The integrated monitoring system for running parameters of key mining equipment based on condition monitoring technology

An integrated monitoring system for running parameters of key mining equipmenton the basis of condition monitoring technology and modern communication networktechnology was developed.The system consists of a client computer with functions ofsignal acquisition and processing, and a host computer in the central control room.Thesignal acquisition module of the client computer can collect the running parameters fromvarious monitoring terminals in real-time.The DSP high-speed data processing system ofthe main control module can quickly achieve the numerical calculation for the collectedsignal.The signal modulation and signal demodulation are completed by the frequencyshift keying circuit and phase-locked loop frequency circuit, respectively.Finally, the signalis sent to the host computer for logic estimation and diagnostic analysis using the networkcommunication technology, which is helpful for technicians and managers to control therunning state of equipment.

Working Condition Real-Time Monitoring Model of Lithium Ion Batteries Based on Distributed Parameter System and Single Particle Model

Lithium ion batteries are complicated distributed parameter systems that can be described preferably by partial differential equations and a field theory. To reduce the solution difficulty and the calculation amount, if a distributed parameter system is described by ordinary differential equations （ODE） during the analysis and the design of distributed parameter system, the reliability of the system description will be reduced, and the systemic errors will be introduced. Studies on working condition real-time monitoring can improve the security because the rechargeable LIBs are widely used in many electronic systems and electromechanical equipment. Single particle model （SPM） is the simplification of LIB under some approximations, and can estimate the working parameters of a LIB at the faster simulation speed. A LIB modelling algorithm based on PDEs and SPM is proposed to monitor the working condition of LIBs in real time. Although the lithium ion concentration is an unmeasurable distributed parameter in the anode of LIB, the working condition monitoring model can track the real time lithium ion concentration in the anode of LIB, and calculate the residual which is the difference between the ideal data and the measured data. A fault alarm can be triggered when the residual is beyond the preset threshold. A simulation example verifies that the effectiveness and the accuracy of the working condition real-time monitoring model of LIB based on PDEs and SPM.

Model-based and Fuzzy Logic Approaches to Condition Monitoring of Operational Wind Turbines

It is common for wind turbines to be installed in remote locations on land or offshore, leading to difficulties in routine inspection and maintenance. Further, wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance. The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions, and hence predict output signals based on known inputs. A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models, so as to detect changes that could be due to the presence of faults. This paper discusses several techniques for model-based condition monitoring systems: linear models, artificial neural networks, and state dependent parameter "pseudo" transfer functions.The models are identified using supervisory control and data acquisition(SCADA) data acquired from an operational wind firm. It is found that the multiple-input single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently, state dependent parameter models are used to develop adaptive thresholds for critical output signals. In order to provide an early warning of a developing fault, it is necessary to interpret the amount by which the threshold is exceeded, together with the period of time over which this occurs. In this regard, a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.

反求铝合金淬火边界条件的序列LM梯度算法

准确获取边界条件在淬火仿真中至关重要。为此,根据瞬态热传导模型,利用正则化方法建立反求热通量的目标函数,结合序列函数指定法与LM梯度法,通过最小化目标函数推导出序列LM梯度算法,实现末端淬火实验中热通量的迭代计算。其次,通过训练观测点的平均绝对误差的“3-7-3”神经网络预测模型,建立了序列LM梯度算法关键参数的遗传算法优化方法。最后,以换热系数为边界条件建立淬火仿真模型。结果表明:在相同淬火时间内,一维序列LM梯度算法、一点法和两点法,在观测点的仿真温度平均相对误差分别为5.6%、5.9%、12.6%,17.1%、15.4%、15.3%和17.7%、16.0%、15.6%。因此,与一点法、两点法相比,序列LM梯度算法具有更高的精度。

机械硬盘加速寿命试验及状态监测技术研究

机械硬盘的健康状态监测和诊断对于企业的安全可靠运行和节约运维成本起着至关重要的作用。首先,分析了硬盘的基本组成和不同环境应力下的故障模式和机理;其次,根据实际运行环境制定了加速寿命试验,并在试验过程中对硬盘的SMART数据进行采集和存储;最后,经过对比和分析发现了该型号硬盘的敏感SMART参数,并发现寻道出错率具有明显的退化趋势,可用于故障预警和预测。

炸药压制成型机参数采集系统开发

为进一步实现质量控制与安全生产,研制一套炸药压机参数实时采集系统。基于压机控制系统和传感器,利用高级语言编程从压制过程实时读取相关参数并在人机界面显示,操作人员可实时观测压制过程的工作参数,以便自适应调整参数控制药柱质量、监测压机运行状态并做应急预案。结果表明:该压机参数采集系统性能稳定、操作简单方便,满足参数采集、存储、显示功能。

跨风电机组参数微调智能迁移状态监测方法

针对单台风电机组状态监测模型直接用于风电场其它风电机组状态监测准确率较低的问题,提出跨风电机组参数微调智能迁移状态监测方法。计算风电场风电机组之间的相关性,筛选代表性正常风电机组。利用代表性风电机组海量正常监控与数据采集系统数据构建输入特征,建立基于双向长短时记忆神经网络的风电机组状态监测模型。利用其它风电机组大量历史正常数据对状态监测模型进行参数微调,融入待监测风电机组个性化特征,实现跨风电机组的智能迁移状态监测。利用合作企业某风电场的真实风电机组数据,验证所提出的方法,结果表明,参数微调迁移状态监测方法相比单一风电机组状态监测模型直接跨风电机组监测,可以提前2个月监测到早期故障。与其它深度学习方法相比,所提出的方法具有更高的预测精度。

Optimization of image capturing method of wear particles for condition diagnosis of machine parts

Wear particles are inevitably occurred from moving parts, such as a piston-cylinder made from steel or hybrid materials. And a durability of these parts must be evaluated. The wear particle analysis has been known as a very effective method to foreknow and decide a moving situation and a damage of machine parts by using the digital computer image processing. But it is not laid down to calculate shape parameters of wear particle and wear volume. In order to apply image processing method in a durability evaluation of machine parts, it needs to verify the reliability of the calculated data by the image processing and to lay down the number of images and the amount of wear particles in one image. In this work, the lubricated friction experiment was carried out in order to establish the optimum image capture with the 1045 specimen under experiment condition. The wear particle data were calculated differently according to the number of image and the amount of wear particle in one image. The results show that capturing conditions need to be more than 140 wear particles in one image and over 40 images for the reliable data. Thus, the capturing method of wear particles images was optimized for condition diagnosis of machine moving parts.

Identification of Health and Welfare Parameters for Rabbit Production and Definition of an Evaluation Score

The knowledge on rabbit welfare may be improved by the use of correct tools for monitoring the different aspects of rabbit industrial farming. Therefore, the aim of this study was to define parameters related to health and welfare of animals in industrial farms with intensive husbandry. Health, management, environmental and productive parameters were firstly characterized and then a protocol to assess welfare of rabbits was define. The research was conducted on 8 industrial farms from 2004 to 2007 and around 30 inspections were done in each farm. At each visit, the health conditions were established by： （1） necropsy on animals of different productive category; （2） specific laboratory investigations based on the lesions observed; （3） checking the presence of parasites in environmental faecal samples; （4） bacteriological examination of vaginal, nasal and rectal swabs of rabbit of different age. The immune conditions and the efficacy of vaccinations were measured by determining anti-Myxomatosis and anti-Rabbit Haemorrhagic Disease antibodies using competitive ELISAs, and anti-Encephalitozoon cunicoli antibodies by immunocarbonassay. The environmental conditions were evaluated by measuring air temperature, relative humidity, ammonia concentration and bacterial/fungal count. Finally the productive parameters were also recorded and elaborated. All the entered values were then utilized for defining a score system to establish health and welfare conditions.

计及热电耦合效应的SiC MOSFET阈值电压精确监测方法

在研究SiC MOSFET的阈值电压、体二极管电压、漏-源极通态电阻温度依赖性的基础上,分析偏压温度不稳定性(BTI)引起的V_(TH)的漂移规律,并探究其对温敏电参数(TSEPs)的影响规律。另外,在充分考虑温度和BTI对V_(TH)共同作用的影响下,提出在小电流注入时使用体效应下的体二极管电压监测SiC MOSFET阈值电压的方法。该方法可在不同的结温(T_(j))下监测阈值电压,为校正其他TSEPs测量结温的准确性提供帮助。理论和实验结果证明了该方法的可行性。

基于声发射技术的在用管道腐蚀检测试验

针对在用管道腐蚀检测缺乏科学有效方法的问题,提出利用声发射技术监测X60管线钢在10%的FeCl_(3)·6H_(2)O溶液中的声发射信号特征参量和波形变化。试验结果表明,声发射技术可根据信号类型、频谱特征、能量比例,检测到在用管道初期腐蚀、加速腐蚀、平稳腐蚀3个阶段,可为在用输气管道腐蚀状态的安全性评估提供理论依据。

基于人工智能的造纸机械运行状态实时监测方法

纸张是人类生产与生活的必需品,故造纸行业是国家重点发展行业之一。新兴技术的应用,使得造纸机械的复杂程度逐渐提升,故障事件发生频率也随之上涨,威胁着造纸机械的正常运行,提出基于人工智能的造纸机械运行状态实时监测方法研究。深入分析造纸机械运行特征与故障规律,以此为基础,遵循全面性、可行性、科学性等原则,选取运行状态实时监测指标,引入人工智能技术——优度评价法构建造纸机械运行状态实时监测模型,计算监测指标权重及其关联度数值,获取最终的综合优度数值,对照运行状态等级判定规则表,即可获得造纸机械运行状态的实时监测结果。实验数据显示:应用提出方法获得的监测指标数据拟合曲线、运行状态等级判定结果与实际情况更加贴合,充分证实了提出方法应用性能更佳。

自动化技术在电力系统运行中的应用

阐述在电力系统中引入电气控制自动化技术,以此提高电力系统的运行控制水平,通过自动化状态监测获取系统各项运行参数,通过对状态参数的分析处理,及时发现系统内潜在的故障隐患和问题,从而为下一步维修和改造决策,提供可靠的参考依据。

水电厂水泵运行状态监测技术与应用

水泵对水电厂安全稳定运行具有重要的作用,水泵故障可能引起机组冷却水中断导致机组非计划停用、局部水淹厂房的事故发生,发现水泵运行中存在的缺陷,利用现代电力电子技术,设计、制作监测装置,对水泵的运行状态进行监测、分析,判断水泵存在的问题,及时采取措施、消除故障,从而保证水电厂的安全稳定运行。

趋势故障识别在水电机组运检中的应用试验

该文针对水轮发电机组中的趋势故障问题,结合现阶段在线监测系统在水力发电机组中的应用情况进行研究。为了更及时有效地识别趋势故障,针对机组运行过程中各状态监测量出现的趋势异常现象进行总结并分类,同时提取趋势异常故障识别的有效特征参数,通过自动计算同一工况下的这些特征参数值,进而对不同类型的趋势异常现象做出评价,实现对机组运行健康状态的早期预警和诊断。此外,还列举了不同类型的趋势异常现象及特征参数在其故障识别中的应用案例,验证了趋势故障识别方法在水电机组运检中的实际应用价值。

几个机械状态监测特征量的特性研究

本文深入分析了机械状态监测中常用的几个特征量 :峭度系数、重心频率、均方频率和频率方差的特性 ,指出峭度系数虽然对信号中的突变成分很敏感 ,但当突变发生的频度增加时 ,峭度系数值反而下降 ;而重心频率、均方频率和频率方差近似由自相关系数决定 ,也即它们的信息都包含在自相关系数中 。

大功率并网风电机组状态监测与故障诊断研究综述

状态监测与故障诊断技术是降低大功率并网风电机组的故障率和其运维费用的有效手段之一。对风电机组状态评估和故障预测进行综述。首先,在分析国内外风电机组故障统计情况的基础上,提出状态监测需要关注的风电机组关键部件;其次,综述风电机组整机综合状态评估和故障预测研究现状;然后,重点评述和分析风电机组关键部件的状态监测与故障诊断方法;最后,提出大功率并网风电机组状态监测与故障诊断的研究要点及趋势。

输电线路在线监测装置研制及其通信组网应用

为了提高输电线路的运行稳定性,通过对输电线路需要监测的状态参量进行分析,并基于无线传感网络技术,提出了输电线路一体化智能监测装置架构,研制出输电线路一体化智能监测装置。此装置具有接口丰富、通信方式多样化、集成度高的优点。输电线路一体化智能监测装置在云南省进行了通信组网应用,其结果表明:装置在3 000 m范围内的数据平均传输速率为12.1 Mb/s,且数据传输速率均不低于10.8 Mb/s。此装置为实现分散监测主机间的相互关联、监测数据共享及信息整合,形成光纤、北斗、GPRS及无线通信方式组合的通信网络,为输电线路实时数据及命令的传送提供了有效的通道及途径,提高了输电线路的运行稳定性。

DC-link电容器在线状态监测方法综述

直流支撑(DC-link)电容器是电力电子变换器的关键器件之一,它的可靠性会影响整个系统的可靠性。状态监测是估计电容器健康状态的一种有效手段,综合分析了电容器的健康状态参数和性能状态参数,针对近年来电容器的在线状态监测研究进行分类,将其分为了传感器法、注入法、电路模型法和智能监测法;介绍每种方法的原理和手段,对比分析各种方法的优缺点,其次,针对研究成果的监测对象和监测参数进行数据统计分析;最后提出电容器状态监测的后续研究方向。

变温度下Boost变换器健康状态评估方法

基于特征参数估计的关键元器件健康状态评估是实现电路级故障预测与健康管理的关键技术。然而,工作温度的变化会导致元器件特征参数估计值与失效标准(25℃条件下)不统一,致使状态评估结果不准确。针对该关键问题,以非理想Boost变换器为例,首先,采用改进粒子群优化算法辨识关键元器件特征参数值;然后,分别建立电解电容、功率MOSFET器件特征参数与工作温度的数学模型,并定义器件特征参数归一化因子(NF)及健康指标(HI);最后,提出基于无迹粒子滤波(UPF)动态更新模型参数的方法估计归一化后的特征参数值,从而实现变温度下关键元器件健康状态评估。搭建硬件实验平台,通过研究结果表明参数辨识误差小于5%,健康评估准确度可达90%以上,验证了所提方法的有效性。