Integrating multivariate engineering process control and multivariate statistical process control

Multivariate engineering process control (MEPC) and multivariate statistical process control (MSPC) are two strategies for quality improvement that have developed independently. MEPC aims to minimize variability by adjusting process variables to keep the process output on target. On the other hand, MSPC aims to reduce variability by monitoring and eliminating assignable causes of variation. In this paper, the use of MEPC alone is compared to using the MEPC coupled with MSPC. We use simulations to evaluate the average run lengths (ARL) and the averages of the performance measure. The simulation results show that the use of both MEPC and MSPC can always outperform the use of either alone. To detect small sustained shifts of the mean vector, combing MEPC with a multivariate generally weighted moving average (MGWMA) chart (MEPC/MGWMA) is more sensitive than the MEPC/multivariate exponentially weighted moving average (MEWMA) chart and MEPC/Hotelling’s χ² chart. An example of the application, based on the proposed method, is also given.     

An intelligent sensor fusion system for tool monitoring on a machining centre

The objective of this paper is to construct an intelligent sensor fusion monitoring system for tool breakage on a machining centre. Since none of the sensing and diagnosis techniques have proved to be completely reliable in practice, an intelligent tool-monitoring system consisting of a neural-network-based algorithm and a sensor fusion system is proposed. The dual sensing signals of cutting force and acoustic emission are used simultaneously in the proposed system owing to good correlation existing between them, and, a self-learning neural-network algorithm is used to integrate multiple sensing information to make a proper decision about tool condition. The results show good performance in tool-breakage detection by the proposed monitoring system, especially where there is high interference.     

基于多变量统计过程监控的盾构机故障诊断

由于盾构机安装有众多监测仪表,且依靠单变量过程监控和人工诊断方式已经无法满足监测的要求,因此引入多变量统计过程监控方法(MSPM).主元分析(PCA)是应用最广泛的MSPM技术,PCA根据盾构运行监测的过程变量和历史数据建立数学模型,并计算统计监控量T2和平方预测误差δ,以及主元空间和残差空间的控制限,分析过程变量是否发生异常.最后以盾构刀盘驱动系统和螺旋输送液压系统为例说明MSPM的详细应用.     

Design of a holonic remote monitoring and diagnosis system for fleet management

The main challenges in sensor network based monitoring systems are the control and management of complex modules and elements interacting among them and with the environment. This work presents and validates a design model for monitoring and diagnosis systems based on sensor networks exploiting potentialities of the holonic approach. The holonic structure is designed with the final goal to guarantee the performances and the functionalities required by the application scenario of road and work vehicles rental companies. Actually they are interacting and geographically distributed systems. The capabilities of cooperation among modules, hierarchical organization, modularity and reliability of holonic design are exploited. Diagnosis of monitored events is coded and validated through an ad hoc ontology. The design does not give assumption on the physical architecture of holons but only on their capacities/properties, therefore it can be reused in different physical and hardware resources. The case studies confirm the capability of our design to be quickly transferred to programmers and coded in firmware and software.     

培养过程通用微机智能监控系统的设计与实现

本文简要介绍了“培养过程、发酵过程通用微机智能监控系统”的研制,介绍了系统实现的主要功能,给出了系统的硬件配置和软件实现。     

基于状态监测和故障诊断技术的供水泵组状态维修方法研究

随着现代工业技术的进步,企业竞争的重要物质资源-设备已经朝着集成化、大型化、自动化、精密化的方向发展,先进的设备要求先进的设备管理与维修方法.先进的状态监测及故障诊断技术是提高设备管理与维修的重要手段.通过成功的实践总结出了一套基于此技术用于供水泵组状态维修的有效方法.     

门机机电状态综合监测与分析

针对500 t门座起重机（简称门机）的工作环境和工作特征,结合电气量信号和机械量信号对其进行状态监测和分析,同时对电气信号和机械信号进行监测.以PLC信号为分类标准,将机电信号分成不同工况,再通过统计学方法进行分析.以MATLAB为平台,综合利用了基于连接与嵌入对象的过程控制（OPC）通讯、统计质量控制等技术和方法对数据进行采集、处理和分析.由实验结果可知,通过分析和研究不同工况下机电信号之间的相关特性,可以准确地掌握门机的钢结构受力情况和动态特征.此外,通过基于PLC电气信号的工况分类,可以有效减少由于使用单一的判断标准而产生的误判断.最终达到提高诊断和评估的针对性、准确率的目的,同时也为今后门机设计、建造的改进提供可靠的依据.     

石化企业生产过程监控与管理系统关键技术研究

为满足石化企业生产过程监控与管理的需求,研制了基于Web的生产过程监控与管理系统。对系统开发中所涉及的关键技术进行了深入研究,提出了一个较为通用的基于Web的三层体系结构,借助Infoplus．21实时数据库和Oracle关系数据库,实现了实时数据采集与数据处理;综合应用可扩展标记语言和Office网络组件实现了系统的信息集成和复杂报表的编制;采用基于组件的编程技术和基于案例推理的生产调度策略,增强了系统的灵活性;系统采用Java、Java服务器网页、Servlet、JavaBean、可扩展标记语言和Office网络组件开发,表明了系统的实用性和可扩展性。     

Relevant and independent multi-block approach for plant-wide process and quality-related monitoring based on KPCA and SVDD

Due to prior knowledge being often unavailable in practice, a multi-block strategy totally based on data-driven analytics is an appropriate alternative for plant-wide processes. However, most recent multi-block methods are relatively vague or insufficient for dividing up the process space and lack the comprehensive fault information for quality-related monitoring. This work intends to develop a more reasonable multi-block method and demonstrate the negative impacts of quality-unrelated variables. Both motivations are entirely dependent on the correlation between variables. A major innovation is to determine those independent or related sets of variables, and to provide a more precise indication for those quality-related faults. Sub-blocks with related variables are each modeled by the KPCA, and the rest of the independent variables are treated as an input for a SVDD model. Finally, all of the statistical indicators are aggregated into a single statistic through Bayesian inference. The benefits of the proposed multi-block scheme (MKPCA-SVDD) are elaborated on in detail using numerical simulation, TE benchmark and industrial p-xylene oxidation process.