激光超声信号变分模态分解与裂纹定量检测

针对激光超声检测技术应用于金属增材件中所获取的信号具有复杂、多模态、信噪比低的特性的问题,获取激光超声信号进行时频分析,探究其频域可分性,采用变分模态分解算法根据频域特征进行分离并提取最佳表面波模态。在此基础上,提出一种基于激光超声信号B扫图结合变分模态分解提取表面回波特征值技术,对金属增材件表面裂纹长度进行定量检测。针对直接观察B扫图获取裂纹长度信息存在误差较大的问题,通过对变分模态分解提取的表面波模态在有无裂纹时反射回波峰峰值的变化分析,绘制扫查位置-峰峰值图并据此精确获取裂纹起始和结束位置,检测结果的相对误差不超过8%。与直接获取原始信号B扫图的裂纹长度信息相比,提高了检测精度。该方法在金属增材件的激光超声信号的特征提取与定量检测方面具有可行性。

滚动轴承接触疲劳裂纹建模与扩展规律研究

针对滚动轴承的疲劳剥落损伤演化问题,采用扩展有限元方法研究疲劳裂纹扩展机理,通过算例验证了扩展有限元方法的可靠性。运用断裂力学理论建立了包含次表面初始裂纹的滚动接触疲劳扩展有限元模型,分析了初始裂纹倾斜角度、所处深度和初始裂纹长度对次表面裂纹生长到表面的扩展路径影响机制,通过分析应力强度因子变化研究了裂纹扩展机理。研究结果表明,次表面疲劳裂纹以滑开型裂纹扩展模式为主;初始裂纹角度对疲劳裂纹扩展路径影响较大,初始裂纹角度在15°~45°时裂纹会转向表面发展而形成疲劳剥落;初始裂纹尺寸和深度对裂纹扩展路径形貌的影响较小,但会影响裂纹扩展的难易程度。

高等教育视角下的高考综合改革成效分析与联动机制探索——以西安交通大学为例

国家深化考试招生制度改革对高校既是机遇,也是挑战,高校需要以高度的责任感积极主动应对这次改革。通过对比分析高考综合改革前后西安交通大学生源质量的变化,客观地评价了高考综合改革的成效。从志愿填报方式、招生计划编制、育人模式和管理模式等角度分析了高考综合改革给高校带来的影响,结合西安交通大学"双一流"建设的目标和任务,提出高校加强自身内涵建设、完善人才培养体系和建立联动机制等方面的改革思路和具体举措,并对高考综合改革的进一步优化和完善提出几点建议。

多轴系耦合作用下齿轮系统转子裂纹故障的振动特性研究

为了探究多轴系耦合齿轮系统中的转子裂纹故障与单轴系转子裂纹故障振动响应特性的异同点,基于Jones轴承建模理论,建立滚动轴承的拟静力学模型;利用Timoshenko梁单元建立传动轴的有限元模型;考虑时变啮合刚度、齿轮传递误差、陀螺效应等因素,利用集中参数法建立齿轮副的动力学模型。将轴承、传动轴与齿轮副模型进行集成,建立齿轮系统非线性动力学模型;利用能量释放率理论与应力强度因子为零法分析裂纹转子单元的呼吸效应,利用Newmark-β数值积分法对转子裂纹故障进行动力学仿真,研究转子裂纹故障的振动响应特征。结果表明:与单轴转子系统转子裂纹的振动响应特征不同,当齿轮系统发生转子裂纹故障时,由于齿轮啮合的引起的耦合效应及转子裂纹引起的呼吸效应,时域响应表现出明显的幅值调制现象,频域中转频及其2倍频幅值增加明显,在啮合频率处伴有明显的边频带。研究结果可为齿轮系统转子裂纹故障的监测与诊断提供理论基础。

Steam turbine rotor crack detection using sifting process of EMD and B-spline wavelet on the interval element model

A high-precision identification method for steam turbine rotor crack is presented. By providing the first three measured natural frequencies,contours for the specified natural frequency are plotted in the same coordinate,and the intersection of the three curves predicts the crack location and size. The cracked rotor system is modeled using Bspline wavelet on the interval(BSWI)finite element method,and a method based on empirical mode decomposition(EMD)and Laplace wavelet is implemented to improve the identification precision of the first three measured natural frequencies. Compared with the classical nondestructive testing,the presented method shows its effectiveness and reliability. It is feasible to apply this method to the online health monitoring for rotor structure.

Tunable Q-factor wavelet transform denoising with neighboring coefficients and its application to rotating machinery fault diagnosis

Fault diagnosis of rotating machinery is of great importance to the high quality products and long-term safe operation.However,the useful weak features are usually corrupted by strong background noise,thus increasing the difficulty of the feature extraction.Thereby,a novel denoising method based on the tunable Q-factor wavelet transform(TQWT)using neighboring coefficients is proposed in this article.The emerging TQWT possesses excellent properties compared with the conventional constant-Q wavelet transforms,which can tune Q-factor according to the oscillatory behavior of the signal.Meanwhile,neighboring coefficients denoising is adopted to avoid the overkill of conventional term-by-term thresholding techniques.Because of having the combined advantages of the two methods,the presented denoising method is more practical and effective than other methods.The proposed method is applied to a simulated signal,a rolling element bearing with an outer race defect from antenna transmission chain and a gearbox fault detection case.The processing results demonstrate that the proposed method can successfully identify the fault features,showing that this method is more effective than the conventional wavelet thresholding denoising methods,term-by-term TQWT denoising schemes and spectral kurtosis.

A pseudo wavelet system-based vibration signature extracting method for rotating machinery fault detection

The rotating machinery,as a typical example of large and complex mechanical systems,is prone to diversified sorts of mechanical faults,especially on their rotating components.Although they can be collected via vibration measurements,the critical fault signatures are always masked by overwhelming interfering contents,therefore difficult to be identified.Moreover,owing to the distinguished time-frequency characteristics of the machinery fault signatures,classical dyadic wavelet transforms(DWTs) are not perfect for detecting them in noisy environments.In order to address the deficiencies of DWTs,a pseudo wavelet system(PWS) is proposed based on the filter constructing strategies of wavelet tight frames.The presented PWS is implemented via a specially devised shift-invariant filterbank structure,which generates non-dyadic wavelet subbands as well as dyadic ones.The PWS offers a finer partition of the vibration signal into the frequency-scale plane.In addition,in order to correctly identify the essential transient signatures produced by the faulty mechanical components,a new signal impulsiveness measure,named spatial spectral ensemble kurtosis(SSEK),is put forward.SSEK is used for selecting the optimal analyzing parameters among the decomposed wavelet subbands so that the masked critical fault signatures can be explicitly recognized.The proposed method has been applied to engineering fault diagnosis cases,in which the processing results showed its effectiveness and superiority to some existing methods.

A novel vibration modeling method for a rotating blade with breathing cracks

This paper proposes a vibration modeling method for a rotating blade with breathing cracks, considering the coupling of the centrifugal effects, the breathing effects and the crack effects. Firstly, considering the combined effects of the centrifugal stress and the bending stress, a crack breathing model for the rotating blade is proposed. Since the crack surface cannot provide the spanwise centrifugal tensile stress, an additional bending moment will be generated at the crack surface in the rotating state.Therefore, an additional bending moment caused by the centrifugal stress is then constructed. In addition, due to the discontinuity of the crack cross section, the additional stiffness caused by the centrifugal effects of the cracked blade will be smaller than that of the normal blade. Therefore, a correction coefficient of the centrifugal stiffness is built. Furthermore, based on the Lagrange equation and the assumed modal method, the vibration equation of the rotating blade with a breathing crack is established(RBC model), and a strategy is constructed for solving this vibration equation. Finally, taking the finite element model(FEM) as a reference, the vibration responses of the RBC model, the open crack model(OC) and the bilinear crack model(BC) are compared. The necessity of the RBC model is illustrated, and the accuracy of the RBC model is verified. The RBC model is a computationally efficient alternative to the finite element analysis for the rotating cracked blade. It can provide an important tool for the vibration modeling and the vibration analysis of the rotating shaft-disk-blade system with cracked blades.

A data-driven threshold for wavelet sliding window denoising in mechanical fault detection

Wavelet denoising is an effective approach to extract fault features from strong background noise.It has been widely used in mechanical fault detection and shown excellent performance.However,traditional thresholds are not suitable for nonstationary signal denoising because they set universal thresholds for different wavelet coefficients.Therefore,a data-driven threshold strategy is proposed in this paper.First,the signal is decomposed into different subbands by wavelet transformation.Then a data-driven threshold is derived by estimating the noise power spectral density in different subbands.Since the data-driven threshold is dependent on the noise estimation and adapted to data,it is more robust and accurate for denoising than traditional thresholds.Meanwhile,sliding window method is adopted to set a flexible local threshold.When this method was applied to simulation signal and an inner race fault diagnostic case of dedusting fan bearing,the proposed method has good result and provides valuable advantages over traditional methods in the fault detection of rotating machines.

Gearbox fault diagnosis of rolling mills using multiwavelet sliding window neighboring coefficient denoising and optimal blind deconvolution

Fault diagnosis of rolling mills, especially the main drive gearbox, is of great importance to the high quality products and long-term safe operation. However, the useful fault information is usually submerged in heavy background noise under the severe condition. Thereby, a novel method based on multiwavelet sliding window neighboring coefficient denoising and optimal blind deconvolution is proposed for gearbox fault diagnosis in rolling mills. The emerging multiwavelets can seize the important signal processing properties simultaneously. Owing to the multiple scaling and wavelet basis functions, they have the supreme possibility of matching various features. Due to the periodicity of gearbox signals, sliding window is recommended to conduct local threshold denoising, so as to avoid the "overkill" of conventional universal thresholding techniques. Meanwhile, neighboring coefficient denoising, considering the correlation of the coefficients, is introduced to effectively process the noisy signals in every sliding window. Thus, multiwavelet sliding window neighboring coefficient denoising not only can perform excellent fault extraction, but also accords with the essence of gearbox fault features. On the other hand, optimal blind deconvolution is carried out to highlight the denoised features for operators' easy identification. The filter length is vital for the effective and meaningful results. Hence, the foremost filter length selection based on the kurtosis is discussed in order to full benefits of this technique. The new method is applied to two gearbox fault diagnostic cases of hot strip finishing mills, compared with multiwavelet and scalar wavelet methods with/without optimal blind deconvolution. The results show that it could enhance the ability of fault detection for the main drive gearboxes.

Construction of adaptive redundant multiwavelet packet and its application to compound faults detection of rotating machinery

It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents.Due to diversity and complexity,the compound faults detection of rotating machinery under non-stationary operation turns to be a challenging task.Multiwavelet with two or more base functions may match two or more features of compound faults,which may supply a possible solution to compound faults detection.However,the fixed basis functions of multiwavelet transform,which are not related with the vibration signal,may reduce the accuracy of compound faults detection.Moreover,the decomposition results of multiwavelet transform not being own time-invariant is harmful to extract the features of periodical impulses.Furthermore,multiwavelet transform only focuses on the multi-resolution analysis in the low frequency band,and may leave out the useful features of compound faults.To overcome these shortcomings,a novel method called adaptive redundant multiwavelet packet(ARMP) is proposed based on the two-scale similarity transforms.Besides,the relative energy ratio at the characteristic frequency of the concerned component is computed to select the sensitive frequency bands of multiwavelet packet coefficients.The proposed method was used to analyze the compound faults of rolling element bearing.The results showed that the proposed method could enhance the ability of compound faults detection of rotating machinery.

The principle of second generation wavelet for milling cutter breakage detection

Performance degradation or failure of manufacturing equipment will badly influence machining quality.Because of discontinuousness of the milling process,dynamic signals produced in the milling process become non-stationary.This paper indicates that the essence of SGW(second generation wavelet) transform in non-stationary signal processing is the mathematics principle of inner product transform of a dynamic signal with basis functions.Namely,by means of the inner product operation of a signal with basis functions containing scale function and wavelet function,signal decomposition and reconstruction are obtained.Acoustic emission signals generated in the milling processes of a CNC machine were analyzed by using the basis functions of SGW which are oscillation,decay and compact support.The features of end milling cutter breakage have been extracted,and the influences on machining surface quality have been identified effectively,which provide scientific bases for fault diagnosis,error tracing and quality control.

Material analysis of the fatigue mechanism of rollers in tapered roller bearings

Rolling contact fatigue is the main failure mechanism of tapered roller bearings. This study investigated the fatigue mechanism of rollers in a tapered roller bearing that failed in a run-to-failure test. Roller microstructure and crack morphology were investigated through scanning electron microscopy. A microhardness test was performed to investigate the strain hardening of the roller material induced by rolling contact fatigue. Results showed that microcavities and holes are important influential factors of crack initiation and propagation. Crack propagation angle affects crack morphology and propagation mode. Material strain hardening accelerates crack growth. Furthermore, roller misalignment causes uneven hardenability and severe damage to roller ends.

A 3D nonlinear finite element method for the dynamic analysis of rotating rotor with a transverse crack

A general and efficient method is presented in this paper for studying the effects of unbalance on the breathing mechanism of crack.Based on 3D finite element models combined with a nonlinear contact approach for crack modeling, the method is free from theassumption of weight-dominance and can be used to gain deep insights into the breathing mechanism of crack. In order to greatlyreduce the computational time, a complex free-interface component mode synthesis (CMS) method is employed to reduce theorder of the model. Based on the proposed method, the effects of unbalance on the breathing mechanism of crack are discussed.Numerical results show that the unbalance can lead to significant changes in the breathing of crack, even when the unbalance force is about an order of magnitude smaller than the self-weight. Moreover, the level and orientation of the unbalance have also remarkable effects on the breathing behaviors of crack. Besides, a new universal non-steady breathing phenomenon of crack is firstly found in this paper, which denotes that the breathing speed of a crack is fluctuated over one revolution when there exists residual unbalance in the cracked rotor.

Maximal-overlap adaptive multiwavelet for detecting transient vibration responses from dynastic signal of rotating machineries

Vibration signal is an important prerequisite for mechanical fault detection.However,early stage defect of rotating machineries is difficult to identify because their incipient energy is interfered with background noises.Multiwavelet is a powerful tool used to conduct non-stationary fault feature extraction.However,the existing predetermined multiwavelet bases are independent of the dynamic response signals.In this paper,a constructing technique of vibration data-driven maximal-overlap adaptive multiwavelet(MOAMW)is proposed for enhancing the extracting performance of fault symptom.It is able to derive an optimal multiwavelet basis that best matches the critical non-stationary and transient fault signatures via genetic algorithm.In this technique,two-scale similarity transform(TST)and symmetric lifting(SymLift)scheme are combined to gain high designing freedom for matching the critical faulty vibration contents in vibration signals based on the maximal fitness objective.TST and SymLift can add modifications to the initial multiwavelet by changing the approximation order and vanishing moment of multiwavelet,respectively.Moreover,the beneficial feature of the MOAWM lies in that the maximal-overlap filterbank structure can enhance the periodic and transient characteristics of the sensor signals and preserve the time and frequency analyzing resolution during the decomposition process.The effectiveness of the proposed technique is validated via a numerical simulation as well as a rolling element bearing with an outer race scrape and a gearbox with rub fault.