Book•
Statistical quality control : a modern introduction
01 Jan 2009-
TL;DR: Part I: Introduction Chapter 1: Quality Improvement in the Modern Business Environment Chapter 2: The DMAIC Process Chapter 3: Statistical Methods Useful in Quality Control and Improvement Chapter 4: Inferences about Process Quality
Abstract: Part I: Introduction Chapter 1: Quality Improvement in the Modern Business Environment Chapter 2: The DMAIC Process Part II: Statistical Methods Useful in Quality Control and Improvement Chapter 3: Modeling Process Quality Chapter 4: Inferences about Process Quality Part III: Basic Methods of Statistical Process Control and Capability Analysis Chapter 5: Methods and Philosophy of Statistical Process Control Chapter 6: Control Charts for Variables Chapter 7: Control Charts for Attributes Chapter 8: Process and Measurement System Capability Analysis Part IV: Other Statistical Process-Monitoring and Control Techniques Chapter 9: Cumulative Sum and Exponentially Weighted Moving Average Control Charts Chapter 10: Other Univariate Statistical Process Monitoring and Control Techniques Chapter 11: Multivariate Process Monitoring and Control Chapter 12: Engineering Process Control and SPC Part V: Process Design and Improvement with Designed Experiments Chapter 13: Factorial and Fractional Experiments for Process Design and Improvements Chapter 14: Process Optimization and Designed Experiments Part VI: Acceptance Sampling Chapter 15: Lot-by-Lot Acceptance Sampling for Attributes Chapter 16: Other Acceptance Sampling Techniques Appendix
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TL;DR: In this paper, the concept of cointegration is introduced for the analysis of non-stationary time series, as a promising new approach for dealing with the problem of environmental variation in monitored features.
Abstract: Before structural health monitoring (SHM) technologies can be reliably implemented on structures outside laboratory conditions, the problem of environmental variability in monitored features must be first addressed. Structures that are subjected to changing environmental or operational conditions will often exhibit inherently non-stationary dynamic and quasi-static responses, which can mask any changes caused by the occurrence of damage. The current work introduces the concept of cointegration , a tool for the analysis of non-stationary time series, as a promising new approach for dealing with the problem of environmental variation in monitored features. If two or more monitored variables from an SHM system are cointegrated, then some linear combination of them will be a stationary residual purged of the common trends in the original dataset. The stationary residual created from the cointegration procedure can be used as a damage-sensitive feature that is independent of the normal environmental and operational conditions.
177 citations
Cites background from "Statistical quality control : a mod..."
...This plot is essentially an SPC ‘X-chart’ (Montgomery 2009), which clearly indicates that damage has occurred shortly after the mid-point of the time record....
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TL;DR: A method to analyze in detail, translocation events providing a novel and flexible tool for data analysis of nanopore experiments, based on the CUSUM algorithm, an abrupt change detection algorithm that provides fitting of current blockages, allowing the user to easily identify the different levels in each event.
Abstract: We have developed a method to analyze in detail, translocation events providing a novel and flexible tool for data analysis of nanopore experiments. Our program, called OpenNanopore, is based on the cumulative sums algorithm (CUSUM algorithm). This algorithm is an abrupt change detection algorithm that provides fitting of current blockages, allowing the user to easily identify the different levels in each event. Our method detects events using adaptive thresholds that adapt to low-frequency variations in the baseline. After event identification, our method uses the CUSUM algorithm to fit the levels inside every event and automatically extracts their time and amplitude information. This facilitates the statistical analysis of an event population with a given number of levels. The obtained information improves the interpretation of interactions between the molecule and nanopore. Since our program does not require any prior information about the analyzed molecules, novel molecule-nanopore interactions can be characterized. In addition our program is very fast and stable. With the progress in fabrication and control of the translocation speed, in the near future, our program could be useful in identification of the different bases of DNA.
157 citations
TL;DR: Wang et al. as mentioned in this paper developed a manufacturing intelligence solution that integrates spatial statistics and neural networks for the detection and classification of WBM patterns to construct a system for online monitoring and visualisation of Wafer bin maps failure percentages and corresponding spatial patterns.
Abstract: Wafer bin maps (WBM) in circuit probe (CP) tests that present specific defect patterns provide crucial information to identifying assignable causes in the semiconductor manufacturing process. However, most semiconductor companies rely on engineers using eyeball analysis to judge defect patterns, which is time-consuming and not reliable. Furthermore, the conventional statistical process control used in CP tests only monitors the mean or standard deviation of yield rates and failure percentages without detecting defect patterns. To fill the gap, this study aims to develop a manufacturing intelligence solution that integrates spatial statistics and neural networks for the detection and classification of WBM patterns to construct a system for online monitoring and visualisation of WBM failure percentages and corresponding spatial patterns with an extended statistical process control chart. An empirical study was conducted in a leading semiconductor company in Taiwan to validate the effectiveness of the propos...
79 citations
Cites methods from "Statistical quality control : a mod..."
...Constructing the conventional SPC charts to attribute data, such as defect number, requires collecting data based on the sampling plan and then calculating control limits according to the binomial or Poisson distribution (Montgomery 2009)....
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TL;DR: A comprehensive review and assessment of the extant Six Sigma healthcare literature, focusing on application, process changes initiated and outcomes, including improvements in process metrics, cost and revenue.
Abstract: Purpose – The purpose of this paper is to conduct a comprehensive review and assessment of the extant Six Sigma healthcare literature, focusing on: application, process changes initiated and outcomes, including improvements in process metrics, cost and revenue. Design/methodology/approach – Data were obtained from an extensive literature search. Healthcare Six Sigma applications were categorized by functional area and department, key process metric, cost savings and revenue generation (if any) and other key implementation characteristics. Findings – Several inpatient care areas have seen most applications, including admission, discharge, medication administration, operating room (OR), cardiac and intensive care. About 42.1 percent of the applications have error rate as their driving metric, with the remainder focusing on process time (38 percent) and productivity (18.9 percent). While 67 percent had initial improvement in the key process metric, only 10 percent reported sustained improvement. Only 28 perc...
79 citations
Journal Article•
TL;DR: A two-sided nonparametric Phase II exponentially weighted moving average (EWMA) control chart, based on the exceedance statistics, is proposed for detecting a shift in the location parameter of a continuous distribution.
Abstract: Distribution-free (nonparametric) control charts provide a robust alternative to a data analyst when there is lack of knowledge about the underlying distribution. A two-sided nonparametric Phase II exponentially weighted moving average (EWMA) control chart, based on the exceedance statistics (EWMA-EX), is proposed for detecting a shift in the location parameter of a continuous distribution. The nonparametric EWMA chart combines the advantages of a nonparametric control chart (known and robust in-control performance) with the better shift detection properties of an EWMA chart. Guidance and recommendations are provided for practical implementation of the chart along with illustrative examples. A performance comparison is made with the traditional (normal theory) EWMA chart for subgroup averages and a recently proposed nonparametric EWMA chart based on the Wilcoxon-Mann-Whitney statistics. A summary and some concluding remarks are given.
78 citations
Cites background from "Statistical quality control : a mod..."
...Shewhart-type charts are popular in practice because of their simplicity and ease of application, they are good to detect larger transient-type shifts but it is known that they are less able to detect small and persistent process shifts quickly enough (see, for example, Montgomery (2009))....
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...These sequential charts are particularly effective in detecting relatively small and persistent changes (step shifts) in the process (see e.g. Montgomery, 2009 pages 400 and 419)....
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...…choose a small λ, say equal to 0.01, 0.025 or 0.05; if moderate shifts (roughly between 0.5 and 1.5 standard deviations) are of greater concern choose λ = 0.10, whereas if larger shifts (roughly 1.5 standard deviations or more) are of concern choose λ = 0.20 (see e.g. Montgomery (2009), page 423)....
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