On the Comparative Efficiency of Change Point Detection in Multivariate Technological Processes Using Multidimensional Double Control Charts

Background Although benchmarks and control charts are basic quality improvement tools, few surgeons use them to monitor surgical site infection (SSI). Obstacles to widespread acceptance include: (1) small denominators, (2) complexities of adjusting for patient risk and (3) scepticism about their true purpose (cost cutting, surgical privilege determination or improving outcomes). Methods The application of benchmark charts (using US national SSI rates as limits) and control charts (using facility rates as limits) was studied in 51 hospitals submitting data to the AICE National Database Initiative. SSI rates were risk adjusted by calculating a new statistic, the standardized infection ratio (SIR), based on the risk index suggested by the Centers for Disease Control National Nosocomial Infection Surveillance Study. Fourteen different types of control chart were examined and 115 suspiciously high or low monthly rates were flagged. Participating hospital epidemiologists investigated and classified each flag as ‘a real problem’ (potentially preventable) or ‘not a problem’ (beyond the control of personnel at this facility). Results None of the standard, widely recommended, control charts studied showed practical value for identifying either preventable rate increases or outbreaks (clusters due to a single organism). On the other hand, several types of risk-adjusted control chart based on the SIR correctly identified most true opportunities for improvement. Sensitivity, specificity and receiver–operator characteristic (ROC) analysis revealed that the XmR chart of monthly SIRs would be useful in hospitals with smaller surgical volumes (ROC area = 0·732, P = 0·001). For larger hospitals, the most sensitive and robust SIR chart for real-time monitoring of surgical infections was the mXmR chart (ROC area = 0·753, P = 0·0005).

A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts

This study considers the construction of control charts when outliers could occur in the data. Simply increasing the width of control limits will make it more difficult to detect an out-of-control signal. This study aims to determine weights for each observation by measuring their respective distances to the average. The study proposes a retrospective weighted average and weighted inter-quartile range (WIQR) control chart based on the normal distribution that accounts for the false alarm rate, special causes, outliers, and a combination of special causes and outliers that might exist within the process. In addition to validating the detection effectiveness of the proposed control charts, this study also compares various average-type and range-type control charts for detecting the outliers and special causes. The proposed control charts show superior detection effectiveness in four tested scenarios compared to other types of control charts. Finally, the study also illustrates the practical application of the control charts for measuring the tensile strength of a deformed bar in coil. [Received 10 February 2014; Revised 21 August 2015; Revised 3 November 2015; Accepted 14 December 2015]

Control charts are a valuable assessment tool in the healthcare industry. The ease of use of these trending charts is crucial to obtain timely important results with minimum time and efforts. The current case showed analysis of non-normal data to obtain control charts with useful output without using exhaustive different means of transformation and/or omitting aberrant numbers. Raw results for the quality of purified water from water treatment plant that converts municipal water to purified water were collected from two points-of-use - (ζ and Ψ). Data gathered were conductivity and total organic carbon (TOC) measurements. The statistical processing and control charts were done using commercial statistical software package. Statistical analysis of data showed that conductivity and TOC results of both points did not follow Gaussian distribution except TOC of point Ψ where it passed normality test, but they were closest to other distributions. There were several observations of outlier values from the results. Moreover, data normalization did not improve after removal of the extreme values. Data were switched to be interpreted using Laney-modified attribute control charts and compared with the original results drawn using individual-moving range (I-MR). Interestingly, both types of control charts agreed regarding control limits and some alarm points. I-MR and Laney-attribute charts could be used for non-normal data with unusual other types of distributions that may not be suitable for conventional types of control charts with the variable charts possess greater sensitivity of alarm detection over the attribute charts.

ABSTRACTThe study proposes a weighted bootstrap control chart based on the strata and determines suitable weights for each stratum by measuring the relative error of estimated Type I risks (Err). In addition to validating the detection effectiveness of the proposed control chart, this study also compares several types of control charts for detecting the Type I, Type II risks, and median run lengths. The proposed control chart shows superior detection effectiveness in six skewed distributions compared to other types of control charts.

Early detection of fuel leakage at service stations with underground petroleum storage systems is a crucial task to prevent catastrophic hazards. Current data-driven fuel leakage detection methods employ offline statistical inventory reconciliation, leading to significant detection delays. Consequently, this can result in substantial financial loss and environmental impact on the surrounding community. In this paper, we propose a novel framework called memory-based online change point detection (MOCPD) which operates in near real-time, enabling early detection of fuel leakage. MOCPD maintains a collection of representative historical data within a size-constrained memory, along with an adaptively computed threshold. Leaks are detected when the dissimilarity between the latest data and historical memory exceeds the current threshold. An update phase is incorporated in MOCPD to ensure diversity among historical samples in the memory. With this design, MOCPD is more robust and achieves a better recall rate while maintaining a reasonable precision score. We have conducted a variety of experiments comparing MOCPD to commonly used online change point detection (CPD) baselines on real-world fuel variance data with induced leakages, actual fuel leakage data and benchmark CPD datasets. Overall, MOCPD consistently outperforms the baseline methods in terms of detection accuracy, demonstrating its applicability to fuel leakage detection and CPD problems.

In many cases, a $\overline X $ control chart based on a performance variable is used in industrial fields. Typically, the control chart monitors the measurements of a performance variable itself. However, if the performance variable is too costly or impossible to measure, and a less expensive surrogate variable is available, the process may be more efficiently controlled using surrogate variables. In this paper, we present a model for the economic statistical design of a VSI (Variable Sampling Interval) $\overline X $ control chart using a surrogate variable that is linearly correlated with the performance variable. We derive the total average profit model from an economic viewpoint and apply the model to a Very High Temperature Reactor (VHTR) nuclear fuel measurement system and derive the optimal result using genetic algorithms. Compared with the control chart based on a performance variable, the proposed model gives a larger expected net income per unit of time in the long-run if the correlation between the performance variable and the surrogate variable is relatively high. The proposed model was confined to the sample mean control chart under the assumption that a single assignable cause occurs according to the Poisson process. However, the model may also be extended to other types of control charts using a single or multiple assignable cause assumptions such as VSS (Variable Sample Size) $\overline X $ control chart, EWMA, CUSUM charts and so on.

In statistics and engineering the problem of change point detection in dynamical systems can be addressed using the class of sequential detection rules called limited generalized likelihood ratio (GLR) schemes. In this paper, some open problems concerning the statistical theory and practical implementation of these procedures are discussed. A method to select the window size and the threshold value, based on the joint use of Monte Carlo simulation and theoretical results, is proposed and illustrated through an example.

Wikipedia has been turned into an immensely popular crowd-sourced encyclopedia for information dissemination on numerous versatile topics in the form of subscription free content. It allows anyone to contribute so that the articles remain comprehensive and updated. For enrichment of content without compromising standards, the Wikipedia community enumerates a detailed set of guidelines, which should be followed. Based on these, articles are categorized into several quality classes by the Wikipedia editors with increasing adherence to guidelines. This quality assessment task by editors is laborious as well as demands platform expertise. As a first objective, in this paper, we study evolution of a Wikipedia article with respect to such quality scales. Our results show novel non-intuitive patterns emerging from this exploration. As a second objective we attempt to develop an automated data driven approach for the detection of the early signals influencing the quality change of articles. We posit this as a change point detection problem whereby we represent an article as a time series of consecutive revisions and encode every revision by a set of intuitive features. Finally, various change point detection algorithms are used to efficiently and accurately detect the future change points. We also perform various ablation studies to understand which group of features are most effective in identifying the change points. To the best of our knowledge, this is the first work that rigorously explores English Wikipedia article quality life cycle from the perspective of quality indicators and provides a novel unsupervised page level approach to detect quality switch, which can help in automatic content monitoring in Wikipedia thus contributing significantly to the CSCW community.

Using control charts to monitor quality of hospital care with administrative data

SUMMARY When lack of control is detected on a control chart, the usual recommendation is to ‘search for an assignable cause’, or ‘take remedial action’, etc., without specific advice on how to proceed. One important situation is a continuously adjustable machine whose mean output is being monitored by a Shewhart -chart. When lack of control is indicated by a signal from this or some other type of control chart, a natural action is to make an adjustment to re-centre the process mean. In this paper we propose three simple methods for determining the amount of adjustment and report on analytical investigations of their properties Somewhat different from other literature in this area is our desire to avoid overadjustment of the process mean, viewing this as a more serious error than underadjustment.

When control charts are used to monitor processes to detect special causes, it is usually assumed that a special cause will produce a sustained shift in a process parameter that lasts until the shift is detected and the cause is removed. However, some special causes may produce a transient shift in a process parameter that lasts only for a short period of time. Control charts are usually based on samples of n ≥ 1 observations using a sampling interval of fixed length, say d. When n > 1, the usual practice, based on the so-called rational subgroups concept, is to take a concentrated sample at one time point at the end of the sampling interval d, but another option is to disperse the sample over the interval d. In this paper, we investigate the question of whether it is better to use n = 1, or to use n > 1 and either concentrated or dispersed samples. The objective of monitoring is assumed to be the detection of special causes that may produce either a sustained or transient shift in the process mean %mU and/or process standard deviation σ. It is assumed that the sampling rate in terms of the number of observations per unit time is fixed, so that the ratio n/d is fixed. The best sampling strategy depends on the type of control chart being used, so Shewhart and cumulative sum (CUSUM) charts are considered. For both types of control charts, a combination of two charts is investigated; one chart is designed to monitor μ, and the other is designed to monitor σ. The overall conclusion is that it is best to take samples of n = 1 and use a CUSUM chart combination. The Shewhart chart combination with the best overall performance is based on n > 1, but this combination has inferior statistical performance compared with the CUSUM chart combination.

This paper presents a study for improving measurement procedures on concrete dams based on the development of behavior prediction models using multiple regression and control charts. The series of displacements of the TA-2 concrete block crest of the Tucuruí Hydroelectric Power Plant, the largest Brazilian hydroelectric power plant, was taken as an example of application. The methodology that was used generated a behavior prediction model that is in good agreement with the measured displacements. Two types of control charts were tested, both of which identified the change in structural behavior of the block.

[ES] En este trabajo se han diseñado gráficos de control con el objetivo de detectar anomalías en los datos de mortalidad infantil del área urbana en Ecuador. A fin de diseñar estos gráficos, se construyeron modelos de regresión binomial negativa para la tasa de mortalidad anual de niños y niñas con edades comprendidas entre uno y menos de cinco años. Con los modelos construidos se elaboraron dos tipos de gráficos de control: el primero, una versión EWMA (Exponentially Weighted Moving Average) de los residuos de desviación de los modelos construidos y el segundo, un gráfico que monitorea la media de muertes a través de la estimación obtenida por el modelo de regresión. Para ambos gráficos se ha considerado un nivel de confianza aproximado de 95 por ciento. El gráfico EWMA fue más eficiente en detectar los subregistros que suelen presentarse en las muertes de niños del área urbana de Ecuador

This paper presents a detailed explanation about the types of control charts or statistical adjustment, their importance, the qualities of their usage and the method of how to work with them. Besides, it presents the types, characteristics, and usage of the concrete blocks as a theoretical side of the paper. As an applied side of the paper, it introduces the method of using the control charts in determining the suitable decisions about the production process of manufacturing concrete blocks, and how it would help to show the actual variation in the produced units. Identification of defected units in an early stage of production will save materials and production costs.