A wavelet‐based nonparametric CUSUM control chart for autocorrelated processes with applications to network surveillance

Li, Jun; Jeske, Daniel R.; Zhou, Yue; Zhang, Xin

doi:10.1002/qre.2427

Cited by 11 publications

(5 citation statements)

References 30 publications

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“…Regression adjustment‐based monitoring of auto‐correlated processes was introduced by Loredo 28 . A variety of techniques for solving autocorrelation issues have been established, including the CUSUM control chart with autocorrelation data 29–31 . In addition, researchers 32–35 created control charts using EWMA autocorrelation data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…28 A variety of techniques for solving autocorrelation issues have been established, including the CUSUM control chart with autocorrelation data. [29][30][31] In addition, researchers [32][33][34][35] created control charts using EWMA autocorrelation data. Correlated variables are used in multivariate methods to monitor systems.…”

Section: Multivariate Statistical Process Control For Auto-correlated...mentioning

confidence: 99%

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Multivariate auto‐correlated process control by a residual‐based mixed CUSUM‐EWMA model

Wang

Asrini

2023

Quality & Reliability Eng

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Multivariate auto-correlated process control issues in industrial systems are a concern for statistical process monitoring (SPM). Traditional control charts produce large false alarms and/or miss timely detections of quality deterioration because they are unable to recognize the signals from multivariate auto-correlated response variables. To track multivariate auto-correlated processes, this paper presents a new residual-based mixed multivariate control chart using cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) approaches. Using in-control data, the multi-output least square support vector regression model's optimal hyper-parameters are determined, and a bootstrap method is used to estimate the upper control limit of the proposed control chart. The suggested control chart has strong detection performance for a small magnitude mean vector shift based on the average run length (ARL) performance for a particular range of shifts. Experimental result elaborates that the proposed control chart is more sensitive to detecting the mean vector shift compared with the existing commonly used models, such as multivariate CUSUM and multivariate EWMA control charts. The proposed control chart model and corresponding computational algorithm are successfully applied to SPM in an electronic conductor production line with multivariate auto-correlated attributes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Multivariate Statistical Process Control For Auto-correlated...mentioning

confidence: 99%

Multivariate auto‐correlated process control by a residual‐based mixed CUSUM‐EWMA model

Wang

Asrini

2023

Quality & Reliability Eng

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“…The proposed mechanism is applied to the uncorrelated residuals from principal component analysis model (Harrou et al, 2018a), see also (Harrou et al, 2019). A distribution-free approach using a multivariate cumulative sum (CUSUM) control chart to monitor wavelet coefficients is proposed to detect location shifts (Li et al, 2019).…”

Section: An Illustrative Example: Weighted Wavelet Coefficients For Process Meanmentioning

confidence: 99%

On wavelet-based statistical process monitoring

Cohen

Atoui

2020

Transactions of the Institute of Measurement and Control

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This paper presents an overview of wavelet-based techniques for statistical process monitoring. The use of wavelet has already had an effective contribution to many applications. The increase of data availability has led to the use of wavelet analysis as a tool to reduce, denoise, and process the data before using statistical models for monitoring. The most recent review paper on wavelet-based methods for process monitoring had the goal to review the findings up to 2004. In this paper, we provide a recent reference for researchers and engineers with a different focus. We focus on: (i) wavelet statistical properties, (ii) control charts based on wavelet coefficients, and (iii) wavelet-based process monitoring methods within a machine learning framework. It is clear from the literature that wavelets are widely used with multivariate methods compared to univariate methods. We also found some potential research areas regarding the use of wavelet in image process monitoring and designing control charts based on wavelet statistics, and listed them in the paper.

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“…We have considered classical Phase‐II CUSUM‐type charts in the current paper, mainly designed for the zero‐state shift but also helpful for shifts at an unknown time. Motivated by Qiu and Zou, 38 Zou and Tsung, 39 and Li et al., 40 among others, powerful change‐point design‐based self‐starting control charts for detecting arbitrary shifts may be studied in a separate article. It is worth mentioning that Ross and Adams 41 considered the bi‐aspect LS for detecting an arbitrary shift when the traditional design‐based Lepage chart was introduced by Mukherjee and Chakraborti 26 .…”

Section: Introductionmentioning

confidence: 99%

An optimally designed distribution‐free CUSUM procedure for tri‐aspect surveillance of continuous processes

Tang

Mukherjee

2023

Quality & Reliability Eng

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In the past few decades, research on nonparametric process monitoring schemes mainly dealt with the uni‐aspect or bi‐aspect schemes, focusing on monitoring process location or scale separately or jointly. Another critical process characteristic, namely, the process shape, is not explicitly dealt with in‐depth in most existing charting schemes. In classical hypothesis testing, some recent literature clearly showed that the multi‐aspect test statistics, although designed for very restrictive alternatives, often perform as well or better than many statistics for arbitrary distributional shifts. They are often better than Kolmogorov‐Smirnov or Cramér‐von Mises statistics and some empirical likelihood‐based test statistics. The current paper aims to use a tri‐aspect statistic to design a distribution‐free Phase‐II Cumulative Sum (CUSUM) charting scheme for monitoring any arbitrary process changes in the process. The proposal is nonparametric and is equivalent to an unknown standard case. It reflects, in addition, which parameters, among location, scale, or shape, are more responsible for a signal. The construction of the CUSUM scheme from an existing tri‐aspect Shewhart‐type chart is simple, so significant attention is devoted to determining a near‐optimal reference parameter of the chart in keeping an unknown shift type in mind. Comparisons of the optimal performance of various competitors are considered in terms of the median run length (MRL) metric. The proposed charting scheme designed with the tri‐aspect statistic compares highly favorably with many existing SPM schemes. The same is evident from our findings based on the Monte‐Carlo simulation. Finally, the proposed schemes are illustrated with a flow‐width measurement monitoring example.

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A wavelet‐based nonparametric CUSUM control chart for autocorrelated processes with applications to network surveillance

Cited by 11 publications

References 30 publications

Multivariate auto‐correlated process control by a residual‐based mixed CUSUM‐EWMA model

Multivariate auto‐correlated process control by a residual‐based mixed CUSUM‐EWMA model

On wavelet-based statistical process monitoring

An optimally designed distribution‐free CUSUM procedure for tri‐aspect surveillance of continuous processes

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