Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

Japanese candlestick charts for diabetes

Continuous glucose monitoring(CGM)is a popular technology among the diabetic population,especially in patients with type 1 diabetes and those with type 2 diabetes treated with insulin.The American Diabetes Association recommends standardization of CGM reports with visual cues,such as the ambulatory glucose profile.Nevertheless,interpreting this report requires training and time for CGM to be cost-efficient.In this work it has been proposed to incorporate Japanese candlestick charts in glucose monitoring.These graphs are used in price analysis in financial markets and are easier to view.Each candle provides extra information to make prudent decisions since it reports the opening,maximum,minimum and closing glucose levels of the chosen time frame,usually the daily one.The Japanese candlestick chart is an interesting tool to be considered in glucose control.This graphic representation allows identification of glucose trends easily through the colors of the candles and maximum and minimum glucose values.

EWMA control chart based on its first hitting time and coronavirus alert levels for monitoring symmetric COVID-19 cases

Objective:To define the alert levels for the total number of COVID-19 cases derived by using quantile functions to monitor COVID-19 outbreaks via an exponentially weighted moving-average(EWMA)control chart based on the first hitting time of the total number of COVID-19 cases following a symmetric logistic growth curve.Methods:The cumulative distribution function of the time for the total number of COVID-19 cases was used to construct a quantile function for classifying COVID-19 alert levels.The EWMA control chart control limits for monitoring a COVID-19 outbreak were formulated by applying the delta method and the sample mean and variance method.Samples were selected from countries and region including Thailand,Singapore,Vietnam,and Hong Kong to generate the total number of COVID-19 cases from February 15,2020 to December 16,2020,all of which followed symmetric patterns.A comparison of the two methods was made by applying them to a EWMA control chart based on the first hitting time for monitoring the COVID-19 outbreak in the sampled countries and region.Results:The optimal first hitting times for the EWMA control chart for monitoring COVID-19 outbreaks in Thailand,Singapore,Vietnam,and Hong Kong were approximately 280,208,286,and 298 days,respectively.Conclusions:The findings show that the sample mean and variance method can detect the first hitting time better than the delta method.Moreover,the COVID-19 alert levels can be defined into four stages for monitoring COVID-19 situation,which help the authorities to enact policies that monitor,control,and protect the population from a COVID-19 outbreak.

Some Group Runs Based Multivariate Control Charts for Monitoring the Process Mean Vector

In this article, we propose two control charts namely, the “Multivariate Group Runs’ (MV-GR-M)” and the “Multivariate Modified Group Runs’ (MV-MGR-M)” control charts, based on the multivariate normal processes, for monitoring the process mean vector. Methods to obtain the design parameters and operations of these control charts are discussed. Performances of the proposed charts are compared with some existing control charts. It is verified that, the proposed charts give a significant reduction in the out-of-control “Average Time to Signal” (ATS) in the zero state, as well in the steady state compared to the Hotelling’s T2 and the synthetic T2 control charts.

New Method for Multivariate Statistical Process Monitoring

A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis （FDA） is used to derive a feature discriminant direction （FDD） between each normal and fault operations,and each FDD thus decided constructs the feature space of each fault operation.Individuals control charts （XmR charts） are used to monitor multivariate processes using the process data projected onto feature spaces.Upper control limit （UCL） and lower control limit （LCL） on each feature space from normal process operation are calculated for XmR charts,and are used to distinguish fault from normal.A variation trend on an XmR chart reveals the type of relevant fault operation.Applications to Tennessee Eastman simulation processes show that this proposed method can result in better monitoring performance than principal component analysis （PCA）-based methods and can better identify step type faults on XmR charts.

Enhancing the Effectiveness of Trimethylchlorosilane Purification Process Monitoring with Variational Autoencoder

In modern industry,process monitoring plays a significant role in improving the quality of process conduct.With the higher dimensional of the industrial data,the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database.Nevertheless,these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices,especially the T^(2) on them.Variational AutoEncoders(VAE),an unsupervised deep learning algorithm using the hierarchy study method,has the ability to make the latent variables follow the Gaussian distribution.The partial least squares(PLS)are used to obtain the information between the dependent variables and independent variables.In this paper,we will integrate these two methods and make a comparison with other methods.The superiority of this proposed method will be verified by the simulation and the Trimethylchlorosilane purification process in terms of the multivariate control charts.

Gas concentration pre-warning system based on fuzzy structured element

In order to accurately test gas concentration and effectively pre-waming when the gas concentration over-limited on work face, used the high-performance and low prices SCM and the low-cost and high transfer efficiency bluetooth technology to forecast the gas concentration in real time. The data tested by SCM, then got the corresponding mathematical model of the data. Put forward the idea of using fuzzy structured element theory to dynamic forecast the gas concentration, analyzed the features in abnormal-effusing on work face and judge whether there was the possibility of abnormal gas-effusion. Simulation results show that mathematical model of this system about gas concentration is correct. This system changes coal mine monitoring system＇s traditional way of after-alarming into early-warning, and thus enhances its feasibility.

环孢素A和他克莫司的高效液相色谱-串联质谱检测及其室内质控评价

目的建立环孢素A和他克莫司治疗药物监测的高效液相色谱-串联质谱(HPLC-MS/MS)质控方法,应用Westgard多规则理论对质控样品进行评估。方法利用HPLC-MS/MS建立人全血中环孢素A和他克莫司的血药浓度检测方法。对治疗药物监测过程中低、中、高3个浓度水平的质控样品进行统计分析,绘制Levery-Jennings和Z分数质控图,应用Westgard多规则理论进行室内质控评估。结果环孢素A和他克莫司的线性范围分别为10.40~1040.00和0.50~49.50 ng·mL^(-1),定量下限分别为10.40和0.50 ng·mL^(-1),提取回收率分别为108.61%~113.24%和101.99%~109.37%,内标归一化的基质因子分别为106.68%~111.27%和95.70%~97.81%,日内和日间的准确度及精密度均小于15.0%,其他参数也均符合生物样本定量分析要求。Levery-Jennings和Z分数质控图显示,2022年第三季度26组质控样品的检测结果提出警告4次(违反12s规则),未出现失控现象。结论建立的环孢素A和他克莫司治疗药物监测室内质控体系能够有效保障血药浓度检测的准确性。

基于运动模式划分的工业机器人健康监测方法

由于工业机器人存在多运动模式耦合的问题,传统的健康监测方案需要在每个关节处单独安装传感器,难以满足实际工业现场需求。本文以6关节工业机器人为研究对象,基于振动信号研究了多运动模式切换场景下工业机器人的健康监测方法。首先,通过跳变点算法实现机器人运动模式划分,获取不同运动模式对应的信号区间。其次,对不同运动模式的信号分别提取监测指标。最后,基于控制图法实现工业机器人不同关节的健康监测。在工业机器人退化实验数据中验证了本文所提方法,表明本文所提方法能够在仅使用两个振动传感器的条件下实现机器人6个关节的健康状态监测。本文所提运动模式划分算法在对大量历史退化数据进行分析时,所需运行时间更短、单次精度更高且重复性更好。本文提出的方法能够在使用少量传感器的条件下,有效避免运动模式耦合和采样信号的差异可能导致的监测结果误判,使得监测结果更加精准可靠,适用于实际工业现场的工业机器人健康状态监测。

基于BP神经网络的油气田站场管道安全预警方法

油气田站场管道承担着接收、增压、分输、清管、计量等具有高度危险性工作,一旦管道出现异常状况容易引起重大燃烧和爆炸事故,因此对油气田站场管道状态进行监测及安全预警十分重要。利用基于BP神经网络的状态监测数据处理方法,首先确定油气田站场管道应变数据作为状态监测指标,采集油气田站场管道的应变监测数据,通过BP神经网络进行仿真实验,获得并验证管道应变预测模型,利用控制图理论筛选出异常数据并对异常数据分类分级,同时依据危险等级类别进行安全预警并制定预案。该方法在风险发生初期就能发现异常状态数据,从而有针对性地采取相应的安全对策,以防止危险事故的发生。

既有建筑砌体结构健康监测和损伤预警研究

为保障既有建筑砌体结构运营期间的结构安全并延长其使用寿命,利用结构健康监测(SHM)系统与均值控制图,研究砌体结构的状况评估和预警机制。以北京市东城区某砌体结构为例,首先,根据该结构病害和损伤状况设计并实施对应的SHM系统,选取该结构承重墙裂缝和窗台相对位移作为监测对象;其次,收集结构响应和环境温度的数据,并利用收集后的监测数据构建结构响应同环境温度的相关性模型;然后,利用相关性模型模拟出结构响应监测数据的温度效应;最后,提出结构状态指数,并结合均值控制图预警分析该结构。结果表明:该监测系统能够实时采集监测数据,掌握砌体结构的健康状况,并对其结构损伤做出预警;根据监测结果,裂缝宽度和窗台相对位移的变化范围分别为[0.746,4.391]和[1.282,5.690]mm,均处在安全范围之内。根据预警系统中均值控制图结果,该结构裂缝宽度和相对位移发展趋势均处于正常状态。

浅析饮用水水质监测实验室化学试剂分类及全流程化管理

饮用水水质监测实验室化学试剂的种类繁多,大多数都属于危险化学品范畴,实验过程中所产生危险废弃物的化学性质复杂,给实验室安全管理带来了一定的难度。本文基于GB/T 5750—2023《生活饮用水标准检验方法》中所涉及的化学试剂,依据GB 6944—2012《危险货物分类和品名编号》将化学试剂分为有机类、无机类和化学分析试剂3大类,并细分为盐类、酸类和醇类等16小类。根据化学试剂的危险性质,依据GB 12268—2012《危险货物品名表》,将化学试剂分为易燃易爆类、有毒有害类、压缩气体和液化气体、腐蚀类、放射性物质、一般化学试剂6大类。并结合实验室管理过程中存在的问题,提出从化学试剂的申购、采购、验收、保存、领用和处理全流程化管理,且对分过程采用流程图式管理,为试剂管理员和实验员提供详细的操作流程和科学的处理方法,提高实验室安全管理能力和规范化体系建设。

科技安全监测预警指标体系构建与实证分析及对策建议

结合国内科技安全面临的形势,提出科技安全监测预警指标体系,并通过熵权法对指标体系进行赋权,以及运用彩虹图方法对2022年北京、上海、深圳和广州等8个重点城市科技安全监测预测进行实证分析。实证结果表明:科技创新能力越强,科技安全水平越高,两者存在较强的正相关关系;北京、上海等城市在科技成果安全、科技支撑经济发展等领域表现出较强的优势。因此,我国需加大科技创新要素投入,完善科技伦理法规与监管体系,强化核心技术产品出口管制,积极融入全球创新网络,综合提升科技安全水平。

基于Logistic回归的数据富裕环境下制造过程质量动态监控

针对数字化工厂“数据丰富,信息贫瘠”环境下制造车间生产过程存在异常信息利用不充分而造成监控效率低的问题,提出基于SPC技术和Logistic回归模型的制造过程质量监控方法。将关键工序中相关质量数据采集到MES系统,根据过程质量数据绘制T^(2)控制图,然后利用Logistic回归模型挖掘过程异常信息,并通过T 2和Logistic回归的联合优化实现数字化工厂制造过程质量监控的动态调整。以某薄膜晶体管液晶显示器等离子薄膜沉积生产工序为实际案例,验证了该制造过程质量监控方法的可行性和有效性。因此,在T^(2)控制图的基础上通过Logistic回归模型考虑过程异常信息能够提高制造过程质量监控的灵敏性和鲁棒性。

利用控制图监控门尼粘度仪性能稳定性

对标准橡胶门尼粘度进行连续25周的测定,通过绘制质量控制图,监控门尼粘度仪的运行状态。应用X^(-)-R(或S)控制图,结果表明,实验室门尼粘度仪性能稳定,在测定顺丁橡胶门尼粘度的系统误差在±0.3范围,随机误差应小于0.77,标准偏差应小于0.41。通过过程能力指数的计算,表明门尼粘度仪测定过程能力充足,但技术管理能力勉强,应加以改进。

基于三向控制图对图像数据过程监控

将机器视觉技术与传统统计过程控制结合,通过监控产品图像数据实现产品质量的过程控制。为了处理具有一致性或特定模式的产品图像数据的质量过程控制的问题,提出一种三向控制图方法对图像数据进行过程监控。将标准图像和捕获图像的强度值残差矩阵划分为特定区域,采用I-MR-R三向控制图对待定区域灰度值进行监控。把图像数据检验看做是对在相同部件不同位置上以相同的方式检测同一过程的统计控制状况。最后将发生偏移的样本均值与受控样本均值进行对比,找到具体发生偏移的区域。

PDCA在重点监控药品管理中的作用探究

为探究在重点监控药品管理中应用PDCA的成效,选取2022年1月—2022年8月淮安市第三人民医院药学部413份重点监控药品医嘱为研究对象,通过柱状图分析法分析重点监控药品医嘱的合理性;通过PDCA联合鱼骨分析图分析和处理重点监控药品管理问题。结果显示,在药学部重点监控药品的413份医嘱中发现了206份不合理医嘱,得出重点监控药品的合理使用率为50.12%,通过数据分析发现医院存在无监督体系、无明确宣教资料、药师干预不足、科室培训不足等问题,这些问题成为影响药品合理使用的原因。经过PDCA管理之后,该院药品合理使用率提升至79.58%。研究发现,将PDCA应用于药学部重点监控药品管理,不仅能够及时发现重点监控药品管理中存在的问题,还能够显著提升药品管理的效率和质量,具有很高的应用价值,值得推广。

基于GPRS和电子海图的船舶导航与监控系统

为了有效地保障船舶在受限水域及能见度不良情况下的航行安全,综合应用高精度差分GPS定位技术、GPRS数据传输及处理技术、电子海图显示信息系统技术、计算机网络技术、图象处理技术、ORACLE数据库管理技术等6项高科技实现了船舶导航与监控系统,该系统具有航线设计、船舶导航、航行记录、航行再现、岸上监控、码头泊位管理、引航作业管理、海图管理、海图改正、海图作业、打印、信息查询等功能。用摄像头定时拍摄驾驶台分罗经,对拍摄到的图片进行图象处理,将获得的船舶航向与GPS船位通过串口送入船上笔记本电脑进行叠加显示,笔记本电脑将本船信息、引航员信息经压缩编码后通过GPRS网络送入ORACLE数据库并通知与服务器连接的各个监控终端,保证与之连接的监控终端同步显示。

3.0MeV高频高压电子加速器计算机监控系统的研制

描述了基于PLC的高频高压电子加速器的计算机监控系统。在满足加速器正常操作的基础上,增加了能量反馈控制回路,提高了输出能量的稳定度(1％);保证了扫描电流的大小随输出能量变化的自动跟踪调节;在故障诊断方面也提供了有效的手段帮助排除故障或分析故障原因。

地铁隧道沉降变形信息发布及预警系统设计与实现

研究营运隧道沉降监测系统变形信息网络发布设计方案。分析系统整体方案的需求、使用要求,构建系统整体框架。根据信息发布要求和变形监测硬件通讯要求建立数据库系统。利用SQL Server平台构建数据库系统,用于接收底层硬件的传输数据,存储数据处理的结果,并为信息发布提供数据支持。整个发布系统最终实现隧道变形系统的信息实时发布和预警功能,并以图表形式提供数据查询结果。监测人员通过获取的信息数据和报警信息,实时监控隧道的运营情况,及时做出相应的列车运行调整。