Proposal for an Indexical Method of Evaluating Risk of Hand Tool Operators’ Exposure to Mechanical Vibrations

The article presents proposals for an indexical evaluation of exposure of hand tool operators to vibrations. The presented indices have been developed on the basis of the results of laboratory tests. The examinations studied the effect of pressure force and grip force exerted by an operator on a hand tool as well as the amplitudes and frequencies of an exciting signal on vibrations transmitted in the hand-tool handle system.     

Influence of the milling tool setup on occupational safety in furniture making

One of the most serious causes of accidents in furniture making is kickback of machined material. The aim of this study was to determine the influence of the milling tool setup on hazards associated with kickback in furniture making. The speed of kickback was accepted as the measure of these hazards. The experiment involved controlled changes in milling tool setup, projection of cutting knives over the body of the milling tool, number of cutting knives and clearance angle. Multifactor analysis of variance was applied to the results of individual experiments, showing statistically significant factors and their interactions. Inspection and analysis of traces left by the cutting knives of the tools on the test pieces made of wood materials supported inferential statistics. The obtained results verified some common opinions and ideas on the impact of the milling tool setup on the hazards resulting from kickback in furniture making.     

Examining the effectiveness of anti-vibration gloves with a neural network

Whether anti-vibration gloves are effective in protecting against vibrations depends not only on the materials they are made of, but also on the parameters of the source of vibration. Depending on those parameters, the effectiveness of the same means of protection may be radically different. This article presents a methodology of using a neural network to test anti-vibration gloves. A network can map gloves in various conditions, i.e., for vibrations of various amplitudes and spectra, and for various forces exerted by the worker on a tool. Real, measured vibration signals produced by different tools were used in training a neural network. The results presented in this article prove that real properties of gloves are accurately represented by their models developed as a result of training a neural network.     

煤矿生产系统干扰事件的离散小波变换检测

小波变换常用于数字信号传输的噪声检测与处理,为了检测煤矿生产系统中的突发事件,将其模拟为数字信号传输中的干扰,构建系统稳态方程和对应的扰动方程,根据离散小波变换原理,将煤矿生产系统中的监测数据分解为近似部分和细节部分,再进行离散变换。采用多尺度边缘检测法,由光滑后的一阶和二阶导数检测出信号的模极大值点即为瞬态突变点。将A矿记录到的瓦斯浓度监控数据进行可行性分析后,在MATLAB的wavelet工具中对该瓦斯浓度监控数据作离散小波变换,找到四个瓦斯浓度瞬态突变点,那么这四个时刻发生了煤与瓦斯突出事件。结果表明该方法简单、实用性强。据此从计算机实时监控和现场技术防突方面提出应对煤矿干扰事件的措施。     

柴油发动机隔振方面的仿真研究

本文主要研究模糊控制技术在柴油发动机隔振系统中的应用，将模糊控制技术引入柴油发动机隔振主动控制系统，针对柴油发动机隔振模拟实验台架的振动特点，建立了控制系统数学模型，进行了柴油发动机隔振模拟实验台架垂直方向的仿真研究。     

Experimental research of the dynamic behavior of the natural gas suspension pipeline aerial crossing during pigging process

Suspension pipeline aerial crossings (SPAC) are mainly used to carry pipeline segments over large obstacles that are not suitable for trenchless technologies. It may suffer great vibrations and displacements during the pigging process due to the dynamic pigging loads and insufficient constraints from cables. Based on similarity criteria, an experimental scale model based on the Nujiang natural gas SPAC was designed and established. The vibration and displacement of the scale model were measured under different pigging velocities and liquid deposit volumes. The experimental results reveal that the natural gas SPAC vibrates during the pigging process, but the displacement versus time forms a U curve instead of a sinusoidal like curve. The extremum of the displacement is positively related to the pigging velocity and liquid deposit volume. In particular, the extremum increases by 0.37% with a 1% increase in the pigging velocity, while the displacement increases by 0.62% with a 1% increase in the liquid deposit volume. Under certain pigging conditions, or with damaged constraint components, the displacement extremum could be unacceptable. A low pigging velocity and a short time interval between two pigging operations are suggested to guarantee the safety of the natural gas SPACs. Besides, based on the experimental data, an empirical formula is developed to obtain the SPAC displacement-time curve with consideration of the span length, the pipeline diameter, the pigging velocity, the length and holdup of the liquid column. This formula can help to determine a suspicious displacement overrun, and to develop a data basis for choosing a safe pigging scheme of SPACs. This research provides insightful information to understand the mechanism of the SPAC's dynamic response as well as a practical tool to calculate the SPAC's displacement during the pigging process.     

Detection of small leakage from long transportation pipeline with complex noise

In the long distance pipeline remote monitoring system, small leak detection becomes an important issue. Weak singularities in small leak signals are usually difficult to detect precisely under complicated noise background, which may cause false alarm or miss alarm. The advantage of applying the harmonic wavelet method is explored in this paper. Pipeline small leak sensitive characteristics are recognized and the negative pressure wave inflexions are extracted by harmonic wavelet analysis, expressed in terms of harmonic wavelet time-frequency mesh map, time-frequency contour map, and time-frequency profile plot. This paper also presents a comparative study of both Daubechies wavelet and harmonic wavelet analysis when applied to pipeline small leak detection under complicated background noises. Results of simulating test and field experiment show that it is possible to distinguish weak non-stationarities from complicated noises by harmonic wavelet analysis in pipeline small leak detection system. The comparison clearly illustrates that harmonic wavelet based pipeline small leakage detection method is significantly more accurate than other wavelets analysis such as Daubechies wavelet. This work provides a reliable and safe guarantee for oil and gas long distance transportation, reducing petroleum product losses and protecting surrounding environment.     

安全监督HSE能力评价体系研究与应用

为了有效评价安全监督HSE能力,本文以安全监督HSE能力是一个复杂系统为出发点,在对安全监督HSE能力影响因素分析和安全监督深入访谈的基础上,初拟指标后对指标体系进行问卷咨询,确定了监督能力、监督业绩和监督效果3个维度的评价体系,包括3个一级指标,14个二级指标,29个三级指标,并应用层次分析法确定了各级指标的隶属度和权重值。最后通过应用实践验证,形成的安全监督HSE能力评价体系,可为安全监督HSE能力的培训、派驻及考核晋级提供参考依据。     

小波分析在语音识别系统中的研究与应用

在语音识别系统的不同处理阶段,大部分可以使用小波分析技术进行实现,并且这种实现方法比传统的方法具有更好的效果.本文重点探讨了在端点检测、噪声去除以及参数提取过程中使用小波分析技术的原理及方法,并提出现阶段所存在的问题及解决方案.     

油锯手把的手传振动与主动防振动问题探讨

通过对油锯手把的手传振动状况分析发现，国产油锯产品质量仍有待加强，提出了油锯手把的主动防振动保护措施及建议。     

基于小波神经网络的瓦斯涌出量预测研究

准确地预测瓦斯涌出量对于指导矿井设计和安全生产有重要意义,而瓦斯涌出量是一个与自然因素及开采技术等多因素有关的非线性建模问题。鉴于传统神经网络方法解决非线性问题收敛速度慢,易陷入局部最优解的缺陷,笔者提出一种既充分利用小波变换的时频局部化性质,又能结合神经网络的自学习能力的小波神经网络预测瓦斯涌出量的方法,并建立了预测模型。在此基础上,采用Delphi语言,设计了小波/BP神经网络仿真器。通过实例分析表明该方法较传统神经网络收敛迅速,预测精度高。     

基于小波神经网络的煤层底板突水非线性预测方法研究

针对煤层底板突水系统为一非线性动力学系统的特性,并在考察目前煤层底板突水预测方法的基础上,给出利用小波神经网络对煤层底板突水进行预测的可行性和优越性;阐述了小波神经网络的基本原理;提出和分析了基于小波神经网络的煤层底板突水预测模型及算法;并通过实例证明,应用小波神经网络解决煤层底板突水预测的可行性和优越性。研究及实践表明:小波神经网络的预测精度更高、更准确。     

基于灰色关联度的集对分析方法在高层建筑火灾危险性评价中的应用

为了对某钢管厂高层建筑进行火灾风险评估,基于高层建筑的防火、灭火设计要求,从防火系统、灭火系统、安全疏散和消防安全管理四个方面构建了高层建筑火灾危险性指标评价体系,并运用层次分析法确定了各火灾危险性影响因子的权重。根据集对分析的联系度模型对高层建筑进行火灾危险性评估,确定其安全等级。结合灰色关联度理论对差异度与同一度、对立度两组数据进行计算比较,选取绝对关联度大的值作为差异度系数i的值,从而可以确定各组联系度的值。根据联系度最大值所属的安全等级,确定了高层建筑的火灾危险等级为一般安全,结果与实际情况相符。     

基于检验测试策略的PFD模型建立与分析*

为制定合理检验测试策略,提高安全仪表系统（SIS）在低要求运行模式下的安全性,提出要求平均失效概率(PFDavg)通用计算模型,引入检验测试分布因子和共因失效修正因子,表征部分和完全检验测试对SIS安全性的影响。结果表明:该模型适用于所有同构koon架构系统,可应用于周期性、非周期性部分检验测试及共因失效影响较大的场景,可以为企业制定检验测试策略提供理论依据。     

小波熵去噪在微差爆破延时精确识别中的应用

在小波熵去噪理论的基础上,对实际隧道爆破工程采集到的爆破振动信号进行了小波熵方法去噪。利用db8小波对去噪后的信号在尺度a=16进行了连续小波变换得到其模极大值,准确识别了隧道多段别微差爆破实际延期时间间隔,并验证了小波熵方法去噪的可靠性。结果表明,小波熵去噪方法能够有效滤除和抑制爆破振动非线性信号所夹杂的高频噪声分量,并且很好地保留了爆破振动信号的突变细节。对滤波后的信号进行模极大值变换,信号局部奇异点的辨识更准确,可以精确识别隧道微差爆破延时间隔。     

基于G1-DEMATEL法的通用航空维修安全影响因素研究

为定量分析影响通用航空维修安全的风险因素,进而有针对性地制定重点安全监管内容,根据 “人-机-环-管”模型,建立了通航维修安全风险因素指标体系。通过结合G1法和决策试验与评价实验法（DEMATEL）,构建基于G1-DEMATEL法的通航维修安全风险因素分析模型。利用G1法计算各个因素指标的权值,再以各指标权值之比建立直接影响矩阵;采用DEMATEL法从中心度和原因度两个视角对影响通用航空维修安全的各个风险因素进行分析,以确定因素指标间的影响程度及其作用关系。G1-DEMATEL法为定量分析安全风险因素,并确定关键因素提供可能性。同时,为局方和通用航空公司制定通航维修安全监管内容提供有价值的参考。     

基于循环经济的煤矿环境评价指标体系

基于循环经济理论和煤矿开采中环境系统的演化规律.从资源循环与资源损失正反两个方面构建了煤矿区环境评估指标体系.指标体系分为目标层、指标类别层和单项指标层3个层次,单项指标层是具体舒化指标.由它直接计算指标类别层的值,进而计算目标层的评估结果.将此指标体系运用到重庆市某县煤矿,对该县近年来煤矿资源开发中环境可持续能力进行了计算和分析,评价结果与实际情况相符,揭示了该县煤矿开发中环境可持续能力提高的关键因素,进一步探讨了煤矿开发中环境能力提高和循环经济理念的实施建议.     

Predicting of blasting vibrations in Sarcheshmeh copper mine by neural network

Artificial Neural Networks (ANN) have proven to be an effective tool for solving complex engineering problems requiring estimation, pattern recognition, and classification of variables. Ground vibration caused by blasting imposes damages and financial penalties and as such must be predicted accurately. In this study, the potentials of ANN are investigated in prediction of ground vibrations due to blasting in open pit mines. Real vibration data is recorded using PDAS 100 seismometers, and used as input data for ANN. Using back propagation algorithm and performance function, root mean square error (RMSE) the network, containing four hidden layers and 23 data sets, was trained. Six sets of data were used to make sure that correct training had been carried out. This produced the coefficient correlation of 0.99355.     

基于DEMATEL的机械生产人员行为形成关键因子分析

针对机械生产人员安全行为形成受多种因素交互影响的问题,提出了一种基于模糊数学和DEMATEL法耦合的机械生产人员行为形成关键因子量化分析方法。首先,运用行为形成因子分析方法确定机械生产人员作业行为影响因素,构建了包含5个因子大类和24个因子因素的机械生产人员行为形成因子体系,然后运用三角模糊数表征因素间的关联程度,并通过问卷调查获取数据,从而借助DEMATEL法构建机械生产人员行为形成关键因子分析模型。最后,通过一个算例,说明所提出分析模型的运用过程。结果表明,所建立的方法可以定量确定机械生产人员行为形成关键因子。     

Mathematical Modelling of Muscle Effect on the Kinematics of the Head-Neck Complex in a Frontal Car Collision: A Parameter Study

A 2-dimensional multibody model of the head-neck complex with muscle elements was developed to estimate the influence of muscles on the kinematics of the head-neck complex in a frontal car collision. With this model the authors evaluated how strongly the calculated influence of muscles depends on 3 important factors: (a) impact severity, (b) reflex time, and (c) parameters that determine characteristics of different components of the muscle model. When muscles were triggered at the beginning of impact, the maximum angle of the head flexion was decreased by the muscles by 40% in a frontal collision with an acceleration of 15g. The influence of muscles was significant for reflex times lower than 60 (80) ms. The calculated influence of muscles was not sensitive to most parameters of the muscle model.