A side-sensitive synthetic coefficient of variation chart

Control charts for monitoring the coefficient of variation (γ) are useful for processes with an inconsistent mean (μ) and a standard deviation (σ) which changes with μ, by monitoring the consistency in the ratio σ over μ. The synthetic-γ chart is one of the charts proposed to monitor γ, and its attractiveness lie in waiting until a second point to fall outside the control limits before a decision is made. However, existing synthetic-γ charts do not differentiate between the points falling outside the upper control limit (UCL) and lower control limit (LCL). Hence, this paper proposes a side-sensitive synthetic-γ chart, where successive nonconforming samples must either fall above the UCL or below the LCL. Formulae to compute the average run length (ARL), the standard deviation of the run length (SDRL) and expected average run length (EARL) are derived using the Markov chain approach, and the algorithms to obtain the optimal charting parameters are proposed. Subsequently, the optimal charting parameters, ARL, SDRL and EARL values for various numerical examples are shown. Comparisons show that the side-sensitive synthetic-γ chart consistently outperforms the existing synthetic-γ chart, especially for small shifts. The proposed chart also consistently outperforms the Shewhart-γ chart, while showing comparable or better performance than the Exponentially Weighted Moving Average (EWMA) chart for most shift sizes, except for very small shifts. Finally, this paper shows the implementation of the proposed chart on an industrial example.     

面向云计算应用层演化的隐私保护方法研究

云计算应用层中的组合服务具有演化属性,因此,隐私数据在服务组合过程中,用户的隐私数据可能会因为服务或服务流程的演化而暴露。根据服务演化的特征,以描述逻辑为基础,提出了一种面向云计算应用层演化的隐私保护方法。对隐私协议进行形式化描述;根据服务的演化特征,对服务的演化进行监控,保证满足用户的隐私需求;利用实例研究证明该方法的正确性与可行性。     

Stretchable Composite Acoustic Transducer for Wearable Monitoring of Vital Signs

A highly flexible, stretchable, and mechanically robust low‐cost soft composite consisting of silicone polymers and water (or hydrogels) is reported. When combined with conventional acoustic transducers, the materials reported enable high performance real‐time monitoring of heart and respiratory patterns over layers of clothing (or furry skin of animals) without the need for direct contact with the skin. The approach enables an entirely new method of fabrication that involves encapsulation of water and hydrogels with silicones and exploits the ability of sound waves to travel through the body. The system proposed outperforms commercial, metal‐based stethoscopes for the auscultation of the heart when worn over clothing and is less susceptible to motion artefacts. The system both with human and furry animal subjects (i.e., dogs), primarily focusing on monitoring the heart, is tested; however, initial results on monitoring breathing are also presented. This work is especially important because it is the first demonstration of a stretchable sensor that is suitable for use with furry animals and does not require shaving of the animal for data acquisition.     

Laplacian regularized robust principal component analysis for process monitoring

Principal component analysis (PCA) is one of the most widely used techniques for process monitoring. However, it is highly sensitive to sparse errors because of the assumption that data only contains an underlying low-rank structure. To improve classical PCA in this regard, a novel Laplacian regularized robust principal component analysis (LRPCA) framework is proposed, where the “robust” comes from the introduction of a sparse term. By taking advantage of the hypergraph Laplacian, LRPCA not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information. An efficient alternating direction method of multipliers is designed with convergence guarantee. The resulting subproblems either have closed-form solutions or can be solved by fast solvers. Numerical experiments, including a simulation example and the Tennessee Eastman process, are conducted to illustrate the improved process monitoring performance of the proposed LRPCA.     

选矿生产指标可视化监控平台研究

针对选矿工业过程流程长、工序多、生产指标多的特点,结合数据可视化及可视分析技术,开发一种选矿生产指标可视化监控组态平台。该平台包括生产工艺可视化组态设计环境、生产指标监控和可视及可视分析3个工具。组态设计环境支持以组态方式绘制工艺流程图,并能通过可视界面自定义生产工序的输入输出指标、触发事件、约束规则、工序状态属性、提示信息等;支持集成专家知识、经验和规则以实现基于知识的生产指标监控;提供算法配置接口,方便集成指标监控算法。其组态出的每一个工序都可以复用和扩展,可以构建选矿行业的基础工序单元组件库,形成选矿行业的知识积累。基于可视的工艺流程,利用指标监控工具为指标配置可视化方案,实现指标可视化监控。可视化方案由可视及可视分析工具提供,包括实时数据、历史数据及其统计特性、多指标综合对比分析、指标关联关系分析、多视图等可视方案。此外,为了提升工序指标监控效率和减轻操作人员监控强度,系统提供因子分析、Pearson相关分析、互信息、信息熵等分析手段,以辅助人们提取出工序关键监控指标,从而实现对监控生产指标的约简。由于该平台以组态方式提供,使其可以快速应用于其他流程行业,实现生产指标可视化监控的组态化。最后,该系统作为选矿生产执行系统的一部分成功应用到某选矿厂生产过程中,取得了良好的应用效果。     

基于VRS技术的煤矿地表变形监测实践研究

随着采深的增大,晋华宫矿开采过程产生严重的地表沉降问题,对该矿地面建构及设施造成了安全隐患。该矿选择采用虚拟参考站技术(VRS技术)来对地表沉降进行测量监测。监测结果表明,VRS技术具有更好的准确性和科学性,在监测过程中表现出测量范围广、工作效率高、工作量小的特点,可以对该矿地表沉降问题进行有效监测。     

In situ monitoring of the biocatalysed partial hydrolysis of cocoa butter and palm oil fraction

Partial glycerides are widely used ingredients in confectionery products that can be produced from natural fats. In a biocatalytic partial hydrolysis of cocoa butter and palm oil fractions, a product mixture containing 1.5% monoglycerides and 5.5% diglycerides intended for the use in confectionery products was created. This study is a proof of principle that shows the feasibility of monitoring the biocatalytic partial hydrolysis of these two natural fats in situ with ATR-FTIR spectroscopy. An economic approach was utilised for calibration since partial glyceride calibration standards are costly and poorly available. The released compounds were quantified by means of chemometric modelling, and the model was validated with gas chromatography. Resulting root mean square errors were in the low per cent range. Additionally, the results indicate that distinction of the released free fatty acids is possible with ATR-FTIR spectroscopy.     

Multiphase batch process monitoring based on higher-order cumulant analysis

In this paper, a two-step phase partitioning strategy is proposed. Firstly, the number of phases is automatically determined according to the intra-class and inter-class similarity of feature space data, thus avoiding excessive manual intervention. Secondly, the phases are partitioned by step-wise adding the kernel entropy extended load matrix (KEELM), avoiding the wrong division of phases caused by unstable state of working condition conversion. A process monitoring model based on multiway kernel entropy independent component analysis (MKEICA) is constructed in each sub-phase to deal with complex batch processes with nonlinear and non-Gaussian properties. A new statistics index based on the idea of high order cumulant analysis (HCA) is constructed in each sub-phase for process monitoring. Compared with the traditional second-order statistics, it can obtain high-order statistical information. Finally, the proposed method is applied to the penicillin simulation platform process and compared with the traditional multiway kernel independent components analysis (MKICA) and HCA methods to verify the effectiveness of the method that is mentioned above.