基于Statistical Process Control风险等级判定及神经网络模型构建珠海市传染病指数

目的建立珠海市传染病指数预报模型,为传染病风险预测预报提供思路。方法利用统计过程控制(SPC)的控制下限、中线和控制上限划分全市2014—2017年以周次为时间计量单位的流感样病例比例、手足口病及其他感染性腹泻发病率的风险等级(布雷图指数采用5、10、20判定)。运用长短时记忆神经网络模型(LSTM)和自回归移动平均模型(ARIMA)对2018年15~19周数据进行预测。计算传染病指数并将预测值与实际值对比进而评估预测一致性。结果珠海市手足口病发病率LSTM模型中,测试集MSE为9.0441,RMSE为3.0073,训练集MSE为1.1812,RMSE为1.0868。其余模型在训练集和测试集均表现良好,没有出现过拟合现象。风险指数等级预测与实际值对比,预测一致率为96.0%。结论利用SPC划分风险等级,运用LSTM等构建传染病指数预测模型可行。

Bayesian Reliability——Growth Analysis for Statistical of Diverse Population Based on Non-homogeneous Poisson Process

New armament systems are subjected to the method for dealing with multi-stage system reliability-growth statistical problems of diverse population in order to improve reliability before starting mass production. Aiming at the test process which is high expense and small sample-size in the development of complex system, the specific methods are studied on how to process the statistical information of Bayesian reliability growth regarding diverse populations. Firstly, according to the characteristics of reliability growth during product development, the Bayesian method is used to integrate the testing information of multi-stage and the order relations of distribution parameters. And then a Gamma-Beta prior distribution is proposed based on non-homogeneous Poisson process（NHPP） corresponding to the reliability growth process. The posterior distribution of reliability parameters is obtained regarding different stages of product, and the reliability parameters are evaluated based on the posterior distribution. Finally, Bayesian approach proposed in this paper for multi-stage reliability growth test is applied to the test process which is small sample-size in the astronautics filed. The results of a numerical example show that the presented model can make use of the diverse information synthetically, and pave the way for the application of the Bayesian model for multi-stage reliability growth test evaluation with small sample-size. The method is useful for evaluating multi-stage system reliability and making reliability growth plan rationally.

New Method for Multivariate Statistical Process Monitoring

A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis （FDA） is used to derive a feature discriminant direction （FDD） between each normal and fault operations,and each FDD thus decided constructs the feature space of each fault operation.Individuals control charts （XmR charts） are used to monitor multivariate processes using the process data projected onto feature spaces.Upper control limit （UCL） and lower control limit （LCL） on each feature space from normal process operation are calculated for XmR charts,and are used to distinguish fault from normal.A variation trend on an XmR chart reveals the type of relevant fault operation.Applications to Tennessee Eastman simulation processes show that this proposed method can result in better monitoring performance than principal component analysis （PCA）-based methods and can better identify step type faults on XmR charts.

From statistical analysis to process optimization during cladding using a Nd:YAG laser

Application of statistical methods to optimize the process parameters was achieved by employing full factorial design of experiments,which was accomplished by cladding using stepwise ramped laser power.The correlations between clad geometry and dilution(clad characteristics)and the main process parameters laser power(P_(l)),cladding speed(v_(c)),the powder feed rate(m)were obtained through application of variance analysis technique(ANOVA).The obtained correlations between the main processing parameters and the clad characteristics are discussed and a statistical model was developed.The desirability investigations using the developed statistical model were performed by considering the clad geometry,aspect ratio,dilution and hardness.Optimal parameters for cladding Stellite 6 on AISI 420 steel substrate and for cladding Nucalloy 488V on S355 J2 steel substrate were obtained.The optimal processing parameters can be applied to clad other materials with similar chemical compositions.

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

核主管部件分析(KPCA ) 方法采用开始的几个核主管部件(KPC ) ，它为过程监视显示正常观察的大多数变化信息，但是不能反映差错信息。在这研究，敏感内核主管部件分析(SKPCA ) 被建议改进监视性能的过程，即，错过了察觉率处理 T2 统计、摆平的预言错误 SPE 统计数值和还原剂的不和。T2 统计数值能被用来沿着每 KPC 直接测量变化并且分析察觉表演以及在一个过程捕获最有用的信息。随沿着每 KPC 的 T2 统计数值的变化率的计算， SKPCA 为进程监视选择敏感内核主管部件。一个模仿的简单系统和田纳西伊斯门过程被采用在联机监视上表明 SKPCA 的效率。结果显示监视表演显著地被改进。

A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing

To overcome the large time-delay in measuring the hardness of mixed rubber, rheological parameters were used to predict the hardness. A novel Q-based model updating strategy was proposed as a universal platform to track time-varying properties. Using a few selected support samples to update the model, the strategy could dramatically save the storage cost and overcome the adverse influence of low signal-to-noise ratio samples. Moreover, it could be applied to any statistical process monitoring system without drastic changes to them, which is practical for industrial practices. As examples, the Q-based strategy was integrated with three popular algorithms(partial least squares(PLS), recursive PLS(RPLS), and kernel PLS(KPLS)) to form novel regression ones, QPLS, QRPLS and QKPLS, respectively. The applications for predicting mixed rubber hardness on a large-scale tire plant in east China prove the theoretical considerations.

Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.

Integrated Statistical and Engineering Process Control Based on Smooth Transition Autoregressive Model

Traditional studies on integrated statistical process control and engineering process control (SPC-EPC) are based on linear autoregressive integrated moving average (ARIMA) time series models to describe the dynamic noise of the system.However,linear models sometimes are unable to model complex nonlinear autocorrelation.To solve this problem,this paper presents an integrated SPC-EPC method based on smooth transition autoregressive (STAR) time series model,and builds a minimum mean squared error (MMSE) controller as well as an integrated SPC-EPC control system.The performance of this method for checking the trend and sustained shift is analyzed.The simulation results indicate that this integrated SPC-EPC control method based on STAR model is effective in controlling complex nonlinear systems.

Multivariate Statistical Process Monitoring of an Industrial Polypropylene Catalyzer Reactor with Component Analysis and Kernel Density Estimation

Data-driven tools,such as principal component analysis(PCA)and independent component analysis (ICA)have been applied to different benchmarks as process monitoring methods.The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latent variables are independent.Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution.However,this kind of constraint cannot be satisfied by several practical processes.To ex- tend the use of PCA,a nonparametric method is added to PCA to overcome the difficulty,and kernel density esti- mation(KDE)is rather a good choice.Though ICA is based on non-Gaussian distribution information,KDE can help in the close monitoring of the data.Methods,such as PCA,ICA,PCA with KDE(KPCA),and ICA with KDE (KICA),are demonstrated and compared by applying them to a practical industrial Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.

A statistical RCL interconnect delay model taking account of process variations

As the feature size of the CMOS integrated circuit continues to shrink, process variations have become a key factor affecting the interconnect performance. Based on the equivalent Elmore model and the use of the polynomial chaos theory and the Galerkin method, we propose a linear statistical RCL interconnect delay model, taking into account process variations by successive application of the linear approximation method. Based on a variety of nano-CMOS process parameters, HSPICE simulation results show that the maximum error of the proposed model is less than 3.5%. The proposed model is simple, of high precision, and can be used in the analysis and design of nanometer integrated circuit interconnect systems.

STATISTICAL PROPERTY OF THRESHOLD-CROSSING FOR ZERO-MEAN-VALUED, NARROW-BANDED GAUSSIAN PROCESSES

Based on a comprehensive discussion of the calculation method for the threshold-crossing statistics of zero mean valued, narrow banded Gaussian processes of various practical engineering problems, including the threshold-crossing probability, average number of crossing events per unit time, mean threshold-crossing duration and amplitude, a new Simple numerical procedure is proposed for the efficient evaluation of mean threshold-crossing duration. A new dimensionless parameter, called the threshold-crossing intensity, is defined as a measure of the threshold-crossing severity, which is equal to the ratio of the product of average number of crossing events per unit time and mean threshold-crossing duration and amplitude over the threshold. It is found, by the calculation results for various combinations of stochastic processes and different thresholds, that the threshold-crossing intensity, irrelevant of the threshold and spectral density of the process, is dependent only on the threshold-crossing probability.

Optimization of Rolling Process for Bi(2223)/Ag Superconducting Tapes by a Statistical Method

Ag-sheathed (Bi,Pb)(2)SoCa(2)Cu(3)O(x) tapes were prepared by the powder-in-tube method. The influences of rolling parameters on superconducting characteristics of Bi(2223)/Ag tapes were analyzed qualitatively with a statistical method. The results demonstrate that roll diameter and reduction per pass significantly influence the properties of Bi(2223)/Ag superconducting tapes while roll speed does less and working friction the least. An optimized rolling process was therefore achieved according to the above results.

A Robust Statistical Batch Process Monitoring Framework and Its Application

In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.

Study on Quality and Safety Trust Early Warning of Dairy Products Based on Statistical Process Control

In order to solve such problems as lack of dynamic evaluation system in evaluation of quality and safety trust of dairy products,and weak awareness of prevention,it is necessary to introduce the statistical process control into the quality and safety trust evaluation system of dairy products,and establish quality and safety trust early warning model for dairy products,so as to determine the control limit of control chart and carry out early warning according to eight criteria. According to the empirical results,the statistical process control is helpful for finding the hidden process risks and providing the necessary basis for enterprises taking positive measures to raise the confidence of consumers.

Multivariate Statistical Process Monitoring and Control: Recent Developments and Applications to Chemical Industry

Multivariate statistical process monitoring and control (MSPM& C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper,The four-step procedure of performing MSPM &C for chemical process ,modeling of processes ,detecting abnormal events or faults,identifying the variable(s) responible for the faults and diagnosing the source cause for the abnormal behavior,is analyzed,Several main research directions of MSPM&C reported in the literature are discussed,such as multi-way principal component analysis (MPCA) for batch process ,statistical monitoring and control for nonlinear process,dynamic PCA and dynamic PLS,and on -line quality control by infer-ential models,Industrial applications of MSPM&C to several typical chemical processes ,such as chemical reactor,distillation column,polymeriztion process ,petroleum refinery units,are summarized,Finally,some concluding remarks and future considerations are made.

Statistical Control and Investigation of Capability of Process and Machine in Wire Cut Edm Process of Gas Turbine Blade Airfoil Tip

Statistical process control (spc), as one of the quality devices, can be help manufacturers improve the quality of their products in today’s competitive world. This research examines the statistical method of wire-cut electric discharge machining (WEDM) process of the turbine blade airfoil tip for control and consistency of the process. For this purpose, the standard deviation control chart, S, and the average data, , which are applied for identifying the acquired factors, have been used. Next, regarding the plan features, the manufacturing process is assessed to determine whether the products meet quality and the customer requirements or not. Therefore, the coefficients Cpk is applied which indicate the capability of the manufacturing process. Then, in order to produce high quality blades within the tolerance range, the capability of WEDM machine is examined, using coefficients CM. Finally, it is shown that in order to produce the desired product, the process can be controlled and fixed by using the statistical process control devices and inspecting the standard deviation of data and investigation of capability of process and machine.

Application of Statistical Process Control for Setting Action Thresholds as Quality Assurance of Dose Verifications in External Beam Radiotherapy

Purpose: To test the concept of Statistical Process Control (SPC) as a Quality Assurance (QA) procedure for dose verifications in external beam radiation therapy in conventional and 3D Conformal Radiotherapy (3D-CRT) treatment of cervical cancer. Materials and Methods: A study of QA verification of target doses of 198 cervical cancer patients undergoing External Beam Radiotherapy (EBRT) treatments at two different cancer treatment centers in Kenya was conducted. The target doses were determined from measured entrance doses by the diode in vivo dosimetry. Process Behavior Charts (PBC) developed by SPC were applied for setting Action Thresholds (AT) on the target doses. The AT set was then proposed as QA limits for acceptance or rejection of verified target doses overtime of the EBRT process. Result and Discussion: Target doses for the 198 patients were calculated and SPC applied to test whether the action limits set by the Process Behavior Charts could be applied as QA for verified doses in EBRT. Results for the two sub-groups of n = 3 and n = 4 that were tested produced action thresholds which are within clinical dose specifications for both conventional AP/PA and 3D-CRT EBRT treatment techniques for cervical cancer. Conclusion: Action thresholds set by SPC were within the clinical dose specification of ±5% uncertainty for both conventional AP/PA and 3D-CRT EBRT treatment techniques for cervical cancer. So the concept of SPC could be applied in setting QA action limits for dose verifications in EBRT.

Assessing the Awareness and Usage of Quality Control Tools with Emphasis to Statistical Process Control (SPC) in Ethiopian Manufacturing Industries

Introduction: The present work was devoted to assess the awareness and usage of quality control tools with the emphasis on statistical process control in Ethiopian manufacturing industries. Semi structured questionnaire has been employed to executive and technical managers of manufacturing industries of various size and specialism across the country. Stratified random sample method by region was used to select sample industries for the study. The samples used for this study are industries mainly from Oromiya, Addis Ababa, Tigray, Amara, SNNP and Diredawa regions proportional to their size of the available industries. Methods: Exploratory method and descriptive statistics was used for data analysis. Available documents and reports related to quality control policy of the selected companies were investigated. Results and Discussions: The number of manufacturing industries involved in this study was 44. Of the sampled manufacturing industries about 60% are from Oromiya and Addis Ababa regions. It has been reported that 100% of the respondents said that the importance of quality control tools is very important to their organizations’ productivity and quality improvement (Figure 3). Quality control professionals were also asked the extent to which quality control system is working in their industry and majority of the respondents (45%) have indicated that quality control system is working to some extent in their respective industries (Figure 18). Conclusions and Recommendations: Most of the quality department of the industries did not fully recognize the importance of statistical process control as quality control tools. This is mainly due to lack of awareness and motivation of the top managements, shortage of man power in the area, and others together would make it difficult to apply quality control tools in their organization. In general, the industries in Ethiopia are deficient in vigor and found to be stagnant hence less exposed to a highly competitive market and don’t adopt the latest quality control techniques in order to gain knowledge about systems to improve quality and operational performance. We conclude that quality management system has to be established as an independent entity with a real power and hence the quality control department which is responsible for quality can make an irreversible decision with respect to quality of any given product. Moreover, the concerned bodies (government and ministry of industries) should give attention and work together with universities to ensure how these statistical process control techniques could be incorporated in a curriculum of the universities at higher levels in degree and masters programs. Furthermore, different trainings which could improve quality and efficiency of their respective management system should be given as short and long term to the employees including top and middle managers found in various industries relevant to their process.

Statistical Analyses of the Effects of Welding Processes on Load, Extension and Hardness Properties of Welded Mild Steel Plates

Mild steel plates of thicknesses 0.5 mm,0.6 mm,0.7 mm,0.8 mm,0.9 mm and 1.0 mm were prepared as test samples.After welding with the developed welding robot and manual electric arc welding machine these test samples were subjected to Tensile Strength and Hardness tests.All data obtained including hardness,load and extension were analyzed and the data produced from electric arc welding operations,the robot welding operations and un-welded plates(control)were compared with one another.The statistical analyses of hardness,load and extension tests for developed welding robot,manual electric arc welding and un-welded(control)mild steel plates of different thicknesses were carried out.The results revealed that for hardness,the developed robot welding has the highest mean value of 115.30,standard deviation value of 14.32 and variance value of 205.06.The descriptive statistics of the load showed that the developed robot welding samples collectively have the lowest mean value of 2,536.85,standard deviation value of 704.21 and variance value of 495,911.72.The descriptive statistics of the extension in which the developed robot welding samples collectively have the lowest mean value of 1.29,standard deviation value of 0.43 and variance value of 0.18 were also determined.The result for hardness showed homogeneity of variance among hardness tests of the samples,which implies variation in the hardness test among the tests of the samples since p-value is 0.038.While the result for loads shows homogeneity of variance among loads of the samples in which the result reveals that there is no variation in the loads among the tests of the samples since p-value is 0.322.The result for extension shows homogeneity of variance among extensions of the samples in which it revealed that there is variation in the extensions among the tests of the samples since p-value is 0.011.The analysis of variance(ANOVA)test result revealed that there is a significant difference in the hardness of the samples in which developed robot welding operation gave the highest hardness compared with electric arc welding and un-welded(CONTROL)since p-value is 0.028.The ANOVA test result for load revealed that there is no significant difference in the loads of the samples since p-value is 0.51.The ANOVA test result of the extension shows that there is a significant difference in the extension of the samples in which developed robot welding operation gave the lowest extension compared with electric arc welding and un-welded(CONTROL)since p-value is 0.001.The results of hardness also showed the mean difference of 16.48 between developed robot welding and un-welded(CONTROL)samples and 7.26 between developed robot welding and electric arc welding samples.Finally,for extension the mean difference of-5.28 between developed robot welding and un-welded(CONTROL)samples and-1.22 between developed robot welding and electric arc welding samples were established.

Statistical Process Control in Flexible Manufacturing Systems

The statistical process control techniques used in flexible manufacturing systems arestudied in this paper.Control charts which can be used in the low volume production are pro-posed.The automatic recognizer of unnatural patterns for the control chart by using back-propagation neural network is also presented.