OWave control chart for monitoring the process mean

Cohen, Achraf; Tiplica, Téodor; Kobi, Abdessamad

doi:10.1016/j.conengprac.2016.06.002

Cited by 3 publications

(4 citation statements)

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“…On the other hand, the d 1 shifts positively when the mean decreases. In fact, wavelet coefficients have symmetrical behaviour with positive and negative mean shifts, then one can use the same weights w i and symmetric control limit, in order to detect both positively and negatively shifts in the mean, more details are given in Cohen et al (2016b).…”

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

confidence: 99%

“…In the literature, a very small number of published papers use a statistic based on wavelet coefficients to monitor the mean and/or the variance of the process in an univarate case. Also, wavelet coefficients can be useful when data are autocorrelated (Cohen et al, 2015(Cohen et al, , 2016bJeske et al, 2018). Image statistical control using wavelet: Image data are become available in today's industries (Koosha et al, 2017;Megahed et al, 2011).…”

Section: Some Research Directionsmentioning

confidence: 99%

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

confidence: 99%

Section: Some Research Directionsmentioning

confidence: 99%

On wavelet-based statistical process monitoring

Cohen

Atoui

2020

Transactions of the Institute of Measurement and Control

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“…But the result of WPD will be more comprehensive and specific than the result of wavelet transform. 26,27 Therefore, WPD was chosen as an efficient tool to enrich fault feature information in this work.…”

Section: ■ Introductionmentioning

confidence: 99%

“…WPD completes the decomposition simultaneously at low frequency and high frequency. , Wavelet transform usually continuously decomposes the low-frequency information, and it also could obtain some level of high-frequency information through proper design. But the result of WPD will be more comprehensive and specific than the result of wavelet transform. , Therefore, WPD was chosen as an efficient tool to enrich fault feature information in this work.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection with Data Imbalance Conditions Based on the Improved Bilayer Convolutional Neural Network

Jing

Zhang

Zhou

2020

Ind. Eng. Chem. Res.

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Fault detection is considered a significant and urgent issue in industrial processes. However, faults rarely occur, resulting in an imbalance between normal data and fault data. This paper presents a novel fault-detection model based on wavelet packet decomposition and a bilayer convolutional neural network (WPD-biCNN) within imbalanced data conditions. WPD helps eliminate the adverse impact of data scarcity by mining information in multiple frequency domains rather than generating new data. More rich information in various time and frequency scales is obtained from collected fault samples, which increases the fault information and can help address the imbalance. The improved bilayer convolutional neural network combining local and full convolution stages is employed to extract features and detect fault. The experimental results of an actual polyethylene production process reveal that the proposed method achieves effective performance in the fault detection of agglomeration. In particular, the proposed method exhibits an excellent classification ability for imbalanced data sets with less fault data and more normal data.

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