Exploring Variability in Sea Level at a Tide Gauge Station through Control Charts

Monitoring temporal changes in sea level is important in assessing coastal risk.Sea level anomalies at a tide gauge station,if kinematically conceived,include systematic variations such as trend,acceleration,periodic oscillations,and random disturbances.Among them,the non-stationary nature of the random sea level variations of known or unknown origin at coastal regions has been long recognized by the sea level community.This study proposes the analyses of subgroups of random residual statistics of a rigorously formulated kinematic model solution of tide gauge variations using X-bar and S control charts.The approach is demonstrated using Key West,Florida tide gauge records.The mean and standard errors of 5-year-long subgroups of the residuals revealed that sea level changes at this location have been progressively intensifying from 1913 to the present.Increasing oscillations in sea level at this locality may be attributed partly to the thermal expansion of seawater with increasing temperatures causing larger buoyancy-related sea level fluctuations as well as the intensification of atmospheric events including wind patterns and the impact of changes in inverted barometer effects that will alter coastal risk assessments for the future.

Top-down法评定EDTA络合滴定法测定海洋沉积物中碳酸钙含量的不确定度

依据Top-down技术中的质控图法对EDTA络合滴定法测定海洋沉积物中碳酸钙含量的不确定度进行评定。利用2021年质控样标准物质GBW 07333、GBW 07314和GBW 07316中CaCO_(3)测定值的时序数据进行评定,3组时序数据正态统计量A^(2*)(s)和独立性统计量A^(2*)(RM)均小于1,CaCO_(3)测定值、A_(EWMi)和移动极差控制图没有失控点,3组时序数据处于受控状态。当CaCO_(3)质量分数分别为2.05%、6.20%和38.56%时,计算得其扩展不确定度分别为0.08%、0.07%和0.20%。将测量不确定度与对应含量值进行线性拟合建立了不确定度预测模型,GBW 07309和GBW 07334中CaCO_(3)的测量不确定度预测值分别为0.09%和0.18%,预测值与Top-down技术获取的评定结果较为吻合。

Ranked-Set Sampling Based Distribution Free Control Chart with Application in CSTR Process

Nonparametric(distribution-free)control charts have been introduced in recent years when quality characteristics do not follow a specific distribution.When the sample selection is prohibitively expensive,we prefer ranked-set sampling over simple random sampling because ranked set sampling-based control charts outperform simple random sampling-based control charts.In this study,we proposed a nonparametric homogeneously weighted moving average based on theWilcoxon signed-rank test with ranked set sampling(NPHWMARSS)control chart for detecting shifts in the process location of a continuous and symmetric distribution.Monte Carlo simulations are used to obtain the run length characteristics to evaluate the performance of the proposed NPHWMARSS control chart.The proposed NPHWMARSS control chart’s performance is compared to that of parametric and nonparametric control charts.These control charts include the exponentially weighted moving average(EWMA)control chart,Wilcoxon signed-rank with simple random sampling based the nonparametric EWMA control chart,the nonparametric EWMA sign control chart,Wilcoxon signed-rank with ranked set sampling-based the nonparametric EWMA control chart,and the homogeneously weighted moving average control charts.The findings show that the proposed NPHWMARSS control chart performs better than its competitors,particularly for the small shifts.Finally,an example is presented to demonstrate how the proposed scheme can be implemented practically.

Memory-Type Control Charts Through the Lens of Cost Parameters

A memory-type control chart utilizes previous information for chart construction.An example of a memory-type chart is an exponentially-weighted moving average(EWMA)control chart.The EWMA control chart is well-known and widely employed by practitioners for monitoring small and moderate process mean shifts.Meanwhile,the EWMA median chart is robust against outliers.In light of this,the economic model of the EWMA and EWMA median control charts are commonly considered.This study aims to investigate the effect of cost parameters on the out-of-control average run lengthðARL_(1)Þin implementing EWMA and EWMA median control charts.The economic model was used to compute the ARL_(1) parameter.The 14 input parameters were identified and the analysis was carried out based on the one-parameter-at-a-time basis.When the input parameters change based on a predetermined percentage,the ARL_(1) is affected.According to the results of the EWMA chart,nine input parameters had an effect andfive input parameters had no effect on the ARL_(1) parameter.Further,only seven of the 14 input parameters had an effect on the ARL_(1) of the EWMA median chart.However,the effect of each input parameter on the ARL_(1) was different.Moreover,the ARL_(1) for the EWMA median chart was smaller than the EWMA chart.This analysis is crucial to observe and determine the input parameters that have a significant impact on the ARL_(1) of the EMWA and EWMA median control charts.Hence,practitioners can obtain an overview of the influence of the input parameters on the ARL_(1) when implementing the EWMA and EWMA median control charts.

Top-down控制图法评定石墨炉原子吸收光谱法测定血中铅含量的不确定度

采用Top-down控制图法评定石墨炉原子吸收光谱法测定血中铅含量的不确定度。依据GBZ/T 316.1—2018《血中铅的测定第1部分:石墨炉原子吸收光谱法》,不同人员每周测定铅质量浓度为(104±15)μg/L的冻干牛血质控样品1次,连续测定35次。对检测的35组数据进行离群值、正态性、独立性验证,并用Top-down控制图法评定铅含量的不确定度。血中铅质量浓度质控测试结果为(105.97±12.04)μg/L(95%包含概率)。Top-down控制图法评定血中铅含量的不确定度,简单、易操作,可以作为血铅含量检测的质量控制手段,对实验室检测结果进行趋势性分析,发现潜在的影响检测结果的因素,规避实验室风险。

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.

基于Shewhart Control Chart的生鲜乳中兽药残留风险预警方法研究

针对生鲜乳中兽药残留水平动态变化的特点,本研究探索构建一种基于历史数据的生鲜乳中兽药残留动态预警方法。基于Shewhart Control Chart理论,建立了兽药残留超标预警、检出率异常预警和平均值标准偏差预警方法。对中国某大型乳企的生鲜乳中氟甲喹和达氟沙星检测数据分析发现,无需触发风险预警,同时对假定出现的检出率异常和平均值异常预警情况进行了分析。

Automated Identification of Basic Control Charts Patterns Using Neural Networks

The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identify the three basic control chart patterns;natural, shift, and trend. This identification is in addition to the traditional statistical detection of runs in data, since runs are one of the out of control situations. It is assumed that a process starts as a natural pattern and then may undergo only one out of control pattern at a time. The performance of the proposed algorithm was evaluated by measuring the probability of success in identifying the three basic patterns accurately, and comparing these results with previous research work. The comparison showed that the proposed algorithm realized better identification than others.

Construction of Control Charts for Monitoring Various Parameters Related to the Management of the COVID-19 Pandemic

To monitor the quality characteristics of a process, appropriate graphical and statistical tools must be used. These tools are capable of showing the evolution over time of the behavior of the quality characteristics (measurable or countable) and detecting situations that seem to present certain anomalies. The control chart is one of these tools widely used in quality management. In the process of managing the COVID-19 pandemic, this tool will make it possible to know at all times whether the parameters monitored such as the positivity rate, the recovery rate, and the mortality rate, are under control and to act accordingly. Monitoring cure and mortality rates will also show us the effectiveness of the treatments used.

The Principle of Mathematical Induction Applied to the Generalized Model for the Economic Design of X-Control Charts

Rahim and Banerjee [1] developed a general model for the optimal design of x-control charts. The model minimizes the expected cost per unit time. The heart of the model is a theorem that derives the expected total cost and the expected cycle length. In this paper an alternative simple proof for the theorem is provided based on mathematical induction.

Calculation of One-Valued Control Limits by Control Chart of Angles

The data we use to express angle or direction are entitled directional data. In a plan right angled coordinate system the traditional control chart can’t solve the quality control problem which the characteristic value is angle. This paper analyses and calculates the one valued control limits by control chart of angles.

Control charts for the Pareto distribution

The Pareto distribution plays an important role in various areas of research. In this paper, the average run length （ARL） unbiased control charts, which monitor the shape and threshold parameters of the Pareto distribution respectively, are proposed when the incontrol parameters are known. The effects of parameter estimation on the performance of the proposed control charts are also studied. Results show that the control charts with the estimated parameters are not suitable to be used in the known parameter case, thus the ARL-unbiased control charts for the shape and threshold parameters with the desired ARLo, which consider the variability of the parameter estimates, are further developed. The performance of the proposed control charts is investigated in terms of the ARL. Finally, an example is given to illustrate the proposed control charts.

Computation of Average Run Length for Residual-Based T^2 Control Chart for Multivariate Autocorrelated Processes

The expression of residual is obtained according to its dynamic response to mean shift,then the distribution of T 2 statistic applied to the residual is derived,thus the probability of the T 2 statistic lying outside the control limit is calculated.The above-mentioned results are substituted into the infinite definition expression of the average run length(ARL),and then the final finite ARL expression is obtained.An example is used to demonstrate the procedures of the proposed method.In the comparative study,eight autocorrelated processes and four different mean shifts are performed,and the ARL values of the proposed method are compared with those obtained by simulation method with 50,000 replications.The accuracy of the proposed method can be illustrated through the comparative results.

A Cause-Selecting Control Chart Method for Monitoring and Diagnosing Dependent Manufacturing Process Stages

Many industrial products are normally processed through multiple manufacturing process stages before it becomes a final product.Statistical process control techniques often utilize standard Shewhart control charts to monitor these process stages.If the process stages are independent,this is a meaningful procedure.However,they are not independent in many manufacturing scenarios.The standard Shewhart control charts can not provide the information to determine which process stage or group of process stages has caused the problems(i.e.,standard Shewhart control charts could not diagnose dependent manufacturing process stages).This study proposes a selective neural network ensemble-based cause-selecting system of control charts to monitor these process stages and distinguish incoming quality problems and problems in the current stage of a manufacturing process.Numerical results show that the proposed method is an improvement over the use of separate Shewhart control chart for each of dependent process stages,and even ordinary quality practitioners who lack of expertise in theoretical analysis can implement regression estimation and neural computing readily.

Analysis of the Effect of Subgroup Size on the X-Bar Control Chart Using Forensic Science Laboratory Sample Influx Data

This paper analyzes the effect of subgroup size on the x-bar chart characteristics using sample influx (SIF) into forensic science laboratory (FSL). The characteristics studied include changes in out-or-control points (OCP), upper control limit UCLx, and zonal demarcations. Multi-rules were used to identify the number of out-of-control-points, Nocp as violations using five control chart rules applied separately. A sensitivity analysis on the Nocp was applied for subgroup size, k, and number of sigma above the mean value to determine the upper control limit, UCLx. A computer code was implemented using a FORTRAN code to create x-bar control-charts and capture OCP and other control-chart characteristics with increasing k from 2 to 25. For each value of k, a complete series of average values, Q(p), of specific length, Nsg, was created from which statistical analysis was conducted and compared to the original SIF data, S(t). The variation of number of out-of-control points or violations, Nocp, for different control-charts rules with increasing k was determined to follow a decaying exponential function, Nocp = Ae–α, for which, the goodness of fit was established, and the R2 value approached unity for Rule #4 and #5 only. The goodness of fit was established to be the new criteria for rational subgroup-size range, for Rules #5 and #4 only, which involve a count of 6 consecutive points decreasing and 8 consecutive points above the selected control limit (σ/3 above the grand mean), respectively. Using this criterion, the rational subgroup range was established to be 4 ≤ k ≤ 20 for the two x-bar control chart rules.

Top-down control on major groups of global marine diazotrophs

Dinitrogen(N_(2))fixed by a group of prokaryotes(diazotrophs)is the dominant process adding bioavailable nitrogen into the ocean.Although it has been intensively studied how N_(2) fixation is controlled by resources(bottom-up factors),it is unclear whether the grazing(top-down control)effectively impacts growth and distribution of different diazotroph groups.In this study,we evaluate this question by conducting log-log regression of diazotroph biomass onto corresponding N_(2) fixation rates in the global ocean.The slope of the regression for Trichodesmium is~0.8,indicating that a small portion of the increase in N_(2) fixation does not accumulate as its biomass.This leads to a conclusion that Trichodesmium is under a substantial top-down control,although bottom-up control still dominates.We also analyze the residuals of the regression in the North Atlantic,concluding that free trichomes of Trichodesmium are subject to stronger top-down control than its colonies.The weak correlation between the biomass and N_(2) fixation of unicellular cyanobacterial diazotrophs indicates that the degree of top-down control on this type of diazotrophs varies greatly.The analyses obtain unrealistic results for diatom-diazotroph assemblages due to complicated nitrogen sources of these symbioses.Our study reveals the variability of top-down control among different diazotroph groups across time and space,suggesting its importance in improving our understandings of ecology of diazotrophs and predictions of N_(2) fixation in biogeochemical models.Measurements of size-specific N_(2) fixation rates and growth rates of different diazotroph groups can be useful to more reliably analyze the top-down control on these key organisms in the global ocean.

Control Charts for the Shape Parameter of Power Function Distribution under Different Classical Estimators

In practice,the control charts for monitoring of process mean are based on the normality assumption.But the performance of the control charts is seriously affected if the process of quality characteristics departs from normality.For such situations,we have modified the already existing control charts such as Shewhart control chart,exponentially weighted moving average(EWMA)control chart and hybrid exponentially weighted moving average(HEWMA)control chart by assuming that the distribution of underlying process follows Power function distribution(PFD).By considering the situation that the parameters of PFD are unknown,we estimate them by using three classical estimation methods,i.e.,percentile estimator(P.E),maximum likelihood estimator(MLE)and modified maximum likelihood estimator(MMLE).We construct Shewhart,EWMA and HEWMA control charts based on P.E,MLE and MMLE.We have compared all these control charts using Monte Carlo simulation studies and concluded that HEWMA control chart under MLE is more sensitive to detect an early shift in the shape parameter when the distribution of the underlying process follows power function distribution.

The New Neutrosophic Double and Triple Exponentially Weighted Moving Average Control Charts

The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average(NEWMA)chart.In this study,two control charts are designed under the uncertain environment or neutrosophic statistical interval system,when all observations are undermined,imprecise or fuzzy.These are termed neutrosophic double and triple exponentially weighted moving average(NDEWMA and NTEWMA)control charts.For the proficiency of the proposed chart,Monte Carlo simulations are used to calculate the run-length characteristics(such as average run length(ARL),standard deviation of the run length(SDRL),percentiles(P_(25),P_(50),P_(75)))of the proposed charts.The structures of the proposed control charts are more effective in detecting small shifts while these are comparable with the other existing charts in detecting moderate and large shifts.The simulation study and real-life implementations of the proposed charts show that the proposed NDEWMA and NTEWMA charts perform better in monitoring the process of road traffic crashes and electric engineering data as compared to the existing control charts.Therefore,the proposed charts will be helpful in minimizing the road accident and minimizing the defective products.Furthermore,the proposed charts are more acceptable and actual to apply in uncertain environment.

Enhancing Software Process Management through Control Charts

In software development life cycle, Software Process Management (SPM) acts as a significant part throughout the execution of project. In this study, the application of control chart for analyzing the stability of software process and defects in the software product is discussed. This paper will discuss the analyzing impact or collision of rework effort, defect density, inspection performance and productivity by using control charts. This paper also explains the benefits and challenges of using control charts in software organization.

Economic Design of & S Control Charts Based on Taguchi's Loss Function and Its Optimization

Much research effort has been devoted to economic design of X ＆ S control charts,however,there are some problems in usual methods.On the one hand,it is difficult to estimate the relationship between costs and other model parameters,so the economic design method is often not effective in producing charts that can quickly detect small shifts before substantial losses occur;on the other hand,in many cases,only one type of process shift or only one pair of process shifts are taken into consideration,which may not correctly reflect the actual process conditions.To improve the behavior of economic design of control chart,a cost ＆ loss model with Taguchi＇s loss function for the economic design of X ＆ S control charts is embellished,which is regarded as an optimization problem with multiple statistical constraints.The optimization design is also carried out based on a number of combinations of process shifts collected from the field operation of the conventional control charts,thus more hidden information about the shift combinations is mined and employed to the optimization design of control charts.At the same time,an improved particle swarm optimization（IPSO） is developed to solve such an optimization problem in design of X ＆ S control charts,IPSO is first tested for several benchmark problems from the literature and evaluated with standard performance metrics.Experimental results show that the proposed algorithm has significant advantages on obtaining the optimal design parameters of the charts.The proposed method can substantially reduce the total cost（or loss） of the control charts,and it will be a promising tool for economic design of control charts.