A Cumulative Summation Nonparametric Multiple Stream Process Control Chart Based on the Extended Median Test

In statistical process control applications, situations may arise in which several presumably identical processes or “streams” are desired to be simultaneously monitored. Such a monitoring scenario is commonly referred to as a “Multiple Stream Process (MSP).” Charts which have been designed to monitor an MSP typically monitor the means of the streams through collecting samples from each stream and calculating some function of the sample means. The resulting statistic is then iteratively compared to control limits to determine if a single stream or subset of streams may have shifted away from a specified target value. Traditional MSP charting techniques rely on the assumption of normality, which may or may not be met in practice. Thus, a cumulative summation nonparametric MSP control charting technique, based on a modification of the classical extended median test was developed and is referred to as the “Nonparametric Extended Median Test – Cumulative Summation (NEMT-CUSUM) chart.” The development of control limits and estimation of statistical power are given. Through simulation, the NEMT-CUSUM is shown to perform consistently in the presence of normal and non-normal data. Moreover, it is shown to perform more optimally than parametric alternatives in certain circumstances. Results suggest the NEMT-CUSUM may be an attractive alternative to existing parametric MSP monitoring techniques in the case when distributional assumptions about the underlying monitored process cannot reasonably be made

A study of new and advanced control charts for two categories of time related processes

Ph.DDOCTOR OF PHILOSOPH

Novel Mixed EWMA Dual-Crosier CUSUM Mean Charts without and with Auxiliary Information

The classical cumulative SUM (CUSUM) chart is commonly used to monitor a particular size of the mean shift. In many real processes, it is assumed that the shift level varies within a range, and the exact level of the shift size is mostly unknown. For detecting a range of shift size, the dual-CUSUM (DC) and dual-Crosier CUSUM (DCC) charts are used to provide better detection ability as compared to the CUSUM and Crosier CUSUM (CC) charts, respectively. This paper introduces a new mixed exponentially weighted moving average (EWMA)-DCC (EDCC) chart to monitor process mean. In addition, AIB-based EWMA-DC (EDC) and EDCC charts (namely, AIB-EDC and AIB-EDCC charts) are suggested to detect shifts in the process mean level. Monte Carlo simulations are used to compute the run length (RL) characteristics of the proposed charts. A detailed comparison of the proposed schemes with other competing charts is also provided. It turns out that the proposed chart provides better performance than the counterparts when detecting a range of mean shift sizes. A real-life application is also presented to illustrate the implementation of the existing and proposed charts. 2022 Muhammad Arslan et al.Scopu

Modeling and designing control chart for monitoring time-between events data

Ph.DDOCTOR OF PHILOSOPH

A study of advanced control charts for complex time-between-events data

Ph.DDOCTOR OF PHILOSOPH

Cumulative sum quality control charts design and applications

Includes bibliographical references (pages 165-169).Classical Statistical Process Control Charts are essential in Statistical Control exercises and thus constantly obtained attention for quality improvements. However, the establishment of control charts requires large-sample data (say, no less than I 000 data points). On the other hand, we notice that the small-sample based Grey System Theory Approach is well-established and applied in many areas: social, economic, industrial, military and scientific research fields. In this research, the short time trend curve in terms of GM( I, I) model will be merged into Shewhart and CU SUM two-sided version control charts and establish Grey Predictive Shewhart Control chart and Grey Predictive CUSUM control chart. On the other hand the GM(2, I) model is briefly checked its of how accurate it could be as compared to GM( I, 1) model in control charts. Industrial process data collected from TBF Packaging Machine Company in Taiwan was analyzed in terms of these new developments as an illustrative example for grey quality control charts

On Data Depth and the Application of Nonparametric Multivariate Statistical Process Control Charts

The purpose of this article is to summarize recent research results for constructing nonparametric multivariate control charts with main focus on data depth based control charts. Data depth provides data reduction to large-variable problems in a completely nonparametric way. Several depth measures including Tukey depth are shown to be particularly effective for purposes of statistical process control in case that the data deviates normality assumption. For detecting slow or moderate shifts in the process target mean, the multivariate version of the EWMA is generally robust to non-normal data, so that nonparametric alternatives may be less often required

Loss Diagnosis and Indoor Position Location System based on IEEE 802.11 WLANs

Wireless local area networks (WLANs) have been widely deployed to provide short range broadband communications. Due to the fast evolvement of IEEE 802.11 based WLAN standards and various relevant applications, many research efforts have been focused on the optimization of WLAN data rate, power and channel utilization efficiency. On the other hand, many emerging applications based on WLANs have been introduced. Indoor position location (IPL) system is one of such applications which turns IEEE 802.11 from a wireless communications infrastructure into a position location network. This thesis mainly focuses on data transmission rate enhancement techniques and the development of IEEE 802.11 WLAN based IPL system with improved locationing accuracy. In IEEE 802.11 systems, rate adaptation algorithms (RAAs) are employed to improve transmission efficiency by choosing an appropriate modulation and coding scheme accord­ ing to point-to-point channel conditions. However, due to the resource-sharing nature of WLANs, co-channel interferences and frame collisions cannot be avoided, which further complicates the wireless environment and makes the RAA design a more challenging task. As WLAN performance depends on many dynamic factors such as multipath fading and co-channel interferences, differentiating the cause of performance degradation such as frame losses, which is known as loss diagnosis techniques, is essential for performance enhance­ ments of existing rate adaptation schemes. In this thesis, we propose a fast and reliable collision detection scheme for frame loss diagnosis in IEEE 802.11 WLANs. Collisions are detected by tracking changes of the signal-to-interference-and-noise-ratio (SINR) in IEEE 802.11 WLANs with a nonparametric order-based cumulative sum (CUSUM) algorithm for rapid loss diagnosis. Numerical simulations are conducted to evaluate the effectiveness of the proposed collision detection scheme. The other aspect of this thesis is the investigation of an IEEE 802.11 WLAN based IPL system. WLAN based IPL systems have received increasing attentions due to their variety of potential applications. Instead of relying on dedicated locationing networks and devices, IEEE 802.11 WLAN based IPL systems utilize widely deployed IEEE 802.11 WLAN infrastructures and standardized wireless stations to determine the position of a target station in indoor environments. iii Abstract In this thesis, a WLAN protocol-based distance measurement technique is investigated, which takes advantages of existing IEEE 802.11 data/ACK frame exchange sequences. In the proposed distance measurement technique, neither dedicated hardware nor hardware modifications is required. Thus it can be easily integrated into off-the-shelf commercial, inexpensive WLAN stations for IPL system implementation. Field test results confirm the efficacy of the proposed protocol-based distance measurement technique. Furthermore, a preliminary IPL system based on the proposed method is also developed to evaluate the feasibility of the proposed technique in realistic indoor wireless environments

A systematic study on time between events control charts

Ph.DDOCTOR OF PHILOSOPH

Anomaly Detection in Time Series: Theoretical and Practical Improvements for Disease Outbreak Detection

The automatic collection and increasing availability of health data provides a new opportunity for techniques to monitor this information. By monitoring pre-diagnostic data sources, such as over-the-counter cough medicine sales or emergency room chief complaints of cough, there exists the potential to detect disease outbreaks earlier than traditional laboratory disease confirmation results. This research is particularly important for a modern, highly-connected society, where the onset of disease outbreak can be swift and deadly, whether caused by a naturally occurring global pandemic such as swine flu or a targeted act of bioterrorism. In this dissertation, we first describe the problem and current state of research in disease outbreak detection, then provide four main additions to the field. First, we formalize a framework for analyzing health series data and detecting anomalies: using forecasting methods to predict the next day's value, subtracting the forecast to create residuals, and finally using detection algorithms on the residuals. The formalized framework indicates the link between the forecast accuracy of the forecast method and the performance of the detector, and can be used to quantify and analyze the performance of a variety of heuristic methods. Second, we describe improvements for the forecasting of health data series. The application of weather as a predictor, cross-series covariates, and ensemble forecasting each provide improvements to forecasting health data. Third, we describe improvements for detection. This includes the use of multivariate statistics for anomaly detection and additional day-of-week preprocessing to aid detection. Most significantly, we also provide a new method, based on the CuScore, for optimizing detection when the impact of the disease outbreak is known. This method can provide an optimal detector for rapid detection, or for probability of detection within a certain timeframe. Finally, we describe a method for improved comparison of detection methods. We provide tools to evaluate how well a simulated data set captures the characteristics of the authentic series and time-lag heatmaps, a new way of visualizing daily detection rates or displaying the comparison between two methods in a more informative way