基于 EWMA 控制图模型的公司财务质量判别

针对以往基于静态数据的两分类判别法的不足,选取沪深A股公司季报数据,将公司的财务质量分为正常、不稳定和困境3种状况,构建了具有时间累积性的EWMA控制图模型进行判别分析。选取了另外的24家沪深A股公司对模型的判别准确率进行了测试。结果表明,通过构建EWMA控制图模型并划分不同的控制限,可以对多个类型的公司财务质量做出有效的判别。     

基于AndroidPN技术的Zigbee无线网络的物联网数据采集

为了实现对体温和脉搏长时间的监测并形象地展示出温度和脉搏随时间变化的趋势,设计了一款基于AndroidPN技术的Zigbee无线网络温度、脉搏实时采集器,通过传感器采集人体实时的体温和脉搏信息,经过蓝牙或通过在PC上配置Android PN服务将采集到的信息传送到Android手机客户端,由手机实现对数据的显示和处理并生成实时的动态图表信息,再由手机的GSM等上网模块将数据上传到云端,实现数据的云存储,便于用户查看、管理。这种实时监测仪提供了一种便捷、低成本的温度和脉搏记录方式,在日常生活中有着广泛的应用。     

Damage alarming for bridge expansion joints using novelty detection technique based on long-term monitoring data

Damage alarming and safety evaluation using long-term monitoring data is an area of significant research activity for long-span bridges. In order to extend the research in this field, the damage alarming technique for bridge expansion joints based on long-term monitoring data was developed. The effects of environmental factors on the expansion joint displacement were analyzed. Multiple linear regression models were obtained to describe the correlation between displacements and the dominant environmental factors. The damage alarming index was defined based on the multiple regression models. At last, the X-bar control chart was utilized to detect the abnormal change of the displacements. Analysis results reveal that temperature and traffic condition are the dominant environmental factors to influence the displacement. When the confidence level of X-bar control chart is set to be 0.003, the false-positive indications of damage can be avoided. The damage sensitivity analysis shows that the proper X-bar control chart can detect 0.1 cm damage-induced change of the expansion joint displacement. It is reasonably believed that the proposed technique is robust against false-positive indication of damage and suitable to alarm the possible future damage of the expansion joints.     

使用Gantt图建立EPC工作流模型的研究

论文结合建筑工程项目的特点,讨论如何使用广义Gantt图建立基于EPC的工作流过程模型,旨在为建筑工程项目的工作流管理提供一个新的思路。     

Two successive occasions resubmitted sampling scheme-based control chart

In this paper, a resubmitted sampling-based successive sampling over two successive occasions control chart is proposed to monitor the underlying characteristic of interest. Auxiliary information of the first occasion is utilized to monitor the relative change in the study variable over the second occasion successively despite high degree of correlation. The structural and operational design is presented along with the comparative performance evaluation. The average run length is used as a performance evaluation measure and proved the argument in favor of the presented concept in comparison with the other auxiliary data control charts. The implementation is explained through two real examples.     

A triple exponentially weighted moving average control chart for monitoring time between events

Time between events (TBE) charts are used in high-yield processes where the rate of occurrences is very low. In the current article, we propose a triple exponentially weighted moving average control chart to monitor TBE (regarded as triple exponentially weighted moving average TEWMA-TBE chart) modeled by a gamma distribution. One- and two-sided schemes of the proposed chart are designed and compared with the double EWMA DEWMA-TBE and EWMA-TBE charts. It is shown that the lower- and two-sided TEWMA-TBE charts outperform its competitors, especially for small to moderate downward shifts, while the upper-sided TEWMA-TBE chart has very good detection ability for small shifts. We also study the robustness of the proposed chart when the true distribution is a Weibull or a lognormal and it is found that the TEWMA-TBE chart has better robustness properties than its competitors, especially for small shifts. Two illustrative examples from airplane accidents and earthquakes are also provided to display the application of the proposed chart.     

An EWMA signed ranks control chart with reliable run length performances

During the design phase of a control chart, the determination of its exact run length properties plays a vital role for its optimal operation. Markov chain or integral equation methods have been extensively applied in the design phase of conventional control charts. However, for distribution-free schemes, due to the discrete nature of the statistics being used (such as the sign or the Wilcoxon signed rank statistics, for instance), it is impossible to accurately compute their run length properties. In this work, a modified distribution-free phase II exponentially weighted moving average (EWMA)-type chart based on the Wilcoxon signed rank statistic is considered and its exact run length properties are discussed. A continuous transformation of the Wilcoxon signed rank statistic, combined with the classical Markov chain method, is used for the determination of the average run length in the in- and out-of control cases. Moreover, its exact performance is derived without any knowledge of the distribution of sample observations. Finally, an illustrative example is provided showing the practical implementation of our proposed chart.     

Statistical testing for sufficient control chart performances during monitoring of grouped processes

With ISO 7870-8, a standardized application of charting techniques for short runs and small mixed batches was presented in 2017. Similar to various scientific approaches, it requires that sample values from grouped processes follow nearly identical distributions. In practice, however, there tend to be differences between distribution parameters. Moreover, equal parameters do not ensure that distributions are properly aligned to the center line and control limits of the chart. These facts can lead to undesired control chart performances which can be expressed by average run lengths (ARL) during in-control and out-of-control conditions. In this work, a statistical test for sufficient control chart performances during monitoring of grouped processes based on preliminary samples is proposed. Control chart performances are defined as sufficient when they deviate within acceptable ranges from usual performances during single process monitoring in mass production. The ARL resulting from estimated distributions and planned production sequences is used as test statistic and calculated via the Markov chain approach. Exemplary tests are executed for scenarios with individuals and cumulated sum (CUSUM) charts. A simulative determination of error rates resulting from the ARL-based testing demonstrates its effectiveness in testing for sufficient control chart performances compared to an indirect testing with Levene's test and a one-way analysis of variance (ANOVA).     

Progressive mean control chart is not a special case of an exponentially weighted moving average control chart

The progressive mean (PM) statistic is based on a simple idea of accumulating information of each subgroup by calculating the average progressively. Its weighting structure is based on a subgroup number that changes arithmetically, which makes the PM chart unique and efficient compared with the existing classical memory control charts. In a recent article (see reference 1), it was claimed that the PM chart is a special case of the exponentially weighted moving average (EMWA) chart. In this article, it is shown that even though the PM statistic can be written in the form of an EWMA statistic, the variance of the PM statistic is different from that of the EWMA statistic. Consequently, the limits of the PM chart are different from that of the EWMA chart. Therefore, it is found that the PM chart is not a special case of the EWMA chart; hence, the claim in reference 1 is incorrect. Furthermore, it is pointed out in this paper that no adaptive property in the weighting parameter of the PM statistic exists, further contradicting the claim in reference 1.     

Distribution-free double-sampling precedence monitoring scheme to detect unknown shifts in the location parameter

In most applications, parametric monitoring schemes are used to monitor the majority of industrial and nonindustrial processes in order to improve the quality of the outputs or services. However, parametric monitoring schemes are known to underperform when the normality assumption is not met or when there is not enough information about the symmetry or asymmetry nature of the process underlying distribution. Hence, in this paper, a new nonparametric Phase II Shewhart-type double-sampling (DS) monitoring scheme based on the precedence statistic is proposed in order to efficiently monitor quality processes when the underlying process distribution departs from normality. The performance is investigated using the average run length (ARL), standard deviation of the run length (SDRL), expected ARL (EARL) and expected average number of observations to signal (EANOS), and the average sample sizes (ASS) metrics. The latter metrics are computed using Monte Carlo simulation and exact formulae. In general, it is shown that the new DS precedence scheme outperforms the existing basic Shewhart precedence scheme with and without supplementary runs rules in many situations. A real-life illustrative example based on a filling process of milk bottles is provided to demonstrate the application and implementation of the new DS precedence monitoring scheme.