8,440 research outputs found
Monitoring of the BTA Deep Hole Drilling Process Using Residual Control Charts
Deep hole drilling methods are used for producing holes with a high lengthto- diameter ratio, good surface finish and straightness. The process is subject to dynamic disturbances usually classified as either chatter vibration or spiralling. In this work, we propose to monitor the BTA drilling process using control charts to detect chatter as early as possible and to secure production with high quality. These control charts use the residuals obtained from a model which describes the variation in the amplitude of the relevant frequencies of the process. The results showed that chatter is detected and some alarm signals are related to changing physical conditions of the process. --
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
Monitoring variance by EWMA charts with time varying smoothing parameter
Memory charts like EWMA-S² or CUSUM-S² can be designed to be optimal to detect a
specific shift in the process variance. However, this feature could be a serious
inconvenience since, for instance, if the charts are designed to detect small shift, then,
they can be inefficient to detect moderate or large shifts. In the literature, several
alternatives have been proposed to overcome this limitation, like the use of control
charts with variable parameters or adaptive control charts. This paper proposes new
adaptive EWMA control charts for the dispersion (AEWMA-S²) based on a timevarying
smoothing parameter that takes into account the potential misadjustment in the
process variance. The obtained control charts can be interpreted as a combination of
EWMA control charts designed to be efficient for different shift values. Markov chain
procedures are established to analyse and design the proposed charts. Comparisons with
other adaptive and traditional control charts show the advantages of the proposals
Adaptive EWMA Control Charts with a Time Varying Smoothing Parameter
It is known that time-weighted charts like EWMA or CUSUM are designed to be optimal to detect a specific shift. If they are designed to detect, for instance, a very small shift, they can be inefficient to detect moderate or large shifts. In the literature, several alternatives have been proposed to circumvent this limitation, like the use of control charts with variable parameters or adaptive control charts. This paper has as main goal to propose some adaptive EWMA control charts (AEWMA) based on the assessment of a potential misadjustment, which is translated into a time-varying smoothing parameter. The resulting control charts can be seen as a smooth combination between Shewhart and EWMA control charts that can be efficient for a wide range of shifts. Markov chain procedures are established to analyze and design the proposed charts. Comparisons with other adaptive and traditional control charts show the advantages of the proposals.Acknowledgements: financial support from the Spanish Ministry of Education and Science, research
project ECO2012-38442
Exponentially Weighted Moving Average Charts for Monitoring the Process Generalized Variance
The exponentially weighted moving average chart based on the sample generalized variance is studied under the independent multivariate normal model for the vector of quality measurements. The performance of the chart is based on an analysis of the chart\u27s initial and steady-state run length distributions. The three methods that are commonly used to determinate run length distribution, simulation, the integral equation method, and the Markov chain approximation are discussed. The integral equation and Markov chain approaches are analytical methods that require a nu- merical method for determining the probability density and cumulative distribution functions describing the distribution of the sample generalized variance. Two meth- ods for determining numerically these functions are discussed. The equivalence of the integral equation and Markov chain methods is shown resulting in a new method for obtaining a Markov chain approximation of the chart. Some examples of the implementation of these methods are given using MATLAB
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