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A novel approach for biosurveillance using multiple data streams is presented. The proposed method is computationally simple, has rapid detection ability, and produces few false alarms. The proposed algorithm is applied to three popular statistical process control (SPC) charts: the Shewhart Chart, the EWMA, and the CUSUM. The proposed method collects disease counts from multiple data streams, computes charting statistics, and then compares these to empirical in-control distributions generated using bootstrap methods to decide whether to signal an alarm. As bootstrap methods are used, no assumption is made about the in-control distributions corresponding to a specific parametric distribution—an assumption that is common with most conventional SPC methods. The proposed method relies on p-values and controls the false discovery rate; which distinguishes it from traditional SPC methods. The relatively low false alarm rate is a highlight of the proposed method, as higher false alarm rates are a common problem with conventional SPC charts. Through extensive simulations, the EWMA and CUSUM methods are shown to have superior performance over the widely-used Shewhart charts, with the EWMA having a slight advantage over the CUSUM. The proposed method is applied to the 2011 Escherichiacoli outbreak in Germany. [ABSTRACT FROM AUTHOR]

As the complexity of production processes increases, the diversity of data types drives the development of network monitoring technology. This paper mainly focuses on an online algorithm to detect serially correlated directed networks robustly and sensitively. First, we consider a transition probability matrix to resolve the double correlation of primary data. Further, since the sum of each row of the transition probability matrix is one, it standardizes the data, facilitating subsequent modeling. Then we extend the spring length based method to the multivariate case and propose an adaptive cumulative sum (CUSUM) control chart on the strength of a weighted statistic to monitor directed networks. This novel approach assumes only that the process observation is associated with nearby points without any parametric time series model, which is in line with reality. Simulation results and a real example from metro transportation demonstrate the superiority of our design. [ABSTRACT FROM AUTHOR]

Some memory-based control charts such as cumulative sum (CUSUM) control chart, exponentially weighted moving average (EWMA) control chart, and homogeneously weighted moving average (HWMA) control chart are used to monitor small-to-moderate shift in the process mean. An HWMA control chart is a memory-based enhanced version of an EWMA control chart because it detects smaller shifts earlier than an ordinary EWMA control chart. The use of auxiliary information-based HWMA statistic enhances the efficiency of a usual CUSUM control chart. This study intends to present an efficient version of usual CUSUM control chart by utilizing an auxiliary information-based HWMA statistic as input information into usual CUSUM control chart. Supremacy of the proposed control chart is established on the basis of average run length (ARL), percent decrease in ARL (PDARL), and Friedman rank test (FRT) by conducting a Monte Carlo simulation. A real-life application shows the implementation of the proposed control chart. [ABSTRACT FROM AUTHOR]

We present a process for designing monitoring procedures that includes practical power guidelines. These guidelines are based upon the average run length (ARL). The specific guideline metric is the ratio of the in-control ARL (ARLic) to the out-of-control ARL (ARLoc). Our recommended design process uses that ARL Ratio, in combination with the ARLic, ARLoc, and ARL curve, to design effective process monitoring procedures. For adequate power, we generally recommend an ARL Ratio of 20 or more and argue that monitoring procedures with ARL Ratios less than 10 should usually be avoided. An area of caution lies between ARL Ratios of 10 and 20, allowing us to propose a stoplight type model for use in monitoring procedure design. We also discuss exceptions to these guidelines as well as a methodology to incorporate power considerations in other approaches to control chart design. [ABSTRACT FROM AUTHOR]

The Conway-Maxwell Poisson (COM-Poisson) distribution is a generalization of the Poisson distribution and encompasses the geometric, the Poisson and the Bernoulli distribution as special cases. This distribution can be used to model over- or under- dispersed data in manufacturing processes. For monitoring such data, a flexible memory-type control chart based on the progressive mean (PM) statistic (regarded as CMP-PM chart) is developed in the present paper. Through a simulation study, we investigate the run-length distribution of the proposed chart. The performance comparison study shows that the CMP-PM chart outperforms the Sellers, the GEWMA and the μ-CUSUM charts at almost all levels of shifts for both over- and under-dispersed data. Finally, the application of the proposed chart is given through an illustrative example. [ABSTRACT FROM AUTHOR]

Toxicant testing is a major component of the environmental solution testing program. This test system aims to evaluate the sensitivity of the test species, evaluate comparability between laboratory test results, and classify possible variability sources such as healthy organisms, organism batches, laboratory water, and food quality changes. For the continuous assessment of sensitivity and accuracy of laboratory toxicity tests, control charts are often used. The cumulative sum (CUSUM) charting structure is a popular monitoring tool that is often used due to its outstanding ability in recognizing small‐to‐moderate unusual changes in process parameters. The CUSUM control charts have now been widely accepted/applied for monitoring the process dispersion under the assumption that the process does not have outliers. In practice, most of the processes have outliers. Therefore, the use of the interquartile range (IQR) as a plotting statistic is an attractive choice for dispersion monitoring. Hence, in this study, the CUSUM control chart with auxiliary information‐based IQR estimators is proposed and employed to monitor dispersion or variation in the variable LC50 by using quantitative structure–activity relationship (QSAR) aquatic toxicity data set. The proposed IQR‐CUSUM charts are compared to its counterparts by using a variety of main output run‐length functions, such as average run length (ARL), standard deviation of run length (SDRL), and extra quadratic loss (EQL), derived from the distribution of run‐length. Further, results revealed that the proposed CUSUM charts have better detection ability as compared to existing counterpart charts, both in theoretical and practical considerations. Control charts are often used for process monitoring. Cumulative sum (CUSUM) charts are popular monitoring tool. The use of the interquartile range (IQR) as a plotting statistic is an attractive choice for dispersion monitoring. In this study, the CUSUM control chart with auxiliary information‐based IQR estimators is proposed and employed to monitor dispersion or variation in LC50 by using quantitative structure‐activity relationship (QSAR) aquatic toxicity data set. The results revealed that the proposed charts outer performed as compared to existing counterpart charts. [ABSTRACT FROM AUTHOR]

This paper develops an integrated economic model for the joint optimization of quality control parameters and a preventive maintenance policy using the cumulative sum (CUSUM) control chart and variable sampling interval fixed time sampling policy. To determine the in-control and out-of-control cost for both mean and variance, a Taguchi quadratic loss function and modified linear loss function are used, respectively. Imperfect preventive maintenance and minimal corrective maintenance policies were considered in developing the model, which determines the optimal values for significant process parameters to minimize the total expected cost per unit of time. A numerical example is used to test the model, which is followed by a sensitivity analysis. The integration of CUSUM mean and variance charts with the maintenance actions are proven successful to detect the slightest shift of the process. The findings reveal that among all the cost components, process failure due to external causes and equipment breakdown has a noteworthy attribute to the total costs of the optimized model. It is expected that top managers can utilize the suggested combined model to minimize the costs related to quality loss and maintenance policy and achieve economical advantages as well. [ABSTRACT FROM AUTHOR]

When designing control charts the in‐control parameters are unknown, so the control limits have to be estimated using a Phase I reference sample. To evaluate the in‐control performance of control charts in the monitoring phase (Phase II), two performance indicators are most commonly used: the average run length (ARL) or the false alarm rate (FAR). However, these quantities will vary across practitioners due to the use of different reference samples in Phase I. This variation is small only for very large amounts of Phase I data, even when the actual distribution of the data is known. In practice, we do not know the distribution of the data, and it has to be estimated, along with its parameters. This means that we have to deal with model error when parametric models are used and stochastic error because we have to estimate the parameters. With these issues in mind, choices have to be made in order to control the performance of control charts. In this paper, we discuss some results with respect to the in‐control guaranteed conditional performance of control charts with estimated parameters for parametric and nonparametric methods. We focus on Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) control charts for monitoring the mean when parameters are estimated. [ABSTRACT FROM AUTHOR]

When in-control process parameters are estimated, this can have a substantial effect on the control chart performance. In particular, it may lead to high false alarm rates for a large number of practitioners. In recent literature, control limit adjustments have been proposed for Shewhart, CUSUM and EWMA control charts in order to provide a specified in-control performance with a specified high probability. In this paper, we compare the out-of-control performance of these adjusted Shewhart, CUSUM and EWMA control charts for sustained shifts in the process mean. We find that the CUSUM control chart has faster detection of sustained shifts compared to both the EWMA and Shewhart control charts. This finding generalizes to almost all shift sizes and estimation errors considered in this paper. The performance of the EWMA is not far worse than that of the CUSUM, but the Shewhart control chart is much slower in detecting sustained shifts in the mean compared to these other two charts. [ABSTRACT FROM AUTHOR]

In the classical setup used in process monitoring, the times between the collection of successive plotted samples are considered as nonrandom. However, in several real‐life applications, it seems plausible to assume that the time needed to collect the necessary information for plotting a point in the control chart has a stochastic nature. Under this scenario, instead of focusing on the number of points plotted on the chart until an out‐of‐control signal is initiated, the appropriate statistic to look at is the total time until a signal is generated. If we denote by L the run length of a control chart and by Yt,t = 1,2,..., the times between successive plotted points, then the compound random variable SL=∑t=1LYt expresses the time to signal of a monitoring scheme, under a particular sampling policy. In this paper, we illustrate how SL can be exploited to study various charts that are suitable for monitoring Poisson observations. We provide some results for the exact distribution of SL that may facilitate the task of the performance assessment of a control chart with random plotting times; illustrations and several numerical comparisons that are useful for quality control experts who wish to practice them are presented, and finally, an illustrative example elucidating the implementation of the proposed model is also provided. [ABSTRACT FROM AUTHOR]

In this research, we consider monitoring mean and correlation changes from zero-inflated autocorrelated count data based on the integer-valued time series model with random survival rate. A cumulative sum control chart is constructed due to its efficiency, the corresponding calculation methods of average run length and the standard deviation of the run length are given. Practical guidelines concerning the chart design are investigated. Extensive computations based on designs of experiments are conducted to illustrate the validity of the proposed method. Comparisons with the conventional control charting procedure are also provided. The analysis of the monthly number of drug crimes in the city of Pittsburgh is displayed to illustrate our current method of process monitoring.

The ordinary CUSUM chart is based on normality assumption. But in real-life phenomenon such as monitoring of lifetime variable, this assumption is not always valid. Hence, a variant of the CUSUM chart, the VCUSUM chart, has been constructed to monitor small shift in a process that is based on a Maxwell distribution. The performance of the chart has been checked by studying the run length properties and it was found to perform better for small shifts as compared to Shewhart type V chart for Maxwell parameter. Finally, a real-life application is presented to monitor the failure rate of a vertical boring machine. [ABSTRACT FROM AUTHOR]

The aim of the present study was to automatically predict the onset of farrowing in crate-confined sows. (1) Background: Automatic tools are appropriate to support animal surveillance under practical farming conditions. (2) Methods: In three batches, sows in one farrowing compartment of the Futterkamp research farm were equipped with an ear sensor to sample acceleration. As a reference video, recordings of the sows were used. A classical CUSUM chart using different acceleration indices of various distribution characteristics with several scenarios were compared. (3) Results: The increase of activity mainly due to nest building behavior before the onset of farrowing could be detected with the sow individual CUSUM chart. The best performance required a statistical distribution characteristic that represented fluctuations in the signal (for example, 1st variation) combined with a transformation of this parameter by cumulating differences in the signal within certain time periods from one day to another. With this transformed signal, farrowing sows could reliably be detected. For 100% or 85% of the sows, an alarm was given within 48 or 12 h before the onset of farrowing. (4) Conclusions: Acceleration measurements in the ear of a sow are suitable for detecting the onset of farrowing in individually housed sows in commercial farrowing crates. [ABSTRACT FROM AUTHOR]

In this paper, we propose control charts to monitor the Weibull scale parameter of type-2 censored reliability data in multistage processes. A cumulative sum control chart and 2 exponentially weighted moving average control charts based on conditional expected values are devised to detect decreases in the mean level of reliability-related quality characteristic. The proposed control schemes are based on standard smallest extreme value distributions derived from Weibull processes to effectively account for the cascade property, which is the main characteristic of multistage processes. Subsequently, simulation study is conducted to evaluate the performance of the control charts using average run length criterion. Extra quadratic loss, performance comparison index, and relative average run length are also used to compare the detect ability of our proposed monitoring procedures. Moreover, sensitivity analysis is done to study the impact of failure number in the sample size and to investigate the robustness of the proposed monitoring procedures against the shift in the previous stage. Finally, a real case study in a glass bottle-making company is investigated to illustrate the performance of the competing control charts. The results reveal the superiority of the cumulative sum control chart. [ABSTRACT FROM AUTHOR]

The process personnel always seek the opportunity to improve the processes. One of the essential steps for process improvement is to quickly recognize the starting time or the change point of a process disturbance. The proposed approach combines the CUSUM control chart with the Bayesian estimation technique. We show that the control chart has some information about the change point and this information can be used to make an informative prior. Two Bayes estimators corresponding to the informative and a non informative prior along with MLE, frequentist approach, are considered. Their mean square error of estimators, are compared through a series of simulations. The results show that the Bayes estimator with the informative prior is more accurate and more precise for almost all values of the shift in the process mean compared to Bayes estimator with a non-informative prior and MLE. [ABSTRACT FROM AUTHOR]

Implementation of the Shewhart, CUSUM, and EWMA charts requires estimates of the in-control process parameters. Many researchers have shown that estimation error strongly influences the performance of these charts. However, a given amount of estimation error may differ in effect across charts. Therefore, we perform a pairwise comparison of the effect of estimation error across these charts. We conclude that the Shewhart chart is more strongly affected by estimation error than the CUSUM and EWMA charts. Furthermore, we show that the general belief that the CUSUM and EWMA charts have similar performance no longer holds under estimated parameters. [ABSTRACT FROM PUBLISHER]

We will deal with financial proceedings of the company using methods of SPC (Statistical Process Control), specifically through time series control charts. The paper will outline the intersection of two disciplines which are econometrics and statistical process control. The theoretical part will discuss the methodology of time series control charts and in the research part there will be this methodology demonstrated in three case studies. The first study will focus on the regulation of simulated financial flows for a company by CUSUM control chart. The second study will involve the regulation of financial flows for a heteroskedastic financial process by EWMA control chart. The last case study of our paper will be devoted to applications of ARIMA, EWMA and CUSUM control charts in the financial data that are sensitive to the mean shifting while calculating the autocorrelation in the data. In this paper, we highlight the versatility of control charts not only in manufacturing but also in managing the financial stability of cash flows.

This paper considers the first-order integer-valued autoregressive (INAR) process with Katz family innovations. This family of INAR processes includes a broad class of INAR(1) processes with Poisson, negative binomial, and binomial innovations, respectively, featuring equi-, over-, and under-dispersion. Its probabilistic properties such as ergodicity and stationarity are investigated and the formula of the marginal mean and variance is provided. Further, a statistical process control procedure based on the cumulative sum control chart is considered to monitor autocorrelated count processes. A simulation and real data analysis are conducted for illustration. [ABSTRACT FROM PUBLISHER]

Today's competitive market has witnessed a growing interest in improving the reliability of products in both service and industrial operations. A large number of monitoring schemes have been introduced to effectively control the reliability-related quality characteristics. These methods have focused on single-stage processes or considered quality variables which are independent. However, the main feature of multistage processes is the cascade property which needs to be justified for the sake of optimal process monitoring. The problem becomes complicated when the presence of censored observations is pronounced. Therefore, both the effects of influential covariates and censored data must be taken into account while presenting a monitoring scheme. In this paper, the accelerated failure time models are used and two regression-adjusted control schemes based on Cox-Snell residuals are devised. Two different scenarios with censored and non-censored data are considered respectively. The competing control charts are compared in terms of zero-state and steady-state average run length criteria using Markov chain approach. The comparison study reveals that the cumulative sum based monitoring procedure is superior and more effective. It should be noted that the application of the proposed monitoring schemes are not restricted to manufacturing processes and thus service operations such as healthcare systems can benefit from them. [ABSTRACT FROM AUTHOR]

Count data processes are often encountered in manufacturing and service industries. To describe the autocorrelation structure of such processes, a Poisson integer-valued autoregressive model of order 1, namely, Poisson INAR(1) model, might be used. In this study, we propose a two-sided cumulative sum control chart for monitoring Poisson INAR(1) processes with the aim of detecting changes in the process mean in both positive and negative directions. A trivariate Markov chain approach is developed for exact evaluation of the ARL performance of the chart in addition to a computationally efficient approximation based on bivariate Markov chains. The design of the chart for an ARL-unbiased performance and the analyses of the out-of-control performances are discussed. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The INARCH(1) model has been proposed in the literature as a simple, but practically relevant, twoparameter model for processes of overdispersed counts with an autoregressive serial dependence structure. In this research, we develop approaches for monitoring INARCH(1) processes for detecting shifts in the process parameters. Several cumulative sum control charts are derived directly from the log-likelihood ratios for various types of shifts in the INARCH(1) model parameters. We define zero-state (worst-state) and steady-state average run length metrics and discuss their computation for the proposed charts. An extensive study indicates that these charts perform well in detecting changes in the process. A real-data example of strike counts is used to illustrate process monitoring. [ABSTRACT FROM AUTHOR]

Monitoring and improving the product reliability is of main concern in a large number of multistage manufacturing processes. The process output is commonly inspected under limited load conditions, and the tensile strength of reliability-related quality characteristic is measured. This brings about censored observations that make the direct application of traditional control charts futile. The monitoring procedure becomes aggravated when the influence of variable competing risk is pronounced during the conducted test. To deal with this critical issue, we propose a regression-adjusted cumulative sum (CUSUM) chart to effectively monitor a quality characteristic that may be right censored because of both fixed and variable competing risks. Moreover, two exponentially weighted moving average (EWMA) control charts on the basis of conditional expected values are devised to detect decreases in the tensile mean. The comparison of the three competing monitoring schemes confirms the superiority of the regression-adjusted CUSUM procedure. Not only is the proposed control chart applicable to manufacturing processes with the aim of monitoring reliability-related quality variables, it is also appropriate for monitoring similar quality measurements in service operations such as survivability measures in healthcare services. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Proper design, reliable control and health of variable–air–volume (VAV) terminals are essential for reliable operation and energy efficiency of VAV air-conditioning systems. This paper presents an online fault diagnosis tool for pressure-independent VAV terminals and its implementation. The fault diagnosis tool is developed using a robust fault detection and diagnosis strategy for VAV terminals. Cumulative sum (CUSUM) control chart with estimated process parameters is utilized to detect faults in VAV terminals. A rule-based fault classifier is designed and utilized to find the sources of faults in VAV terminals. The fault diagnosis tool relies only upon the sensor data and control signals that are commonly available in building management control systems (BMCS). The fault diagnosis tool can be installed in BMCS to online read the operating data of all VAV terminals in a building through local area network and diagnose faults of VAV terminals in the building. The fault diagnosis tool has been validated in a large-scale office building located in Hong Kong and can effectively find all faults occurring in VAV terminals. [Copyright &y& Elsevier]

Abstract: A strategy for in situ fault detection of plasma equipment is presented. This was accomplished by combining optical emission spectroscopy (OES), neural network. The OES was used to collect fault spectra, inducted by radio frequency source and bias powers. A fault detection model was constructed by training the backpropagation neural network (BPNN) on the whole OES spectrum representing a normal plasma operation. The trained BPNN model was tested on the test data generated at other powers. The test result indicates that the BPNN model was capable of detecting abnormal plasma caused by a small variation of 1% in the source power. Due to less impact on the plasma properties, the BPNN model reacted sensitively only to a relatively large variation in the bias power. The performance of the BPNN model-based monitoring scheme was further compared to that of identified radicals. Much improved sensitivity of the BPNN model over them was clearly demonstrated for the source power variation. On the other hand, certain radicals yielded much improved detection for the bias power variation. This was manifest as plasma was monitored by means of the CUSUM control chart. In consequence, monitoring BPNN model-based prediction and identified radicals simultaneously is expected to provide an improved detection of plasma processing equipment. [Copyright &y& Elsevier]

Abstract: A robust fault detection and diagnosis (FDD) strategy using a hybrid approach is presented for pressure-independent variable air volume (VAV) terminals in this paper. The residual-based cumulative sum (CUSUM) control charts are utilized to detect faults in VAV terminals. The residuals between the temperature error and its predication are generated using autoregressive time-series models. The standard CUSUM control charts are used to monitor the residuals which are statistically independent. If the CUSUM value exceeds the chart limits, it means the occurrence of fault or abnormity in the corresponding VAV terminal. The residual-based CUSUM control chart can improve the accuracy of fault detection through eliminating the effects of serial correlation on the performance of control charts. Also, the residual-based CUSUM control chart can enhance the robustness and reliability of fault detection through reducing the impacts of normal transient changes. A rule-based fault classifier consisting of expert rules and fault isolation algorithms is developed to isolate 15 fault sources. The FDD strategy was online tested and validated using in real time data collected from real VAV air-conditioning systems. [Copyright &y& Elsevier]

Abstract: Plasma plays a critical role in fabricating thin films for manufacturing electronic devices. To improve equipment throughput and device yield, plasma should be strictly monitored. A new technique is presented to monitor process plasma. This is accomplished by using a spectrophotometer and a neural network. The spectrometer was used to calculate color chromaticity coordinates and color temperature. Their sensitivity was examined as a function of radio frequency source and bias powers. All the variables linearly increased or decreased for process-induced faults. For an in-situ monitoring, the color variables were modeled by using an auto-correlated type of a time-series neural network and model prediction was applied to a CUSUM control chart. Unlike conventional models, the neural network trained with a reference pattern was utilized for fault detection. The constructed models demonstrated prediction errors ranging from 0.018 to 0.027%. A presented model-based CUSUM yielded improved fault sensitivity over a raw sensor data-based one. The improvement was more drastic for the bias power-induced faults. The proven high sensitivity can be effectively applied to monitor plasma equipment in real-time. [Copyright &y& Elsevier]

Abstract: In order to improve equipment throughput and device yield, chamber leaks needs to be strictly monitored. A new technique for leak detection is presented and this was accomplished by combining backpropagation neural network, discrete wavelet transformation (DWT), and continuous transformation (CWT). Different types of BPNN models were constructed with raw, DWT, and CWT data and these are referred to as raw, DWT, and CWT models, respectively. Constructed models were validated with a total of 47 data sets for normal and leaky chamber conditions. The experimental data were in-situ collected by using an optical emission spectroscopy. Both raw and DWT models could detect all abnormal data sets. Worst detection by CWT model was noted. Wider detection margin provided by DWT model was attributed to enhanced sensitivity of model to leaky condition. A modified cumulative control chart was applied to the statistical mean of raw OES spectra as well as to DWT and CWT data. The statistical mean-based CUSUM control chart was unable to detect chamber leaks. In contrast, chamber leaks could be identified by all model-based CUSUM control charts. Of the proposed models, DWT model is identified to be the most appropriate to chamber leak detection. [ABSTRACT FROM AUTHOR]

Monitoring a leak of plasma processing chamber is crucial to maintaining process quality and improving device yield and equipment throughput. A new technique to detect chamber leak is presented. This is accomplished by constructing a neural network model of optical emission spectroscopy (OES). Using OES, a total of 47 patterns were collected. A neural network model developed with OES pattern yielded accuracies of 1.08% and 1.58% for training and testing data, respectively. The appropriateness of neural network model was tested with the remaining OES patterns. The errors for leaky data were considerably larger, enough to be detectable. The performance of leak detection was evaluated more by applying cumulative sum (CUSUM) control chart to statistical mean, model prediction, and major radical intensity. The neural network model-based CUSUM was found the most effective to monitoring chamber leak. [ABSTRACT FROM AUTHOR]