Comparing Statistical Feature and Artificial Neural Networks for Control Chart Pattern Recognition: A Case Study

Control chart has been widely used for monitoring production process, especially in evaluating the quality performance of a product. An uncontrolled process is usually known by recognizing its chart pattern, and then performing some actions to overcome the problems. In high speed production process, real-time data is recorded and plotted almost automatically, and the control chart pattern needs to be recognized immediately for detecting any unusual process behavior. Neural networks for automatic control chart recognition have been studied in detecting its pattern. In the field of computer science, the performance of its automatic and fast recognition ability can be a substitution for a conventional method by human. Some researchers even have developed newer algorithm to increase the recognition process of this neural networks control chart. However, artificial approaches have some difficulties in implementation, especially due to its sophisticated programming algorithm. Another competing method, based on statistical feature also has been considered in recognition process. Control chart is related to applied statistical method, so it is not unreasonable if statistical properties are developed for its pattern recognition. Correlation coefficient, one of classic statistical features, can be applied in control chart recognition. It is a simpler approach than the artificial one. In this paper, the comparison between these two methods starts by evaluating the behavior of control chart time series point, and measured for its closeness to some training data that are generated by simulation and followed some unusual control chart pattern. For both methods, the performance is evaluated by comparing their ability in detecting the pattern of generated control chart points. As a sophisticated method, neural networks give better recognition ability. The statistical features method simply calculate the correlation coefficient, even with small differences in recognizing the generated pattern compared to neural networks, but provides easy interpretation to justify the unusual control chart pattern. Both methods are then applied in a case study and performances are then measured

EWMA Chart and Measurement Error

Measurement error is a usually met distortion factor in real-world applications that influences the outcome of a process. In this paper, we examine the effect of measurement error on the ability of the EWMA control chart to detect out-of-control situations. The model used is the one involving linear covariates. We investigate the ability of the EWMA chart in the case of a shift in mean. The effect of taking multiple measurements on each sampled unit and the case of linearly increasing variance are also examined. We prove that, in the case of measurement error, the performance of the chart regarding the mean is significantly affected.Exponentially weighted moving average control chart, Average run length, Average time to signal, Measurement error, Markov chain, Statistical process control

A simple approach for monitoring business service time variation.

Control charts are effective tools for signal detection in both manufacturing processes and service processes. Much of the data in service industries comes from processes having nonnormal or unknown distributions. The commonly used Shewhart variable control charts, which depend heavily on the normality assumption, are not appropriately used here. In this paper, we propose a new asymmetric EWMA variance chart (EWMA-AV chart) and an asymmetric EWMA mean chart (EWMA-AM chart) based on two simple statistics to monitor process variance and mean shifts simultaneously. Further, we explore the sampling properties of the new monitoring statistics and calculate the average run lengths when using both the EWMA-AV chart and the EWMA-AM chart. The performance of the EWMA-AV and EWMA-AM charts and that of some existing variance and mean charts are compared. A numerical example involving nonnormal service times from the service system of a bank branch in Taiwan is used to illustrate the applications of the EWMA-AV and EWMA-AM charts and to compare them with the existing variance (or standard deviation) and mean charts. The proposed EWMA-AV chart and EWMA-AM charts show superior detection performance compared to the existing variance and mean charts. The EWMA-AV chart and EWMA-AM chart are thus recommended

Robust Control Charts for Time Series Data

This article presents a control chart for time series data, based on the one-step- ahead forecast errors of the Holt-Winters forecasting method. We use robust techniques to prevent that outliers affect the estimation of the control limits of the chart. Moreover, robustness is important to maintain the reliability of the control chart after the occurrence of alarm observations. The properties of the new control chart are examined in a simulation study and on a real data example.Control chart;Holt-Winters;Non-stationary time series;Out- lier detection;Robustness;Statistical process control

Pressure Injury and Restraint Prevalence Surveys: Saving Time and Dollars for Patient Care by Automating Manual Chart Abstraction

Bronson Healthcare Group performs quarterly pressure injury and restraint audits as part of the National Database of Nursing Quality Indicators (NDNQI). The chart abstraction portion of the audit previously required nurses to manually abstract 31 data points. To save time and cost, we used Lean and PDSA process improvement tools to automate the chart abstraction portion of the audit, reducing the number of data points requiring manual abstraction to 2. We validated the automated abstraction by comparing it to abstractions done manually by the audit nurses. We found that an automated process has the potential to reduce the impact of human error inherent in manual abstraction

Application of machine learning and expert systems to Statistical Process Control (SPC) chart interpretation

Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts