轴承故障信号共振衰减成分的特性,利用谱峭度方法对AR预测滤波器滤波后的信号进行处理,实现了滚动轴承早期微弱故障的识别。通过滚动轴承的疲劳全寿命加速实验获取滚动轴承的自然故障信号,克服了传统轴承故障诊断人工加工故障的不足。通过试验数据的分析表明,基于AR预测滤波的谱峭度方法不仅能够消除干扰成分提取故障特征,还能增加谱峭度方法的稳定性。     

Compound fault diagnosis of bearings using improved fast spectral kurtosis with VMD

The fast spectrum kurtosis (FSK) algorithm can adaptively identify resonance bands of a signal, and fault characteristics can be extracted by analyzing the selected frequency bands. However, in practical applications, the bearing failure may be composed of various faults (inner ring/outer ring/rolling element) and the faults may be located in different resonant bands. Due to the interference between different fault components and noise, the weak components may be submerged when FSK is used to deal with compound fault signals. To improve the accuracy of an FSK processing compound fault located in different resonance bands, an improved FSK method combined with the variational mode decomposition (VMD) is proposed. First, the parameters (number of components K / penalty factor α ) in the VMD decomposition are selected, and the original compound fault signal is preprocessed by VMD decomposition, so that the original signal is decomposed into K variational intrinsic mode function (VIMF) components. The resonance center bands of these signals are different from each other, so the different fault information is located in different VIMF. Finally, each VIMF component is calculated by FSK. Through the simulated and experimental analysis, the method can accurately identify the resonance bands, and identify the weak fault characteristics of compound bearing fault.     

Vibration response analysis caused by rubbing between rotating blade and casing

Two types of blade-tip rubbing due to the static misalignment of the bladed-disk center and casing center and casing deformation are simulated. By applying aerodynamic load in the blade lateral/flexural direction, vibration responses due to blade-casing rubbing are analyzed under the run-up process with constant angular acceleration and the steady-state process at 10000 rev/min. The effects of some parameters, such as the static misalignment e _c, casing stiffness k _c and casing deformation n _p, on the system vibration responses are also illustrated by spectrum cascades, time-domain waveforms of displacement, normal rubbing forces, amplitude spectra and the impulse P in a single blade-casing rubbing period. The results show that blade-tip rubbing will cause amplitude amplification and harmonic resonance phenomena when the multiple frequencies (nf _r) of rotational frequency (f _r) coincide with the first three flexural dynamic frequencies of the blade (f _n1, f _n2 and f _n3). For example, the displacement amplitudes at 3f _r, 14f _r and 38f _r are large and the vibration is dominant near f _n1. In addition, the casing deformation mainly excites the dominant Blade passing frequency (BPF), which is related to the casing deformation coefficient n _p. By comparing these impulse values, for the selected parameters in this paper, the casing stiffness has a greater effect on impulse than the static misalignment and casing deformation coefficient. The impulse shows a linear increase trend with the increasing static misalignment, and it decreases under the large n _p because the contact time decreases with the increase of n _p.     

A technique for forecasting the durability of rolling bearings and the optimum choice of lubricants under flood-lubrication and oil-starvation conditions

Effective conditions of friction in units that ensure performance and maximum longevity have been created by flood lubrication under the conditions of elastohydrodynamic (EHD) lubrication. In connection with this, prolonging the service life of friction units by ensuring local requirements to the amount of oil in the contact area and establishing the optimum choice of lubricating material in terms of its viscosity ratings is a topical problem for rolling bearings. Thus, it is suggested to determine the parameter of the lubricating layer (λ) values for the conditions of flood lubrication (λ_o) and progressing (λ_o.s ) and catastrophic (λ _c.s ) oil starvation for a wide spectrum of lubricating materials of different viscosity, since, in oil starvation mode, the efficiency of EHD conditions is violated, the lubrication mode is disturbed, and the bearing capacity of the EHD lubricating layer is lost.     

Taguchi-based grey relational analysis for modeling and optimizing machining parameters through dry turning of Incoloy 800H

The present research focused on the optimization of machining parameters and their effects by dry-turning an incoloy 800H on the basis of Taguchi-based grey relational analysis. Surface roughness (Ra, Rq and Rz), cutting force (Fz), and cutting power (P) were minimized, whereas Material removal rate (MRR) was maximized. An L ₂₇ orthogonal array was used in the experiments, which were conducted in a computerized and numerical-controlled turning machine. Cutting speed, feed rate, and cut depth were set as controllable machining variables, and analysis of variance was performed to determine the contribution of each variable. We then developed regression models, which ultimately conformed to investigational and predicted values. The combinational parameters for the multiperformance optimization were V = 35 m/min, f = 0.06 mm/rev and a = 1 mm, which altogether correspond to approximately 48.98 % of the improvement. The chip morphology of the incoloy 800H was also studied and reported.     

Calculating the wear resistance of metals in frictional pairs 3. Calculating the wear rate of steel in roller pairs with rolling friction and slip

On the hypothesis that slipping friction has the primary influence on the wear of steel in a rolling frictional pair with slip, the dimensionless wear rate I of wheel steel in the driven roller and the dimensional wear rate W (expressed in terms of I) are calculated. Satisfactory agreement of the theoretical and experimental wear rates is observed in the regions of weak, pronounced, and catastrophic wear.     

Experimental study of effects of water-cement ratio on the acoustic emission rate “<Emphasis Type="Italic">a</Emphasis>” values in concrete

The critical microcracks make durability of the concrete seriously weakened, and the water-cement (w/c) ratio plays a key role in the durability of concrete, acoustic emission (AE) rate “a” value is an effective parameter to evaluate the amount of critical microcracks quantitatively, so the study on the influence factors and the distribution of w/c ratio on the value “a” can provide a promising reference value for the practical engineering. A total of 90 specimens in different w/c ratio are tested under axial compression in the experiment. The whole damage evolution processes are monitored by fulldigital AE acquisition system. The differences of the amount of critical microcracks in different w/c ratio are explored based on the AE rate process theory, and the rule of the effects of w/c ratio on the rate “a” value is revealed through the analysis of fracture energy and cumulative AE energy in the failure process. The results show that a linear relationship was observed between cumulative AE energy and rate “a” value, the amount of critical microcracks and the rate “a” value increase with the increase of w/c ratio, while the cumulative AE energy and cumulative AE hits are on the contrary.     

Improvement of grinding performance and investigation of cutting parameters using a grinding mechanism with secondary rotational axis

“Grinding Mechanism having Advanced Secondary Rotational Axis” (GMASRA) is one of the newer plane surface grinding methods that have an uncommon abrasion mechanism. Unlike conventional methods, in GMASRA, there are two rotations of a wheel. The first rotation is the same as in conventional grinding methods, which is the circumferential rotation. The other rotation is the newly developed axial rotation, where the wheel rotates around itself perpendicular to its radial axis. In this study, the effects of certain cutting parameters on arithmetical mean deviation of the assessed profile (the R_a parameter) were investigated. Particularly, the effects of cutting parameters on R_a in the GMASRA grinding process were examined. The selected cutting parameters were the depth of cut, the number of axial revolutions of the wheel, and the stepover distance of the wheel. Five wheels with different properties were chosen. Additionally, GMASRA was modeled using the Taguchi orthogonal test design. In this orthogonal design, the depth of cut, the spindle speed, and the type of grinding wheel were chosen as the control factors. The effect of the specified control factors on the surface roughness was demonstrated using an analysis of variance (ANOVA) test. Results show that GMASRA produced better R_a values than the conventional method. R_a values were very close to each other in every part of the ground workpieces. According to the modeling results, the spindle speed had the highest effect on R_a, followed by the depth of cut and the type of grinding wheel. GMASRA is also very cost effective and can be adapted to most milling machines and CNC milling machines.     

基于平方包络谱相关峭度的最优共振解调诊断滚动轴承故障

利用峭度指标识别滚动轴承共振频带,结合包络分析解调故障特征,是滚动轴承故障诊断的常用方法。峭度指标虽然能够表征瞬态冲击特征的强弱,却无法利用瞬态冲击特征循环发生的特点,导致其难以区分脉冲噪声和循环瞬态冲击,无法准确识别共振频带,进而容易导致错误的故障诊断结果。受峭度和信号自相关的启发,重新定义相关峭度,提出平方包络谱相关峭度新指标;并结合Morlet小波滤波和粒子群优化算法,提出一种滚动轴承最优共振解调方法。通过与峭度、谱峭度等进行对比,仿真和试验分析结果表明平方包络谱相关峭度能够准确识别循环瞬态冲击;最优共振解调能够稳健确定共振频带的最优中心频率和带宽,准确解调诊断滚动轴承故障,验证了平方包络谱相关峭度在检测循环瞬态冲击和识别最优共振频带中的有效性和优越性。     

Quantitative proximate X-ray analysis of loose raw materials based on detection of diffraction and characteristic radiation

A method of rapid X-ray analysis is proposed. The content of the method is that the ratio I _d/I _i ^A is measured in two channels of a γ spectrometer, one of which is configured for the diffraction maximum of the determined phase (I _d) and the other measures the intensity of the spectral line of secondary element A (I _i ^A ), the atomic number of which is the same as that of the material of the X-ray tube anode. Results of the X-ray analysis of chromite and molybdenum are presented. The test rate was 7 min per test. The maximum deviation from the content of MoS₂ was 0.4% in standard specimens with concentrations of 24–29% and that of Fe and Cr₂O₃ was 0.3% for concentrations of 14–19%.     

Capacitive MEMS accelerometers for measuring high-g accelerations

A possibility of creating a capacitive accelerometer for measuring high-g accelerations (up to 10⁶ g and higher) is discussed. It is demonstrated that insertion of a thin electret film with a high surface potential into the gap between the electrodes ensures significant expansion of the frequency and amplitude ranges of acceleration measurements, whereas the size of the proposed device is smaller than that of available MEMS accelerometers for measuring high-g accelerations. A mathematical model of an electret accelerometer for high-g accelerations is developed, and the main specific features of accelerometer operation are analyzed.     

Integrated ANN-LWPA for cutting parameter optimization in WEDM

In this paper, we present a new approach to determinate cutting parameters in wire electrical discharge machining (WEDM), integrated artificial neuron network (ANN), and wolf pack algorithm based on the strategy of the leader (LWPA). The cutting parameters considered in this paper are pulse-on, current, water pressure, and cutting feed rate. Models of the effects of the four parameters on machining time (T_p), machining cost (C_p), and surface roughness (Ra) are mathematically constructed. An ANN-LWPA integration system with multiple fitness functions is proposed to solve the modelling problem. By using the proposed approach, this study demonstrates that T_p, C_p, and Ra can be estimated at 164.1852 min, 239.5442 RMB, and 1.0223 μm in single objective optimization, respectively. For example, as for Ra, integrated ANN-LWPA has optimized the Ra value by the reduction of 0.1337 μm (11.6 %), 0.3377 μm (24.8 %), and 0.105 μm (10.3 %) compared to experimental data, regression model, and ANN model, respectively. Consequently, the ANN-LWPA integration system boasts some advantages over decreasing the value of fitness functions by comparison with the experimental regression model, ANN model, and conventional LWPA result. Moreover, the proposed integration system can be also utilized to obtain multiple solutions by uniform design-based exploration. Therefore, in order to solve complex machining optimization problems, an intelligent process scheme could be integrated into the numeric control system of WEDM.     

Soft computing models and intelligent optimization system in electro-discharge machining of SiC/Al composites

In this paper, a multi-variable regression model, a back propagation neural network (BPNN) and a radial basis neural network (RBNN) have been utilized to correlate the cutting parameters and the performance while electro-discharge machining (EDM) of SiC/Al composites. The four cutting parameters are peak current (I_p), pulse-on time (T_on), pulse-off time (T_off), and servo voltage (S_v); the performance measures are material remove rate (MRR) and surface roughness (Ra). By testing a large number of BPNN architectures, 4-5-1 and 4-7-1 have been found to be the optimal one for MRR and Ra, respectively; and it can predict them with 10.61 % overall mean prediction error. As for RBNN architectures, it can predict them with 12.77 % overall mean prediction error. The multivariable regression model yields an overall mean prediction error of 13.93 %. All of these three models have been used to study the effect of input parameters on the material remove rate and surface roughness, and finally to optimize them with genetic algorithm (GA) and desirability function. Then, an intelligent optimization system with graphical user interface (GUI) has been built based on these multi-optimization techniques, in which users can obtain the optimized cutting parameters under the desired surface roughness (Ra).     

Heat transfer in a turbulent jet impinging on a moving plate considering high plate-to-jet velocity ratios

In this paper, heat transfer characteristics of a turbulent slot jet impinging orthogonally on an isothermal moving hot plate is studied numerically. The governing equations were discretized using the finite volume method and the υ ² — f turbulence model was employed for turbulence modeling. The effect of the jet Reynolds number and the plate-to-jet velocity ratio (R) on the Nusselt were investigated. Despite of most previous studies, which have been restricted to R≤2, in the present research higher values of R, also were considered (0≤R≤6). Range of studied jet Reynolds number was between 3000 and 60000. The results indicate that at a fixed plate-to-jet velocity ratio increment of the Reynolds number leads to the enhancement of the average Nusselt number. For each Reynolds number, the average Nusselt number reduces with increasing the plate-to-jet velocity ratio until it becomes minimum at R = 1.25. For R>1.25 trend changes so that these parameters increase. In addition, it was found that only for R>2.5 the average Nusselt number is improved due to the plate motion in comparison with the stationary jet. The results are validated against available experimental data, showing good agreement.     

Multi-color space threshold segmentation and self-learning <Emphasis Type="Italic">k</Emphasis>-NN algorithm for surge test EUT status identification

The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm (k-NN) for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space (L*a*b*, hue saturation lightness (HSL), hue saturation value (HSV)) to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample S _r was classified by the k-NN algorithm with training set T _z according to the feature vector, which was formed from number of pixels, eccentricity ratio, compactness ratio, and Euler’s numbers. Last, while the classification confidence coefficient equaled k, made S _r as one sample of pre-training set T _z ′. The training set T _z increased to T _z+1 by T _z ′ if T _z ′ was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples (a total of 21600 frames), the algorithm had a 98.65%identification accuracy, also selected five groups of samples to enlarge the training set from T ₀ to T ₅ by itself.     

Nonparametric estimation of a probability density function and its derivatives. <Emphasis Type="Italic">C</Emphasis>-approach

Nonparametric (kernel) estimation of a probability density function f(x) for a sample of finite size is considered using the C-approach. The smoothness parameter β of the estimated probability density is introduced. For the case β > 2, it is shown that the convergence of the density estimate f _n (x) to the function f(x) can be improved by using alternating-sign weight functions (higher-order weight functions). Estimation of the derivatives of a function is briefly considered using the same approach.     

The use of new resonance integrals for determining the parameters of the epithermal neutron spectrum

A simple method for determining parameters α and b in the presentation of the epithermal neutron spectrum in the form of φ_epi(E) = b/E ^{1 + α} using expansion of function φ_epi(E) in Taylor series with respect to parameter α is described. The problem of determining the parameters is reduced to finding a solution to a system of algebraic equations. The approximation used describes the spectrum with an adequate accuracy up to energies of ～1–50 keV, depending on value α ～ 0.2?0.1, with the help of the first two or three expansion terms (resonance integrals), respectively. The first two resonance integrals for reactions ¹⁹⁷Au(n, γ)¹⁹⁸Au and ⁵⁵Mn(n, γ)⁵⁶Mn are estimated. The measured rates of the appropriate reactions and the values of parameters α and b for the case of an assembly with a graphite moderator are presented.