Applications of Control Charts Arima for Autocorrelated Data

Russo, Suzana Leitão; Camargo, Maria Emí­lia; Fabris, Jonas Pedro

doi:10.5772/50990

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…On the contrary to the deterministic drift, the latter is a natural random process that appears in every system, which is observed on enough long-term periods. This particular limit of control charts was already pointed out by many authors [7][8][9][10]. This issue is an emphasis in the real cases presented below.…”

Section: The Autocorrelated Modelsupporting

confidence: 52%

Evaluation of the Long-Term Stability of Metrology Instruments

Coulon¹

2022

Applied Aspects of Modern Metrology

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This chapter aims to emphasize the issue of the long-term stability of instruments used in metrology. This issue is a concern mentioned in the IEC/ISO17025:2017 standard and the JCGM100:2008 guide. Control charts are mentioned in these key documents as tools to assess whether a measurement process is under statistical control or not. Control charts (Shewhart charts, CUSUM chart, EWMA chart) are introduced and tested with simulated and real datasets from metrology instruments that operate at the ionizing department of the BIPM. The interest and the limits of such statistical analysis are discussed. They take their basis in a measurement model composed of Gaussian white noise. Although a measurement monitored over a relatively short period may be consistent with this model, it has been observed that the autocorrelation of the measurement data acquired over a long period limits the relevance of control charts. In this case, time series analysis seems more appropriate than conventional control charts. As an illustration, an optimal Bayesian smoother is introduced to demonstrate how to deconvolve the low-frequency random noise and refine the evaluation of uncertainty according to the measurement model for long-term measurement.

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Section: The Autocorrelated Modelsupporting

confidence: 52%

Evaluation of the Long-Term Stability of Metrology Instruments

Coulon¹

2022

Applied Aspects of Modern Metrology

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“…This technique incorporates the monitoring of residuals i.e. differences between actual and fitted data using an appropriate statistical model [16] and [27]. Autoregressive-integrated-moving-average (ARIMA) models appear to be a reasonable choice due to their high flexibility and need for very few assumptions [28].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Because the expression in square brackets with increasing number of samples converges to 1, UCL t and LCL t approach constant values. EWMA control chart can be applied to the sequence of residuals that are not autocorrelated if a model is properly chosen [27]. Either Shewhart or EWMA control charts of measured data can be employed for detection of regime change.…”

Section: Ewma Control Chartmentioning

confidence: 99%

A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors

Jurjevčič

Senegačnik

Kuštrin

2017

SV-JME

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Operational surveillance of all vital parts of thermal power plants is nowadays more important than any time before due to requirements for their extremely flexible operation resulting from intermittent behaviour of renewable energy sources. New methods for online measuring of pneumatic transport provide new possibilities for control and early fault detection of coal grinding and conveying system in direct-fired power plant boilers. Arrays of intrusive electrostatic sensors are an attractive option due to their inexpensive application and good spatial sensitivity required in large rectangular ducts of pulverized-coal systems. In this study, statistically treated electrostatic signals are used for detection of unexpected change in operating regime of coal grinding and conveying. Model-based and model-free autocorrelation reduction techniques are used to reduce the inherent autocorrelation of data. Forming batch-means of data, a model-free autocorrelation reduction technique is proposed in combination with an autoregressive-integrated-moving-average (ARIMA) method. Residuals between real and ARIMA-model fitted data are entered into exponentially-weighted-moving-average (EWMA) control chart for statistical surveillance of the process. The robust and cost-effective measuring method accompanied with a simple and intuitive control scheme proves to be effective for early fault detection of the pulverized-coal preparation system.

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“…The X control charts is used in order to control the mean of the considered process. The two charts should be used simultaneously (Werkema, 1995).…”

Section: -S Control Chartsmentioning

confidence: 99%

Practical Concepts of Quality Control

Nezhad¹

2012

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Also he has been a visiting researcher in Karlsruhe University, Germany. He has published more than 37 research papers in national and international journals and is author of two books. Also, he has been awarded a Silver medal in 16th National Mathematics Olympiad in Iran and he has bens ranked 1st in the graduate national university comprehensive exam in System Management in Iran. He has been ranked 47th among all high school graduates in Iran. His areas of interest include: reliability, quality control, quality engineering and operations research ContentsPreface XI

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Applications of Control Charts Arima for Autocorrelated Data

Cited by 6 publications

References 12 publications

Evaluation of the Long-Term Stability of Metrology Instruments

Evaluation of the Long-Term Stability of Metrology Instruments

A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors

Practical Concepts of Quality Control

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