Experimental investigation of AE and friction signals related to the durability of head/disk interface

The durability of a hard disk drive is one of the most critical issues that must be optimized for best performance. Especially as the flying height of the head slider of a hard disk drive decreases over the years, the concern for surface damage and head contamination continues to grow. In this paper the characteristics of AE and friction signals for various operating conditions using CSS and drag tests were investigated from the durability point of view. Also, the wear characteristics of the laser bumps on a magnetic disk were compared between the CSS and drag tests. The general shapes of the AE and friction signals during a single CSS test were quite similar even under less than ideal operating conditions. However, it was found that the AE signal was more sensitive than the friction signal in assessing the damage of the slider/disk interface. Finally, a correlation was established between the CSS and drag testing methods with respect to the laser bump wear. This outcome suggests that the drag test may be used to accelerate the surface damage effect of head/disk system.     

A new approach for the automatic evaluation of the solidification structure in steel using orientational entropy filtering

We present a new method for the automatic evaluation of the dendritic solidification structure in metallurgical micrographs of solidified steel. The evaluation of the microstructural parameters such as the primary dendrite arm spacing and the primary grain size are of high importance due to their direct relationship with the internal quality and mechanical properties of the cast product. Given the repeated geometric features in the micrographs and the regular pattern in colour intensity, we applied a filter mask to determine the local entropies within the masks in order to detect the centre coordinates of each individual dendrite. The orientation of the dendrites was determined by rotating the filter mask over each pixel to find the orientation which corresponds to the lowest entropy value. The segmentation of the microstructure was then performed via Delaunay tessellation and subsequent transformation of the triangular mesh into a rectangular grid, enabling the determination of the desired microstructural parameters.     

Indexes for performance evaluation of cameras applied to dynamic measurements

Thanks to technology improvements, the applications of vision-based measurement to dynamic applications have been increasing in the last years. The available image resolutions and the high grabbing frequencies allow to acquire high-speed moving object with a good scaling factor and to perform dynamic analysis of vibrating items. Uncertainty analysis of vision-based measuring devices working in almost-static conditions was widely studied in literature, but the case of dynamic measurements still needs a further analysis. The measuring performances thus depend on the well-known parameters that affect the static performances (image resolution and contrast, processing algorithm, noise, etc.) but also on other factors, above all the exposure time and the camera-object relative motion, in terms of instantaneous velocity and acceleration. In this work, a performance analysis of imaging devices applied to dynamic measurements is proposed. The analysis aims to qualify the measurement uncertainty by some indexes, proposed in this work, and designed to quantify the motion effect on the acquired images and consequently the measurement uncertainty. These indexes are based on exposure time and Spatial Frequency Response (SFR) function, which is widely applied in literature and recommended in international standards for the image quality estimation in static acquiring conditions. Appropriate developments of SFR are proposed herein to obtain information on the image quality grabbed in dynamic conditions. The effectiveness of the proposed indexes are proved by several tests, where a target is moved with an harmonic law in controlled condition (varying its frequency and amplitude) and fixing different acquisition conditions in terms of lighting settings, diaphragm aperture, exposure time, etc.     

Cohesive and continuum damage models applied to fracture characterization of bonded joints

In this work, two different methods for simulating damage propagation are presented and applied to fracture characterization of bonded joints in pure modes I and II. The cohesive damage model is based on a special developed interface finite element including a linear softening damage process. In the continuum damage model the softening process is performed by including a characteristic length associated with a given Gauss point. The models were applied to the simulation of “double cantilever beam” (DCB) and “end notched flexure” (ENF) tests used to obtain the critical strain release rates in mode I and II of bonded joints. In mode I it was observed, under certain conditions, a good agreement between the results obtained by the two models with the reference value of critical strain energy release rate in mode I (G_Ic), which is an inputted parameter. However, in mode II some discrepancies on the obtained G_IIc values were observed between the two models. These inaccuracies can be explained by the simplifying assumptions inherent to the cohesive model. Better results were achieved considering the crack equivalent concept.     

Fast algorithms for frequency, amplitude and phase evaluation of nonsinusoidal signals with noises

A fast and accurate algorithm for frequency, amplitude and phase estimation of the signals with white Gaussian noises is proposed in this paper. The proposed algorithm need two sample and computation process, one of which is used for frequency estimation in half cycle of the signal and another of which is used for amplitude and phase estimation in another half cycle. The proposed algorithm spends at most 1 cycle. Frequency estimation is based on numerical differentiation, and amplitude and phase estimation is based on fast Fourier Transform. With an initial sample frequency of 512 × 50 Hz, the signal is sampled and the frequency of the signal with white Gaussian noises is estimated at an error of 0.001% over a range of 1 Hz–1000 kHz. With another sample frequency based on the estimated frequency, the signal is once again sampled and the amplitude of the signal is estimated an error of 0.001% over a range of 1 V–320 V and the phase angle of the signal is estimated an accuracy of 0.001% over a range of 0–360. Using Matlab software, the simulation results of the test example are satisfactory.     

测量与控制系统精度评价的熵方法

通过对传统测控系统误差的分析,建立测量与控制系统精度评价的熵方法分析通用模型。为消除量纲对熵值计算的影响,并使连续变量离散化,提出以分辨率为单位对量程区间进行刻划(即“刻度概率”),进而推导建立静态和动态熵方法不确定度评价指标体系,为测量与控制系统精度的进一步分析提供了依据。     

汽车道路试验自适应脉冲数据采集仪的研制

针对汽车性能道路试验特点研制了脉冲数据采集仪样机。样机中设计了全程采样定时器并根据精度和采样频率要求实时确定每次采样的脉冲组脉冲个数,克服了串行通信速率瓶颈限制和传输延迟对数据处理精度的影响。实车试验表明样机的设计是合理正确的。     

Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis

Fault diagnosis of gearboxes, especially the gears and bearings, is of great importance to the long-term safe operation. An unexpected damage on the gearbox may break the whole transmission line down. It is therefore crucial for engineers and researchers to monitor the health condition of the gearbox in a timely manner to eliminate the impending faults. However, useful fault detection information is often submerged in heavy background noise. Thereby, a new fault detection method for gearboxes using the blind source separation (BSS) and nonlinear feature extraction techniques is presented in this paper. The nonstationary vibration signals were analyzed to reveal the operation state of the gearbox. The kernel independent component analysis (KICA) algorithm was used hereby as the BSS approach for the mixed observation signals of the gearbox vibration to discover the characteristic vibration source associated with the gearbox faults. Then the wavelet packet transform (WPT) and empirical mode decomposition (EMD) nonlinear analysis methods were employed to deal with the nonstationary vibrations to extract the original fault feature vector. Moreover, the locally linear embedding (LLE) algorithm was performed as the nonlinear feature reduction technique to attain distinct features from the feature vector. Lastly, the fuzzy k-nearest neighbor (FKNN) was applied to the fault pattern identification of the gearbox. Two case studies were carried out to evaluate the effectiveness of the proposed diagnostic approach. One is for the gear fault diagnosis, and the other is to diagnose the rolling bearing faults of the gearbox. The nonstationary vibration data was acquired from the gear and rolling bearing fault test-beds, respectively. The experimental test results show that sensitive fault features can be extracted after the KICA processing, and the proposed diagnostic system is effective for the multi-fault diagnosis of the gears and rolling bearings. In addition, the proposed method can achieve higher performance than that without KICA processing with respect to the classification rate.     

Neural networks applied for the identification and fault diagnosis of process valves and actuators

This paper is concerned with the instrumentation and technology of fault detection and isolation (FDI) in process valves and actuators. A classification of faults in process valves and actuators is followed by a brief review of EDI techniques. Artificial neural networks (ANNs) are classified and introduced as an effective way of modelling valves and actuators, which are severely nonlinear components. Experimental results obtained from tests conducted on a double acting, twin piston rack-and-pinion actuator, are presented.     

基于Shannon小波能量熵与FFT的电力系统谐波检测方法研究

利用小波变换的频带划分能力和小波熵对扰动信号检测能力,结合傅立叶变换准确的频域分辨能力,提出一种基于傅立叶变换及小波能量熵联合的电力系统谐波检测改进算法。分析了快速小波变换中小波混叠产生的原因,并提出解决方法。根据电力系统谐波的特点,建立谐波信号数学模型,基于该模型利用Matlab对算法进行仿真验证;利用DSP实验台,对改进算法进行实用化测试。     

一种用于电机铁芯磁性能测试的恒流源

针对电机铁芯磁性能测试中保持磁场强度H的波形为正弦形的问题,设计了一种基于数字信号处理器(DSP)全数字实时控制的逆变恒流源。分析了正弦波逆变电源斩波和逆变控制的一般方法和特点,控制系统前级采用同步Buck电路对母线电压斩波,斩波输出电压经过PI闭环控制实现了其在突加、突减负载时的稳定性。控制系统后级通过对电感电流的PID闭环控制和单极性正弦脉宽调制(SPWM)方式,实现瞬时跟踪给定的交流恒流源控制。实验结果表明逆变恒流源具有很好的静、动态性能和稳定性,输出信号谐波含量少,负载适应能力强,很好地实现了系统设计的要求。     

面向绿色制造的钢铁生产流程优化及其评价

绿色制造是实现可持续发展战略的有效途径.钢铁企业面向新世纪的时代命题之一是走绿色制造的道路.探讨了基于绿色制造的钢铁生产流程功能,对钢铁生产流程进行了优化,并运用资源效率、能源效率和环境效率3个指标对优化程度进行了评价,从而使钢铁生产低成本、高效率、低耗、连续、紧凑及环境友好成为可能.     

矢功率谱与蚁群神经网络结合在机械故障诊断中的应用研究

针对传统功率谱信号源不足以及BP神经网络收敛速度慢且容易陷入局部极小等问题,提出矢功率谱和蚁群神经网络相结合的故障诊断方法,该方法是:提取矢功率谱的8个频段能量特征,并输入到蚁群神经网络分类器进行故障识别,通过实际训练结果和实验结果对比可知,蚁群神经网络能有效地提高收敛速度,网络迭代次数明显改善,故障识别率提高,将蚁群神经网络应用于机械故障诊断是有效的.     

Sensitivity of fundamental mode shape and static deflection for damage identification in cantilever beams

Fundamental mode shape and static deflection are typical features frequently used for identification of damage in beams. Regarding these features, an interesting question, still pending, is which one is most sensitive for use in damage identification. The present study addresses the key sensitivity of these features for damage identification in cantilever beams, wherein these features are extremely similar in configurations. The intrinsic relation between the fundamental mode shape and static deflection is discussed, and in particular, an explicit generic sensitivity rule describing the sensitivity of these features to damage in cantilever beams is proposed. The efficiency of this rule in identifying damage is investigated using Euler-Bernoulli cantilever beams with a crack. The validity of the approach is supported by three-dimensional elastic finite element simulation, incorporating the potential scatter in actual measurements. The results show that the generic sensitivity rule essentially provides a theoretical basis for optimal use of these features for damage identification in cantilever beams.     

Sampling theory and automated simulations for vertical sections,applied to human brain

In recent years, there have been substantial developments in both magnetic resonance imaging techniques and automatic image analysis software. The purpose of this paper is to develop stereological image sampling theory (i.e. unbiased sampling rules) that can be used by image analysts for estimating geometric quantities such as surface area and volume, and to illustrate its implementation. The methods will ideally be applied automatically on segmented, properly sampled 2D images – although convenient manual application is always an option – and they are of wide applicability in many disciplines. In particular, the vertical sections design to estimate surface area is described in detail and applied to estimate the area of the pial surface and of the boundary between cortex and underlying white matter (i.e. subcortical surface area). For completeness, cortical volume and mean cortical thickness are also estimated. The aforementioned surfaces were triangulated in 3D with the aid of FreeSurfer software, which provided accurate surface area measures that served as gold standards. Furthermore, a software was developed to produce digitized trace curves of the triangulated target surfaces automatically from virtual sections. From such traces, a new method (called the ‘lambda method’) is presented to estimate surface area automatically. In addition, with the new software, intersections could be counted automatically between the relevant surface traces and a cycloid test grid for the classical design. This capability, together with the aforementioned gold standard, enabled us to thoroughly check the performance and the variability of the different estimators by Monte Carlo simulations for studying the human brain. In particular, new methods are offered to split the total error variance into the orientations, sectioning and cycloid components. The latter prediction was hitherto unavailable – one is proposed here and checked by way of simulations on a given set of digitized vertical sections with automatically superimposed cycloid grids of three different sizes. Concrete and detailed recommendations are given to implement the methods.     

Experimental identification of turbulent fluid forces applied to fuel assemblies using an uncertain model and fretting-wear estimation

This paper is devoted to the identification of stochastic loads applied to fuel assemblies using an uncertain computational model and experimental measurements of responses. The stochastic loads applied to the structure are induced by a turbulent flow. The structure is made up of a nonlinear complex dynamical system. The experimental responses of the structure are obtained from strain sensors located on the structure. There are several sources of uncertainties in this experimental identification problem of the stochastic loads: uncertainties on the nonlinear dynamical computational model of the structure (fuel assemblies), uncertainties on the parameters of the mathematical model of the stochastic loads themselves and finally, measurements errors. All these sources of uncertainties are identified and taken into account in the identification process of the stochastic loads. Then, the stochastic nonlinear dynamical computational model of fuel assemblies on which the identified stochastic loads are applied yields interesting results concerning the robustness of the estimation of the fretting-wear of the fuel rods.     

A soft and hardware complex for recording and processing of acoustic emission signals and for location and identification of their sources

A soft and hardware complex designed for recording and processing acoustic emission (AE) signals in real time is described. The hardware part of the complex includes a computer with PCI connectors, four-channel parallel-reading analog-to-digital converters (ADCs), preamplifiers, and receiving converters. The software part of the complex is used to record and save in all recording channels the full shape of signals with a preset sampling frequency of up to 20 MHz, locate, identify, and classify AE sources in real time. It differs from analogs in the capability of saving the full shape of AE signals for further analysis and processing, in use of commercially produced ADCs, and in real-time data processing and classifying of AE sources.     

Plastic mechanism and elastic-analytical approaches applied to estimate the strength of an axially compressed-thin-walled channel steel section beam

This paper presents the study of an analytical model to estimate the strength of a thin-walled channel steel section beam subjected to axial-compressive loads. The model is based on two different methods of analysis, which are performed by analysing a plastic failure mechanism and elastic behaviour of the beam. These analytical methods can be used to establish plastic-unloading and elastic-inclining-theoretical load-deflection behaviour of the beam. Meanwhile, the axial-compressive strength of the beam is estimated by directly measuring the value of load at an intersection point between two different curves of the theoretical load-deflection behaviour. The accuracy of using this analytical model is also verified by comparing its estimated data of the strength to the one obtained from a number of tests on 38 specimens of thin-walled channel steel section under the test loads of axial compression. It is clearly shown that deviation of the analytical data from the experimental one is still scattered within acceptable limits of ±20%. A statistical analysis of the scattered data indicates that its mean value is 1.03 with standard deviation of 0.058. This certainly means that the estimated strength, on average, displaces from the actual one by 3% and mostly tends to be conservative.     

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.     

A quadratic polynomial signal model and fuzzy adaptive filter for frequency and parameter estimation of nonstationary power signals

Accurate estimation of amplitude, phase and frequency of a sinusoid in the presence of harmonics/inter harmonics and noise plays an important role in a wide variety of power system applications, like protection, control and state monitoring. With this objective, the paper presents a novel hybrid approach for the accurate estimation of dynamic power system frequency, phasor and in addition to suppressing the effect of harmonics/interharmonics and noise in the voltage and current signals. The algorithm assumes that the current during a fault occurring on a power system consists of a decaying dc component, and time variant fundamental and harmonic phasors. For accurate estimation of fundamental frequency, phasor, decaying dc and ac components in the fault current or voltage signal, the algorithm uses a quadratic polynomial signal model and a fuzzy adaptive ADALINE filter with a modified Gauss–Newton algorithm. Extensive study has been carried out to demonstrate the performance analysis and fast convergence characteristic of the proposed algorithm. The proposed method can also be implemented for accurate estimation of dynamic variations in the amplitude and phase angles of the harmonics and inter harmonics mixed with high noise conditions.