Statistical Process Control Theory Research Articles

Sensitivity and reliability comparisons of EWMA mean control chart based on robust scale estimators under non‐normal process: COVID data application

AbstractRobust control charts are getting vital importance in statistical process control theory as they are insensitive to the departure from normality. Therefore, the main objective of current work is to determine the effects of non‐normal process on the Exponentially Weighted Moving Average (EWMA) control chart. To achieve this goal, the sensitivity and reliability comparisons are made under the non‐normal process by comparing five robust M‐scale estimators, suggested in literature to modify the EWMA control limits for monitoring process mean and on the basis of percentile bootstrap estimator. The paper addresses the run length (RL) distribution of a robust EWMA control chart under the non‐normal process for which the exponential distribution is used as non‐normal process. The standard deviation of RL, out‐of‐control average run length (ARL), and shift detection probabilities are examined to assess the sensitivity and reliability of robust EWMA control charts for mean of monitoring process. The results of this research indicate that the classical EWMA control chart's performance is substantially impacted by the non‐normal distribution and the proposed EWMA control charts show higher sensitivity than classical one in terms of having smaller values of out‐of‐control ARLs. A real‐life example from the medical sciences field is provided the practical usage of the proposed control charts. The simulation analysis and practical example have shown that the suggested control charts are effective in quickly monitoring the out‐of‐control process.

Measuring the Development Progress of Least Developed Countries: In the Context of World Development

ABSTRACT This article measures the ten-year development progress of 47 least developed countries (LDCs) based on comparing the development performances of 217 countries and economies. A methodology based on the theory of statistical process control is used to evaluate development performances in three dimensions: per capita income, life expectancy, and education. This methodology uses a pair of average and standard deviation charts to measure each LDC’s degree of growth and stability of growth in each dimension of development, and uses the three standard deviation limits in each chart to identify exceptional development performances that are outside the upper or lower limit. This article has three key findings on the exceptional development performances of LDCs in the context of world development over 2010–2019: (1) Seventeen LDCs achieved exceptionally high increases in life expectancy; (2) one LDC achieved exceptionally high growth in per capita income, but two LDCs had exceptionally low performances in the growth of per capita income; and (3) six LDCs experienced exceptionally unstable growth of per capita income. These findings shed some light on the future development priorities for LDCs. The reliability of analysis is also discussed.

Detection of Abnormal Data in GNSS Coordinate Series Based on an Improved Cumulative Sum

The global navigation satellite system (GNSS), as a high-time resolution and high-precision measurement technology, has been widely used in the field of deformation monitoring. Owing to the influence of uncontrollable factors, there are inevitably some abnormal data in the GNSS monitoring series. Thus, it is necessary to detect and identify abnormal data in the GNSS monitoring series to improve the accuracy and reliability of the deformation disaster law analysis and warning. Many methods can be used to detect abnormal data, among which the statistical process control theory, represented by the cumulative sum (CUSUM), is widely used. CUSUM usually constructs statistics and determines control limits based on the threshold criteria of the average run length (ARL) and then uses the control limits to identify abnormal data in CUSUM statistics. However, different degrees of the ‘trailing’ phenomenon exist in the interval of abnormal data identified by the algorithm, leading to a higher false alarm rate. Therefore, we propose an improved CUSUM method that uses breaks for additive season and trend (BFAST) instead of ARL-based control limits to identify abnormal data in CUSUM statistics to improve the accuracy of identification. The improved CUSUM method is used to detect abnormal data in the GNSS coordinate series. The results show that compared with CUSUM, the improved CUSUM method shows stronger robustness, more accurate detection of abnormal data, and a significantly lower false alarm rate.

Applying statistical process control to teaching quality assurance at higher education institutions

Although student evaluation of teaching is an important part of teaching quality assurance at higher education institutions, no absolute standards exist for assessing each course’s teaching evaluation rating after teaching evaluation. In this paper, we develop a novel method of using average and standard deviation charts ( charts), which are based on the theory of statistical process control, to properly assess every course’s teaching evaluation rating. Through an application of teaching performance assessment at a management school, this research demonstrates a useful contribution to enhancing the practice of teaching and teaching performance assessment: Using charts’ three-sigma limits (which are established based on a school’s historical teaching evaluation data) as robust performance assessment standards, this method can provide a reliable assessment result on every course’s teaching evaluation rating; such assessment is useful for helping teachers and administrators reliably identify the low-performance courses in each semester for making timely improvements.

A study on the application of control chart in healthcare

The most concerning issues in the healthcare system will always be quality control and quality improvement as they are significant to the health condition of the patient. A quality statistical tool such as statistical process control (SPC) charts will be efficient and highly effective in reducing the sources of variation within the healthcare process and in monitoring or controlling improvement of the process. The control chart is a statistical process control methodology designed to evaluate the process improvement or change in the manufacturing industry and is being implemented gradually in the healthcare sector. This will enable healthcare organizations to prevent unnecessary investment or spending in any changes that sound good but do not have any positive impact on real progress or improvement. When there is greater participation of humans in healthcare, the risks of error are also greater. Control charts help determine the source of error by differentiating the common and special cause of variation, each requiring a different response from healthcare management. This paper intends to deliver an overview of SPC theory and to explore the application of SPC charts by presenting a few examples of the implementation of control charts to common issues in the healthcare sector. After a brief overview of SPC in healthcare, the selection and construction of the two widely used control charts (Individuals and Moving Range chart, U chart) were adopted and illustrated by using the example from healthcare.

Co-digestión anaerobia en modo semi-continuo de residuos de vegetales y estiércol de vaca: un estudio de estabilidad de proceso

The anaerobic digestion of vegetable waste (VW) often shows the accumulation of fatty acids and low buffering capacity that promotes instability and low methane productivity. This work evaluated the anaerobic co-digestion of VW with cow manure (CM) as a strategy to improve the process stability. As a reaction system, a 4 L semi-continuous stirred tank reactor with an HRT of 20 days and fed with a substrate formulation of 40 g of VS was used in two periods: 34 days of VW mono-digestion and 26 days of VW:CM co-digestion. The mono and co-digestion processes were numerically evaluated through three analysis tools: a proposed co-digestion model embedded in the Anaerobic Digestion Model No. 1 structure, statistical process control theory, and modeling the pH dynamics as the response of a first-order linear system to an impulse manipulation. The mono-digestion process showed productivity of 0.381 L CH4 L digester-1 d-1, which increased by 14 % during co-digestion. The results also indicated that in VW:CM co-digestion, the pH dynamics presents a slower response to the daily feed induced by pulses, keeping the values of this parameter within the statistical stability range; as well as the early warning indicator IA/BA (ratio between intermediate and bicarbonate alkalinity) outside the failure thresholds. It was shown that the addition of CM to a mono-digestion of VW increases the buffer capacity of the system and the production of CH4, promoting a stable and efficient process.

A robust approach for assessing teaching performance during regular reviews

ABSTRACT Although teachers at many colleges and universities receive regular performance reviews (e.g. annual reviews), there are no absolute standards regarding what teaching performance in terms of teaching evaluation ratings during the review period is satisfactory. To address this issue, in this paper we propose a new two-step approach to construct average and standard deviation charts ( charts), which are based on the theory of statistical process control, for annual teaching performance reviews at a management school: first, we use the school’s all historical data of teaching evaluation ratings to build charts; then, we plot each teacher’s teaching evaluation ratings during the annual review period into the established charts for performance assessment. School administrators and teachers can use this generic and robust approach to identify high, normal, and low teaching performance during regular reviews for helping teachers make timely improvements or maintain good teaching performance.

A robust interpretation of teaching evaluation ratings

There are no absolute standards regarding what teaching evaluation ratings are satisfactory. It is also problematic to compare teaching evaluation ratings with the average or with a cutoff number to determine whether they are adequate. In this paper, we use average and standard deviation charts ( charts), which are based on the theory of statistical process control, to assess five-year teaching evaluation ratings at a management school. We demonstrate that the upper and lower three-sigma limits of charts are sufficiently robust to unbiasedly identify comparatively exceptional teaching evaluation ratings. This research work makes two recommendations concerning teaching and teaching evaluations: teachers should look for special causes, which contribute to exceptional teaching evaluation ratings outside the three-sigma limits, for the purpose of making improvements or maintaining good teaching performance; if teaching evaluation ratings are used for decision-making, the teaching evaluation ratings within the three-sigma limits represent normal teaching performance and thus should not be treated differently.

Getting high added-value from sampling

Author Summary: Determination of the complete sampling distribution (Lyman, 2014), as opposed to estimation of the sampling variance, represents a significant advance in sampling theory. This is one link that has been missing for sampling results to be used to their full potential. In particular, access to the complete sampling distribution provides opportunities to bring all the concepts and risk assessment tools from statistical process control (SPC) into the production and trading of mineral commodities, giving sampling investments and results their full added-value. The paper focuses on the way by which sampling theory, via the complete sampling distribution, interfaces with production and statistical process control theory and practice. The paper evaluates specifically the effect of using the full sampling distribution on the Operating Characteristic curve and control charts’ Run Length distributions, two SPC cornerstones that are essential for quality assurance and quality control analysis and decision-making. It is shown that departure from normality of the sampling distribution has a strong effect on SPC analyses. Analysis of the Operating Characteristic curve for example shows that assumption of normality may lead to erroneous risk assessment of the conformity of commercial lots. It is concluded that the actual sampling distribution should be used for quality control and quality assurance in order to derive the highest value from sampling.

A Precision Compensation Method for Space Manipulator Trajectory Planning Based on Particle Filter

A precision compensation method for space manipulator trajectory planning is presented for improving the accuracy during the execution of a task. First, the control process is described by using a state space equation. Second, process parameters are estimated in real-time using particle filter. Then, the system’s real time operational reliability is calculated based on the state space equation and process parameters. Finally, a control variable compensation strategy is given based on the theory of Statistical Process Control. Simulations show that this method effectively improves the accuracy and stability of space manipulator control system.

An Anomaly Identification Method of Coal Gas Pre-Drainage Parameters Based on T<sup>2</sup> Control Chart

The T2statistic is one important indicator of statistical process control theory to identify anomalies of the multivariate industrial process. In the research field of the coal gas pre-drainage process control, previous achievements mainly based on the univariate control chart, which leaded to huge workload and facilitated some human errors. Against these problems, a more comprehensive and easy-to-use method based on the T2statistic was proposed. First at all, the basic thought and the principle of T2control chart was elaborated. Secondly, the data structure and data samples were provided after their principle component analysis. Finally, the multivariate control chart of coal gas pre-drainage process was established. Results show that the proposed anomaly identification method can integrate dozen of univariate control charts into one. Then technicians needn’t deal with many control charts in the same time and many human errors can be avoided.

Research on Control Method Based on Real-Time Operational Reliability Evaluation for Space Manipulator

A control method based on real-time operational reliability evaluation for space manipulator is presented for improving the success rate of a manipulator during the execution of a task. In this paper, a method for quantitative analysis of operational reliability is given when manipulator is executing a specified task; then a control model which could control the quantitative operational reliability is built. First, the control process is described by using a state space equation. Second, process parameters are estimated in real time using Bayesian method. Third, the expression of the system's real-time operational reliability is deduced based on the state space equation and process parameters which are estimated using Bayesian method. Finally, a control variable regulation strategy which considers the cost of control is given based on the Theory of Statistical Process Control. It is shown via simulations that this method effectively improves the operational reliability of space manipulator control system.

Optimization of Wind Power and Its Variability With a Computational Intelligence Approach

An optimization model is presented for maximizing the generation of wind power while minimizing its variability. In the optimization model, data-driven approaches are used to model the wind-power generation process based on industrial data. A new constraint is developed for governing the data-driven wind-power generation model based on physics and statistical process control theory. Since the wind-power model is nonparametric, computational intelligence algorithms are utilized to solve the optimization model. Computer experiments are designed to compare the performance of computational intelligence algorithms. The improvement in the generated wind power and its variability is demonstrated with the computational results.

High density print circuit board line width measurement algorithm based on statistical process control theory

The high-density circuit board width detection algorithms are proposed. The detection algorithms include the pre-processing algorithm of measured image line width and the final detection algorithm. The noise suppression of morphological erosion operator based on partial differential equations (PDE) is presented. The mathematical statistical methods and clustering segmentation edge are utilized to measure the upper and lower line width of high-density circuit board with sub-pixel fit to improve detection accuracy. Experiments show that the algorithm can accurately measure the line width distance of circuit board. And measure system analysis results show that the measured data accord with the statistical process control theory and are significant for guiding practice.

Development of a technique based on multi-spectral imaging for monitoring the conservation of cultural heritage objects

A new approach for monitoring the state of conservation of cultural heritage objects surfaces is being developed. The technique utilizes multi-spectral imaging, multivariate analysis and statistical process control theory for the automatic detection of a possible deterioration process, its localization and identification, and the wavelengths most sensitive to detecting this before the human eye can detect the damage or potential degradation changes occur. A series of virtual degradation analyses were performed on images of parchment in order to test the proposed algorithm in controlled conditions. The spectral image of a Dead Sea Scroll (DSS) parchment, IAA (Israel Antiquities Authority) inventory plate # 279, 4Q501 Apocryphal Lamentations B, taken during the 2008 Pilot of the DSS Digitization Project, was chosen for the simulation.

Technique Based on LED Multispectral Imaging and Multivariate Analysis for Monitoring the Conservation State of the Dead Sea Scrolls

The aim of this project is the development of a noninvasive technique based on LED multispectral imaging (MSI) for monitoring the conservation state of the Dead Sea Scrolls (DSS) collection. It is well-known that changes in the parchment reflectance drive the transition of the scrolls from legible to illegible. Capitalizing on this fact, we will use spectral imaging to detect changes in the reflectance before they become visible to the human eye. The technique uses multivariate analysis and statistical process control theory. The present study was carried out on a "sample" parchment of calfskin. The monitoring of the surface of a commercial modern parchment aged consecutively for 2 h and 6 h at 80 °C and 50% relative humidity (ASTM) was performed at the Imaging Lab of the Library of Congress (Washington, DC, U.S.A.). MSI is here carried out in the vis-NIR range limited to 1 μm, with a number of bands of 13 and bandwidths that range from about 10 nm in UV to 40 nm in IR. Results showed that we could detect and locate changing pixels, on the basis of reflectance changes, after only a few "hours" of aging.

Analysis of Assessment Method about the Flight Technical Error Based on Johnson Curves

Ensured navigation performance and standardized functionality of RNP allow new procedures to be designed for airports where infrastructure or terrain makes it difficult or impossible to use some runways. Aircraft must be approved as being able to meet the RNP. Based on theory of statistical process control, this paper evaluates the flight technical error in optimal curve fitting of the distributions Johnson's. The concrete parameter selection, the selection process Johnson curves and a concrete model are given. The results of analysis can be used to the evaluation of flight technical error.