Circular and cylindrical profile monitoring considering spatial correlations

Geometric specifications are important control objects of mechanical components in modern manufacturing. For instance, circularity and cylindricity are essential indicators of high-precision rotary parts. With an increase in the number of measurement points, traditional statistical process control (SPC) methods cannot be applied in many processes because the measurements are highly correlated. During the past two decades, several studies have focused on profile monitoring. A profile, which describes the relationship between independent and response variables, is suitable for large-scale, complex and high-dimensional data monitoring. However, the issue of spatial correlations in measurement points remains unsolved. Considering spatial correlations, this study focuses on circular and cylindrical profiles and proposes a new method combining a spatial correlation model with control charting. SPC methods are utilized to establish control charts and analyze the control processes. The results of simulation and case study indicate that the proposed method is feasible and effective in monitoring circular and cylindrical profiles and can be extended to other geometric specifications.     

Simple linear profiles monitoring in the presence of within profile autocorrelation

Quality of some processes or products can be characterized effectively by a function referred to as profile. Many studies have been done by researchers on the monitoring of simple linear profiles when the observations within each profile are uncorrelated. However, due to spatial autocorrelation or time collapse, this assumption is violated and leads to poor performance of the proposed control charts. In this paper, we consider a simple linear profile and assume that there is a first order autoregressive model between observations in each profile. Here, we specifically focus on phase II monitoring of simple linear regression. The effect of autocorrelation within the profiles is investigated on the estimate of regression parameters as well as the performance of control charts when the autocorrelation is overlooked. In addition, as a remedial measure, transformation of Y-values is used to eliminate the effect of autocorrelation. Four methods are discussed to monitor simple linear profiles and their performances are evaluated using average run length criterion. Finally, a case study in agriculture field is investigated.     

Monitoring and prediction of big process data with deep latent variable models and parallel computing

Process monitoring and quality prediction are crucial for maintaining favorable operating conditions and have received considerable attention in previous decades. For majority complicated cases in chemical and biological industrial processes with particular nonlinear characteristics, traditional latent variable models, such as principal component analysis (PCA), principal component regression (PCR), partial least squares (PLS), may not work well. In this paper, various nonlinear latent variable models based on autoencoder (AE) are developed. In order to extract deeper nonlinear features from process data, the basic shallow AE models are extended to the deep latent variable models, which provides a deep generative structure for nonlinear process monitoring and quality prediction. Meanwhile, with the ever increasing scale of industrial data, the computational burden for process modeling and analytics has becoming more and more tremendous, particularly for large-scale processes. To handle the big data problem, the parallel computing strategy is further applied to the above model, which partitions the whole computational task into a few sub-tasks and assigns them to parallel computing nodes. Then the parallel models are utilized for process monitoring and quality prediction applications. The effectiveness of the developed methods are evaluated through the Tennessee Eastman (TE) benchmark process and a real-life industrial process in an ammonia synthesis plant (ASP).     

起重机智能监控装置的研究与开发

针对起重机监控装置的发展滞后于起重机产业发展的现状,以液压式汽车起重机为对象,分析了国内外液压式汽车起重机系统载荷、力矩建模存在的问题.提出了一种既有理论建模,又有数据分析建模的新建模方式,并结合DSP技术,完成了整套起重机监控系统装置的整体设计以及相应的硬件和软件设计工作.对整套装置的具体实现进行了详尽的研究与分析.     

Multi-block synchronous prediction of concrete dam displacements using MIMO machine learning paradigm

The integration of numerous monitoring points poses a significant challenge to the efficient modeling of dam displacement behavior, and multi-point synchronous prediction is an effective solution. However, traditional approaches usually construct site-specific data-driven models for each monitoring point individually, which focus on single-target regression and discard the underlying spatial correlation among different displacement monitoring points. This study therefore proposes a multi-input multi-output (MIMO) machine learning (ML) paradigm based on support vector machine (SVM) for synchronous modeling and prediction of multi-point displacements from various dam blocks. In this method, a novel multi-output data-driven model, termed as multi-target SVM (MSVM), is formulated through a deep hybridization of classical SVM architecture and multi-target regression. During the initialization of MSVM, the intercorrelation of multiple target variables is fully exploited by decomposing and regulating the weight vectors. The proposed MSVM is designed to capture the complex MIMO mapping from influential factors to multi-block displacements, while taking into account the correlation between multi-block displacement outputs. Additionally, in order to avoid obtaining the unreliable prediction results due to the empirical selection of parameters, an efficient optimization strategy based on the parallel multi-population Jaya (PMP-Jaya) algorithm is used to adaptively tune the hyperparameters involved in MSVM, which contains no algorithm-specific parameters and is easy to implement. The effectiveness of the proposed model is verified using monitoring data collected from a real concrete gravity dam, where its performance is compared with conventional single-target SVM (SSVM)-based models and state-of-the-art ML-based models. The results indicate that our proposed MSVM is much more promising than the SSVM-based models because only one prediction model is required, rather than constructing multiple site-specific SSVM-based models for different dam blocks. Moreover, MSVM can achieve better performance than other ML-based models in most cases, which provides an innovative modeling tool for dam multi-block behavior monitoring.     

云计算环境下基于数据关联度的海洋监测大数据布局策略

海洋监测数据是具有强数据关联的大数据,如何高效地进行数据布局,是制约其有效管理和应用的关键问题之一。在云计算环境下,针对海洋监测大数据的特点,提出了一种基于数据关联度的海洋监测大数据布局策略。在保证数据中心存储均衡的情况下,综合考虑了监测任务、监测点和监测数据之间的关联,建立了海洋监测点间的关联度、监测数据间的关联度和监测数据全局关联度,从三个角度对海洋监测大数据进行布局,使得同一数据中心内的数据具有较高的关联度。通过实验分析,该方法降低了用户访问海洋监测大数据的响应时间,为海洋监测大数据提供了一种有效的布局策略。     

智能电网监控运行大数据分析系统统一建模方法研究

随着电网监控运行一体化运行趋势愈发明显,大数据技术应用的不断成熟、普及,为具有多源、高维、异构等特征的电网监控大数据的分析与应用提供了解决方案。本文提出了面向智能电网监控运行大数据分析系统的统一建模方法,分析了监控大数据的数据源、数据范围及现状与存在问题,指出了数据建模所需解决的问题与思路,采用元数据思想构建了公共模型,基于业务需要构建了应用模型,对于数据接入与存储管理方面,定义了元数据模型,其目的是在接入、汇总监控业务相关数据源的基础上,构建以设备为中心的监控数据关联模型,实现数据对象统一建模,为实现多源数据高效、规范接入提供了模型支撑,同时定义了符合该建模思路的元模型,元模型约束了建模行为,保证建立的模型遵行领域约束,为上层智能监控大数据分析应用奠定了基础。     

面向PHM的民机飞行数据分析及应用研究进展

针对现代飞机装备在可靠性、安全性、维修保障性和成本等方面存在的问题,探究了飞行数据在民机故障预测与健康管理领域的最新技术发展及应用。介绍了民机飞行数据采集和译码原理,针对民机智能运维应用场景,结合机器学习和数字孪生等技术,重点介绍了围绕机载系统健康监测与预测维修、发动机状态监控与寿命管理等应用场景的数据分析与建模方法,并结合实际工程问题给出了数据分析应用案例。开发的具有自主知识产权的民机飞行数据译码和智能化分析工具,可面向不同用户需求、不同机型开展客户化开发与部署,实现了民机的飞行安全监控和故障预警,可为民航运输安全、运营、保障等需求提供数字化、智慧化的关键理论与技术支撑。     

Exponentially weighted moving average-based procedure with adaptive thresholding for monitoring nonlinear profiles: Monitoring of plasma etch process in semiconductor manufacturing

Recently, there are many situations where the quality of a process is characterized by a relationship of functional data (or profiles) such as time series and image data. Such data have been used for detecting out-of-process and quality improvement in many engineering applications such as semiconductor manufacturing, automobile manufacturing, and nano-machining systems. The functional data contain high dimensionality, high feature correlation, non-stationality, and large amount of noise. Due to such characteristics, most classic statistical process control (SPC) may not perform on-line monitoring satisfactorily on functional data. In addition, local shift monitoring with functional data is more significant than the detection of global shifting patterns. In this paper, wavelet-based exponentially weighted moving average (EWMA) test statistic with adaptive thresholding method, which extracts several significant coefficients from original functional data in the wavelet domain and monitors out-of-control events, is proposed. Instead of monitoring global shifting, the local shifting in functional data is of major significance in our study. Throughout this study, we use a spectroscopy in monitoring of plasma etching process from semiconductor manufacturing to illustrate the implementation of the proposed approach. Experiment studies show that the proposed approach quickly detects smaller local shifts compared with the well-known methods.     

环境监测信息共享与时空表达研究

在我国环境监测体系的建设过程中,各种监测系统大多相互独立,缺少统一数据格式和系统规范,环境监测信息共享与可视化问题急待解决;针对此,研究了环境信息集成与共享技术,基于环境信息元数据内容及空问数据库管理引擎,提出了环境监测数据仓库的概念;同时还提出了面向对象的环境监测时空数据建模与时空表达技术;基于上述方法设计实现的环境监测信息共享与时空表达原型系统已投人广州市环境监测体系中测试;结果表明,基于广州市建立的包含信息共享与时空表达的环境监测平台,能够有效地形成一个全面监控环境质量与污染物排放情况的体系,为当地管理部门提供环境监测的数据管理、时空分析和可视化等决策支持服务.     

Concurrent quality and process monitoring with canonical correlation analysis

Canonical correlation analysis (CCA) is a well-known data analysis technique that extracts multidimensional correlation structure between two sets of variables. CCA focuses on maximizing the correlation between quality and process data, which leads to the efficient use of latent dimensions. However, CCA does not focus on exploiting the variance or the magnitude of variations in the data, making it rarely used for quality and process monitoring. In addition, it suffers from collinearity problems that often exist in the process data. To overcome this shortcoming of CCA, a modified CCA method with regularization is developed to extract correlation between process variables and quality variables. Next, to handle the issue that CCA focuses only on correlation but ignores variance information, a new concurrent CCA (CCCA) modeling method with regularization is proposed to exploit the variance and covariance in the process-specific and quality-specific spaces. The CCCA method retains the CCA's efficiency in predicting the quality while exploiting the variance structure for quality and process monitoring using subsequent principal component decompositions. The corresponding monitoring statistics and control limits are then developed in the decomposed subspaces. Numerical simulation examples and the Tennessee Eastman process are used to demonstrate the effectiveness of the CCCA-based monitoring method.     

Dynamics and stability for a class of evolutionary games with time delays in strategies一类具有时滞策略的演化博弈的动力学和稳定性

This paper investigates the modeling and stability of a class of finite evolutionary games with time delays in strategies. First, the evolutionary dynamics of a sequence of strategy profiles, named as the profile trajectory, is proposed to describe the strategy updating process of the evolutionary games with time delays. Using the semi-tensor product of matrices, the profile trajectory dynamics with two kinds of time delays are converted into their algebraic forms respectively. Then a sufficient condition is obtained to assure the stability of the delayed evolutionary potential games at a pure Nash equilibrium.     

Using evolutionary artificial neural networks in monitoring binary and polytomous logistic profiles

A statistical profile is a relationship between a quality characteristic (a response) and one or more explanatory variables to characterize quality of a process or a product. Monitoring profiles or checking the stability of profiles over time, has been extensively studied under the normal response variable, but it has paid a little attention to the profile with the non-normal response variable denoted by generalized linear models (GLM). Whereas, some of the potential applications of profile monitoring are cases where the response can be modelled using logistic profiles entailing binary, nominal and ordinal models. Also, most of existing control charts in this field have been developed by statistical approach and employing machine learning techniques have been rarely addressed in the related literature. Hence, to implement on-line process monitoring of logistic profiles, a novel artificial neural network (ANN) as a control chart with a heuristic training procedure is proposed in this paper. Performance of the proposed approach is investigated and compared using simulation studies in binary and polytomous models based on average run length (ARL) criterion. Simulation results revealed a good performance of the proposed approach. Nevertheless, to enhance the detection ability of the proposed approach more, the idea of combining run-rule which is a supplementary tool for making more sensitive control chart with final statistic is also implemented in this paper. Furthermore, a diagnostic method with machine learning schemes is employed to identify the shifted parameters in the profile. Results indicate the superior performance of the proposed approaches in most of the simulations. Finally, an example is used to illustrate the implementation of the proposed charting scheme.     

一种基于时序的状态监控及故障诊断系统

复杂系统在研发及运行过程中会产生大量的状态量及参数记录，为判定系统运行的健康状况，往往在运行结束后对其进行挖掘分析。在对数据分析的过程中，信号的时序变化成为复杂系统运行状态评估的重要参照之一，但是目前将时序分析作为独立分析模块进行状态监测的研究较少。在时序分析方面进行了改进，使用阈值判断结合时序分析的方法对复杂系统的监测点信号进行数据分析，根据分析结果使用专家系统进行基于案例的信号匹配及故障诊断，实现复杂系统基于时序的状态监测及故障诊断。提出的状态监测及故障诊断系统除了适用于对时序要求较高的复杂系统以外，对航天探测、新能源设备研发、医疗技术发展及海洋、大气、大地环境监测等领域都具有应用价值。     

OPC UA信息建模:煤矿监控系统集成案例研究

为了传统OPC技术由于数据建模能力不足而导致的互操作性问题,OPC基金会发布了OPC UA规范,从理论上解决了此问题,但缺乏具体的建模流程。提出具有可操作性的OPC UA信息建模流程,并以煤矿监控系统集成作为案例来验证此建模流程具有可行性,同时推动了OPC UA从理论到实践的发展。     

利用多时相Landsat遥感图像数据对土法炼焦点进行动态监测——以山西省东南部地区为例

国家明确规定对土法炼焦进行取缔,然而,土法炼焦并没有得到有效地控制,形势相当严峻,提高监测的技术水平是解决问题的重要措施之一。以土焦生产大省山西省东南部为试验区,以1999年和2004年Landsat5数据为背景资料,采用TM751和TM721波段组合技术,对土法炼焦进行了信息提取和挖掘。结果表明,TM7通道是土焦的敏感通道,TM5通道次之,在TM7通道上,土焦点呈现出明显地“峰”值特征,温度达到一定值,对应的五波段也出现“峰”值特征,给出了具体的临界值;TM751波段合成图像具有解译标志明显,解译判对率达90％以上;试验区样区土焦点数量增长10倍以上,且主要分布在人口集中的平原区,对环境造成了严重的污染,最后探讨了控制策略。研究结果为大面积、准确监测土法炼焦动态变化奠定了基础,并为土焦监管提供了一种全新、快速的技术手段。     

Towards online monitoring of concrete dam displacement subject to time-varying environments: An improved sequential learning approach

Data-based models are widely applied in concrete dam health monitoring. However, most existing models are restricted to offline modeling, which cannot continuously track the displacement behavior with dynamic evolution patterns, especially in time-varying environments. In this paper, sequential learning is introduced to establish an online monitoring model for dam displacement behavior. This approach starts by considering the timeliness difference between old and new data using the forgetting mechanism, and a novel adaptive forgetting extreme learning machine (AF-ELM) is presented. A primary predictor based on AF-ELM is then formulated, which aims to sequentially learn the complex nonlinear relationship between dam displacement and main environmental factors. Considering the chaotic characteristics contained in the residual sequence of the primary predictor, a multi-scale residual-error correction (REC) strategy is devised based on divide-and-conquer scheme. Specifically, time-varying filter-based empirical mode decomposition is adopted to decompose the raw chaotic residual-error series into a set of subseries with more stationarity, which are further aggregated and reconstructed by fuzzy entropy theory and suitable approximation criterion. Finally, the corrected residual sequence is superimposed with the preliminary predictions from AF-ELM to generate the required modeling results. The effectiveness of the proposed model is verified and assessed by taking a real concrete dam as an example and comparing prediction performance with state-of-the-art models. The results show that AF-ELM performs better in displacement prediction compared with benchmark models, and the multi-scale REC can effectively identify the valuable information within the residual sequence. The proposed online monitoring model can more closely track the dynamic variations of displacement data, which provides a fire-new solution for dam behavior prediction and analysis.