On wavelet-based statistical process monitoring

2020 ◽  
pp. 014233122093570
Author(s):  
Achraf Cohen ◽  
Mohamed Amine Atoui
Keyword(s):  
Process Monitoring ◽  
Control Charts ◽  
Data Availability ◽  
Image Process ◽  
Monitoring Methods ◽  
Statistical Process ◽  
Learning Framework ◽  
Research Areas ◽  
Statistical Process Monitoring ◽  
Effective Contribution

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.

Comparison of statistical process monitoring methods: application to the Eastman challenge problem

2000 ◽  
Vol 24 (2-7) ◽  
pp. 175-181 ◽  
Author(s):  
Manabu Kano ◽  
Koji Nagao ◽  
Shinji Hasebe ◽  
Iori Hashimoto ◽  
Hiromu Ohno ◽  
...  
Keyword(s):  
Process Monitoring ◽  
Monitoring Methods ◽  
Statistical Process ◽  
Statistical Process Monitoring

scholarly journals Monitoring of Flotation Systems by Use of Multivariate Froth Image Analysis

Minerals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 683
Author(s):  
Chris Aldrich ◽  
Xiu Liu
Keyword(s):  
Image Analysis ◽  
Process Monitoring ◽  
Chemical Species ◽  
Platinum Metal ◽  
Local Binary Patterns ◽  
Monitoring Methods ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Froth Stability ◽  
Froth Image

Froth image analysis has been considered widely in the identification of operational regimes in flotation circuits, the characterisation of froths in terms of bubble size distributions, froth stability and local froth velocity patterns, or as a basis for the development of inferential online sensors for chemical species in the froth. Relatively few studies have considered flotation froth image analysis in unsupervised process monitoring applications. In this study, it is shown that froth image analysis can be combined with traditional multivariate statistical process monitoring methods for reliable monitoring of industrial platinum metal group flotation plants. This can be accomplished with well-established methods of multivariate image analysis, such as the Haralick feature set derived from grey level co-occurrence matrices and local binary patterns that were considered in this investigation.

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Angelo Marcio Oliveira Sant’Anna
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Particle Swarm ◽  
Injection Rate ◽  
Statistical Process ◽  
Swarm Optimization ◽  
Content Type ◽  
Statistical Process Monitoring

PurposeE-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.Design/methodology/approachAn approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.FindingsThe results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.Originality/valueThis research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.

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