基于特征贡献率的机械故障分类方法

为了使7075铝合金薄壁件表面喷丸加工应力得到松弛和均化，运用ANSYS建立平台式振动时效有限元模型，通过振动模态和谐响应分析，获得最佳激振频率和试样装夹位置。在此基础上，将薄壁框架件置于平台上进行振动时效处理，以验证仿真分析结果，评价其对试样表面应力作用效果。结果表明：在亚共振频率为112 Hz时，试样在平台零振幅和最大弯曲位置处分别对称布置，时效后表面均形成应力松弛，松弛率分别为29.5%和33.3%，不均匀波动分别为20.3%和20.7%，而沿振动方向松弛程度不均匀，说明激振力造成了材料表面组织屈服，且屈服程度与应力强度有关。提出再时效-位置交叠方法，使应力松弛不均匀度分别下降到9.9%和15.9%，应力均化得到改善，该研究工作为薄壁类零件表面应力均匀化提供了参考。     

掘进巷道快速过断层施工技术应用

基于四候煤矿3108回风顺槽过F3断层期间,顶板出现严重破碎、下沉现象,提出了俯斜台阶法施工工艺,并对断层破碎带顶板采取MF-2型化学材料注浆加固以及"钢筋锚索网+U29梯形棚"联合支护措施。支护效果检验结果表明,联合支护有效控制了断层破碎区顶板下沉、破碎现象,保证了巷道顶板稳定性。     

Flower-shaped ZnO nanomaterials for low-temperature operations in NOX gas sensors

In this study, we synthesized nanostructured zinc oxide (ZnO) by using various concentrations (0–0.05 M) of cetyltrimethylammonium bromide (CTAB) as a surfactant to optimize its morphology for gas sensor applications. The optimization process was used to elucidate the morphology effects (rod-shaped and flower-shaped morphologies). The morphologies were investigated through scanning electron microscopy, in which the assembly of nanorods leading to a spherical microstructure with a CTAB concentration of 0.005 M was observed. Brunauer–Emmett–Teller isotherm measurements revealed a surface area of 7.928 g/m² for the flower-like morphology, which was relatively higher than those of other CTAB-assisted morphologies. Such morphological features were expected to contribute toward high-performance gas-sensing. The effect of morphology variation on the resistance of ZnO microstructures was used for gas measurements. Among the varied morphologies, a sample with a spherical flower-shaped morphology exhibited a very high response at low temperatures (~29 at 25 °C) toward NO_X gas (0.75 ppm) and a high selectivity toward NO_x among ammonia (NH₃), toluene (C₆H₅CH₃), carbon monoxide (CO), acetone (CH₃COCH₃), and ethanol (C₂H₅OH). Raman and photoluminescence spectroscopy analyses unraveled the presence of a high density of oxygen vacancies in the sample, thereby suggesting a close link between the defective nature of the sample and the high response of the flower-like ZnO at low temperatures.     

基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。     

Pressure sensor matrix for indirect measurement of grip and push forces exerted on a handle

To date, a large part of workers is exposed to vibrations (23% in Europe) which can negatively impact on their health. This work discusses the importance of measuring grip and push forces in the context of hand-arm vibration tests, bearing in mind the state-of-art of current standards. It proposes a method for indirect measurement of coupling forces using a matrix of polymeric pressure capacitive sensors and discusses the model used for defining these quantities. The matrix of pressure sensors is wrapped around the tool handle and the acting forces, exchanged with the handle, are derived from the pressure values measured by the matrix. Calibration is presented and the effect of curvature is discussed. The work continues with the experimental validation of the model proposed for push force measurements carried out through lifting tests using known masses with a cylindrical handle. An experimental correction coefficient is defined in correlation to the type of grip. The method for measuring the push force, thus corrected, is assessed by means of push force tests on an instrumented handle. Finally the experimental data are analysed in order to assess the uncertainty of the proposed method for measuring the push force, highlighting the contribution of the different sources of uncertainty. The proposed measurement method allows to measure the push and the grip force (known influencing quantities for the measurement of the hand-arm vibration) during tool test and without modifying the handles.     

Input variable scaling for statistical modeling

Input variable scaling is one of the most important steps in statistical modeling. However, it has not been actively investigated, and autoscaling is mostly used. This paper proposes two input variable scaling methods for improving the accuracy of soft sensors. One method statistically derives the input variable scaling factors; the other one uses spectroscopic data of a material whose content is estimated by the soft sensor. The proposed methods can determine the scales of the input variables based on their importance in output estimation. Thus, it can reduce the negative effects of input variables which are not related to an output variable. The effectiveness of the proposed methods was confirmed through a numerical example and industrial applications to a pharmaceutical and a distillation processes. In the industrial applications, the proposed methods improved the estimation accuracy by up to 63% compared to conventional methods such as autoscaling with input variable selection.     

Maximizing lifetime in wireless sensor networks with multiple sensor families

Wireless sensor networks are generally composed of a large number of hardware devices of the same type, deployed over a region of interest in order to perform a monitoring activity on a set of target points. Nowadays, several different types of sensor devices exist, which are able to monitor different aspects of the region of interest (including sound, vibrations, proximity, chemical contaminants, among others) and may be deployed together in a heterogeneous network. In this work, we face the problem of maximizing the amount of time during which such a network can remain operational, while maintaining at all times a minimum coverage guarantee for all the different sensor types. Some global regularity conditions in order to guarantee a fair level of coverage for each sensor type to each target are also taken into account in a second variant of the proposed problem. For both problem variants we developed an exact approach, which is based on a column generation algorithm whose subproblem is either solved heuristically by means of a genetic algorithm or optimally by an appropriate ILP formulation. In our computational tests the proposed genetic algorithm is shown to be able to dramatically speed up the procedure, enabling the resolution of large-scale instances within reasonable computational times.     

一种矿用二氧化氮传感器的检测方法

矿用二氧化氮传感器是煤矿常用的气体检测仪表，针对煤矿需求开发的矿用二氧化氮传感器，需要满足煤矿矿用产品要求。文章提出一种矿用二氧化氮传感器检测方法，说明矿用二氧化氮传感器需要满足的技术参数，供实验室检测人员借鉴，也为设计制造矿用二氧化氮传感器提供了参考。     

Influences of magnetic fields on current–voltage characteristics of gold-DNA-gold structure with variable gaps

Achieving highly sensitive magnetic sensors by means of Metal-DNA-Metal (MDM) structure is a key issue. DNA, being a genetic information carrier in living cells reveals tunable semiconducting response in the presence of external electric and magnetic fields, which is promising for molecular electronics. The influence of magnetic fields up to 1200 mT on the current–voltage (I–V) behavior of Gold-DNA-Gold (GDG) structure having variable gap sizes from 20–50 μm are reported in this work. These structures were fabricated using UV lithography, DC magnetron sputtering and thermal evaporation techniques. DNA strands were extracted from Boesenbergia rotunda plant via standard protocol. The acquired I–V characteristics display the semiconducting diode nature of DNA in GDG structures. The potential barrier for all the structures exhibit an increasing trend with the increase of externally imposed magnetic field irrespective of variable gap sizes. Furthermore, the potential barrier in GDG junction at higher magnetic field strengths (>1000 mT) is found to be considerably enhanced. This enhancement in the junction barrier height at elevated magnetic fields is attributed to the reduction of carrier mobility and augmentation of resistance. The achieved admirable features of magnetic sensitivity suggest the viability of using these GDG sandwiches as a prospective magnetic sensor.     

Fault estimation and accommodation for networked control systems with nonuniform sampling periods

This paper deals with the problem of fault estimation and accommodation for a class of networked control systems with nonuniform uncertain sampling periods. Firstly, the reason why the adaptive fault diagnosis observer cannot be applied to networked control systems is analyzed. Based on this analysis, a novel robust fault estimation observer is constructed to estimate both continuous‐time fault and system states by using nonuniformly discrete‐time sampled outputs. Furthermore, using the obtained states and fault information, a nonuniformly sampled‐data fault tolerant control law is designed to preserve the stability of the closed‐loop system. The proposed scheme can not only guarantee the impact of continuous‐time uncertainties and discrete‐time sampled estimation errors on the faulty system to satisfy a H _∞ performance index but also repress the negative effect of the unknown intersample behavior of continuous‐time fault by use of an inequality technique. Finally, simulation results are included to demonstrate the feasibility of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.