Quantitative infrared thermography resolved leakage current problem in cathodic protection system

Leakage current problem can happen in Cathodic Protection (CP) system installation. It could affect the performance of underground facilities such as piping, building structure, and earthing system. Worse can happen is rapid corrosion where disturbance to plant operation plus expensive maintenance cost. Occasionally, if it seems, tracing its root cause could be tedious. The traditional method called line current measurement is still valid effective. It involves isolating one by one of the affected underground structures. The recent methods are Close Interval Potential Survey and Pipeline Current Mapper were better and faster. On top of the mentioned method, there is a need to enhance further by synthesizing with the latest visual methods. Therefore, this paper describes research works on Infrared Thermography Quantitative (IRTQ) method as resolution of leakage current problem in CP system. The scope of study merely focuses on tracing the root cause of leakage current occurring at the CP system lube base oil plant. The results of experiment adherence to the hypothesis drawn. Consequently, res

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

A Nonparametric HEWMA-p Control Chart for Variance in Monitoring Processes

Control charts are considered as powerful tools in detecting any shift in a process. Usually, the Shewhart control chart is used when data follows the symmetrical property of a normal distribution. In practice, the data from the industry may follow a non-symmetrical distribution or an unknown distribution. The average run length (ARL) is a significant measure to assess the performance of the control chart. The ARL may mislead when the statistic is computed from an asymmetric distribution. To handle this issue, in this paper, an ARL-unbiased hybrid exponentially weighted moving average proportion (HEWMA-p) chart is proposed for monitoring the process variance for a non-normal distribution or an unknown distribution. The efficiency of the proposed chart is compared with the existing chart in terms of ARLs. The proposed chart is more efficient than the existing chart in terms of ARLs. A real example is given for the illustration of the proposed chart in the industry.11Ysciescopu

A Proposed Single EWMA Chart Combining The X And R Charts For A Joint Monitoring Of The Process Mean And Variance [TS156. Y46 2008 f rb].

Dua carta kawalan biasanya digunakan untuk kawalan min proses dan varians proses secara berasingan di industri pengeluaran. In manufacturing industries, two control charts are usually used to monitor the process mean and the process variance separately

Investigation of the application of Statistical Process Control into Low Volume Manufacturing

Statistical process control (SPC) into Low Volume Manufacturing environment face a challenge applying SPC techniques. SPC is commonly used for quality control and improvement in the manufacturing sector. In the early 1920s, Dr Walter Shewhart developed the control chart employed to monitor a process over time, where the first data is collected and then plotted on a graph. Moreover, a control chart is composed of a Central Line (CL), the Upper Control Limit (UCL) and the Lower Control Limit (LCL). Parameters and control limits are calculated to analyze the control chart, requiring twenty to twenty-five subgroups of data, with three to five values per subgroup, or at least sixty measurements. However, collect this amount of data is difficult in certain production processes, where the lot size could even be one and it could take weeks or months to accumulate enough data to estimate the process parameters. Statistical process control is a challenge in some scenarios such as startup production, different or individual parts in the same production line, or production of customized products. In these cases, there is not enough amount of data to compute the parameters to monitor the process. Therefore, special techniques and statistical methods are required. Some authors developed self-starting control charts and alternative methods for short-run production, e.g. Q charts, Exponentially Weighted Moving Average (EWMA) and Cumulative sum (CUSUM). This thesis studies the performance of these SPC tools, implementing a Low Volume Statistical Process Control (LV-SPC) model through an Excel spreadsheet, analyzing the production process data from companies that are performing low volume manufacturing. This work provides an interpretation and explanation about statistical process control into low volume manufacturing, analyzing the application of different SPC methods developed for short production runs based on data collected from different companies. Data collected was processed to individual measurements from the process deviation rather than the mean values. Converting the data to individual values the SPC methods for low volume manufacturing are viable to use. Also, performance, effectiveness, and how it can be further implemented were discussed

Affine invariant signed-rank multivariate exponentially weighted moving average control chart for process location monitoring

Multivariate statistical process control (SPC) charts for detecting possible shifts in mean vectors assume that data observation vectors follow a multivariate normal distribution. This assumption is ideal and seldom met. Nonparametric SPC charts have increasingly become viable alternatives to parametric counterparts in detecting process shifts when the underlying process output distribution is unknown, specifically when the process measurement is multivariate. This study examined a new nonparametric signed-rank multivariate exponentially weighted moving average type (SRMEWMA) control chart for monitoring location parameters. The control chart was based on adapting a multivariate spatial signed-rank test. The test was affine-invariant and the weighted version of this test was used to formulate the charting statistic by incorporating the exponentially weighted moving average (EWMA) scheme. The test\u27s in-control (IC) run length distribution was examined and the IC control limits were established for different multivariate distributions, both elliptically symmetrical and skewed. The average run length (ARL) performance of the scheme was computed using Monte Carlo simulation for select combinations of smoothing parameter, shift, and number of p-variate quality characteristics. The ARL performance was compared to the performance of the multivariate exponentially weighted moving average (MEWMA) and Hotelling T2. The control charts for observation vectors sampled the multivariate normal, multivariate t, and multivariate gamma distributions. The SRMEWMA control chart was applied to a real dataset example from aluminum smelter manufacturing that showed the SRMEWMA performed well. The newly investigated nonparametric multivariate SPC control chart for monitoring location parameters--the Signed-Rank Multivariate Exponentially Weighted Moving Average (SRMEWMA)--is a viable alternative control chart to the parametric MEWMA control chart and is sensitive to small shifts in the process location parameter. The signed-rank multivariate exponentially weighted moving average performance for data from elliptically symmetrical distributions is similar to that of the MEWMA parametric chart; however, SRMEWMA\u27s performance is superior to the performance of the MEWMA and Hotelling\u27s T2 control charts for data from skewed distributions

Method of lines and runge-kutta method in solving partial differential equation for heat equation

Solving the differential equation for Newton’s cooling law mostly consists of several fragments formed during a long time to solve the equation. However, the stiff type problems seem cannot be solved efficiently via some of these methods. This research will try to overcome such problems and compare results from two classes of numerical methods for heat equation problems. The heat or diffusion equation, an example of parabolic equations, is classified into Partial Differential Equations. Two classes of numerical methods which are Method of Lines and Runge-Kutta will be performed and discussed. The development, analysis and implementation have been made using the Matlab language, which the graphs exhibited to highlight the accuracy and efficiency of the numerical methods. From the solution of the equations, it showed that better accuracy is achieved through the new combined method by Method of Lines and Runge-Kutta method

A Neural Network Approach to Synthetic Control Chart for the Process Mean

In this project, a multivariate synthetic control chart for monitoring the process mean vector of skewed populations using weighted standard deviations has been proposed. The proposed chart incorporates the weighted standard deviation (WSD) method of Chang and Bai (2004) into the standard multivariate synthetic chart of Ghute and Shirke (2008)

Detecting the process\u27 1.5 sigma shift: A quantitative study

Process behavior can change with time. In this study an attempt was made to discover whether the Six Sigma™ claim of changes in the process mean stayed within +/- 1.5 sigma units. Several process groups were examined for a particular firm that made metal castings, machined parts, tested major components and assembled these into a vehicle that was a product sold to the customer. As the assembly progressed, deficiencies were identified and recorded. Analyses employed cumulative sum (CUSUM) sequence charts, Autoregressive Integrated Moving Average (ARIMA) time series analyses, minimum mean square error (MMSE) exponentially weighted moving average (EWMA), Shewhart control charts and Analysis of Variance (ANOVA) to identify the shift in the process mean, M/sw, the duration of the shift, λB, and the proper choice of EWMA smoothing coefficient, λEWMA. Kruskal-Wallis analysis of the relationship of these measures to process group (assembly, foundry, heat treatment, machining, shaving, test machine, grinding, turning, warranty and yield) was also performed. The method used was generally applicable for all these processes. The process group and the ARIMA type also influenced the measurement of M/sw , λB , and λEWMA

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