Multiscale monitoring of autocorrelated processes using wavelets analysis

Guo, Huairui; Paynabar, Kamran; Jin, Jesse S.

doi:10.1080/0740817x.2011.609872

Cited by 17 publications

(9 citation statements)

References 23 publications

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“…Even though the ideas presented in this paper can be directly extended to other univariate control charts, this work focuses on the Shewhart chart due to its popularity and its computational simplicity [26]. These advantages of multiscale representation have been exploited by [27] who developed wavelet-based multiscale CUSUM and EWMA charts with improved fault detection abilities using autocorrelated process data. These techniques have been shown to improve the out-of-control average run length (ARL 1 ), which measures how long a particular technique takes to identify the presence of a fault.…”

Section: Introductionmentioning

confidence: 99%

Improved Fault Detection and Process Safety Using Multiscale Shewhart Charts.

Sheriff¹,

Nounou²

2017

J Chem Eng Process Technol

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Process safety is a critical component in various process industries. Statistical process monitoring techniques were initially developed to maximize efficiency and productivity, but over the past few decades with catastrophic industrial disasters, process safety has become a top priority. Sensors play a crucial role in recording process measurements, and according to the number of monitored variables, process monitoring techniques can be classified into univariate or multivariate techniques. Most univariate process monitoring techniques rely on three fundamental assumptions: that process residuals contain a moderate level of noise, are independent, and are normally distributed. Practically, however, due to a variety of reasons such as modeling errors and malfunctioning sensors, these assumptions are violated, which can lead to catastrophic incidents. Fortunately, multiscale wavelet-based representation of data inherently possesses characteristics that are able to deal with these violations of assumptions. Therefore, in this work, multiscale representation is utilized to enhance the performance of the Shewhart chart (which is a well-known univariate fault detection method) to help improve its performance. The performance of the developed multiscale Shewhart chart was assessed and compared to the conventional chart through two examples, one using synthetic data, and the other using simulated distillation column data. The results of both examples clearly show that the developed multiscale Shewhart chart provides lower missed detection and false alarm rates, as well as lower ARL 1 values (i.e., quicker detection) for most cases where the fundamental assumptions of the Shewhart chart are violated. Additionally, the relative simplicity of the proposed algorithm encourages its implementation in practice to help improve process safety.

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Section: Introductionmentioning

confidence: 99%

Improved Fault Detection and Process Safety Using Multiscale Shewhart Charts.

Sheriff¹,

Nounou²

2017

J Chem Eng Process Technol

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“…Within‐profile correlation and autocorrelation are also addressed in several research. Montgomery and Mastrangelo, Psarakis and Papal, Staudlammer et al , Jensen et al , Noorossana et al , Soleimani et al , Qiu et al , Guo et al , Amiri and Zou, and Zhang et al are some typical examples.…”

Section: Introductionmentioning

confidence: 99%

“…Montgomery and Mastrangelo, 41 Psarakis and Papal, 42 Staudlammer et al, 31 Jensen et al, 43 Noorossana et al, 44 Soleimani et al, 45 Qiu et al, 46 Guo et al, 47 Amiri and Zou, 38 and Zhang et al 48 are some typical examples. Extensive studies have been conducted on nonlinear profiles, some of which could be found in Jin and Shi, 7,49 Lada et al, 50 Ding et al, 51 Jeong et al, 52 Zhou et al, 53 Zhang and Albin, 54 Chicken et al, 55 Chang and Yadama, 56 Paynabar and Jin, 57 Guo et al, 47 Paynabar et al, 58 Fan et al, 59 Nikoo and Noorossana, 60 and McGinnity et al 61 It is good to mention several other studies using other approaches, which are listed as follows: Zeng and Chen 62 used Bayesian hierarchical approach. Zou et al 63 used penalized regression model to detect outliers.…”

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confidence: 95%

An Area‐based Methodology for the Monitoring of General Linear Profiles

Motasemi

Alaeddini

Zou

2016

Quality & Reliability Eng

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Statistical profile monitoring has received much attention during recent years. While numerous contributions and applications have been demonstrated in the literature, the control statistics across many of the proposed methodologies have mainly remained unchanged, which somehow hinders further improvement of the monitoring schemes. In this paper, however, we propose a novel approach to leverage the information in the area formed between the sampled and incontrol profile to improve the monitoring scheme performance. Specifically, we develop a control statistic based on the convolution of the observed and in-control profiles to monitor the shifts in the slope and intercept parameters. We also extend the mean square statistic to area weighted total sum of squares, to more effectively monitor the shift in the standard deviation. Extensive simulation studies are conducted to demonstrate the performance of the proposed methodology in comparison with some of the existing approaches.

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“…Paynabar and Jin used a wavelet‐based mixed‐effect model with a capability to model within‐profile and between‐profile variations. Guo et al . proposed a multi‐scale control chart scheme for an autoregressive moving average (ARMA) model based on Haar wavelet coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…Paynabar and Jin 20 used a wavelet-based mixed-effect model with a capability to model within-profile and between-profile variations. Guo et al 21 proposed a multi-scale control chart scheme for an autoregressive moving average (ARMA) model based on Haar wavelet coefficients. Three control charts are developed on the basis of three levels of approximate and detail wavelet coefficients for monitoring process mean, process variance, and error variance simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Phase II Monitoring of Nonlinear Profile Variance Using Wavelet

Nikoo

Noorossana

2012

Quality & Reliability Eng

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In some statistical process control applications, the quality of a process or a product can be characterized by a nonlinear relationship between a response variable and one or more explanatory variables. Monitoring nonlinear profiles using nonlinear regression has been proposed by several researchers as a potential area for research. To avoid disadvantages of parameter estimation in nonlinear regression, we used nonparametric regression with wavelets for monitoring nonlinear profiles. In nonparametric regression framework, traditional variance estimator is not proper; other estimators should be used instead. Parametric or nonparametric control charts in phase II are proposed to monitor error term variance when nonparametric regression with wavelet is used. Multivariate control chart based on regression coefficients (approximate wavelet coefficients) is added to variance control chart to check stability in the process mean. It is well known that the performance of control schemes in detecting shifts in multivariate control charts deteriorates as the dimension of regression coefficient increases. To improve the performance of control schemes, we considered decomposition level as a smoothing parameter, which determines the form of regression function (size of approximate wavelet coefficients vector) in nonparametric regression with wavelet. A method based on an analysis of variance is proposed to determine the optimal decomposition level. The statistical performances of the proposed methods are evaluated using average run length criterion using vertical density profile data. Numerical results indicate that the proposed methods perform satisfactorily. Copyright © 2012 John Wiley & Sons, Ltd.

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Multiscale monitoring of autocorrelated processes using wavelets analysis

Cited by 17 publications

References 23 publications

Improved Fault Detection and Process Safety Using Multiscale Shewhart Charts.

Improved Fault Detection and Process Safety Using Multiscale Shewhart Charts.

An Area‐based Methodology for the Monitoring of General Linear Profiles

Phase II Monitoring of Nonlinear Profile Variance Using Wavelet

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