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Processes
Special Issues
Data-Based Process Monitoring, Process Control, and Quality Improvement in Industry
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Data-Based Process Monitoring, Process Control, and Quality Improvement in Industry

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (18 December 2023) | Viewed by 4547

Special Issue Editors

Prof. Dr. Shari Mohd Yusof

E-Mail Website
Guest Editor
Graduate School of Business Adminstration, Meiji University, Surugadai, Tokyo 101-8301, Japan
Interests: statistical quality; design for Six Sigma; quality control; process excellence
Dr. Sarina Abdul Halim-Lim

E-Mail Website
Guest Editor
Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Interests: quality improvement; Six Sigma; quality control; statistical analysis application; process excellence
Dr. Cherrafi Anass

E-Mail Website
Guest Editor
École Supérieure de Technologie de Safi (EST), Cadi Ayyad University, Safi 46000, Morocco
Interests: Lean Six Sigma; Industry 4.0; circular economy; supply chain management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data-based process monitoring, process control, and quality improvement have become key technologies in process industries for safety, quality, and operation efficiency enhancement in recent industry. Quality improvement involves the use of data, through various quality improvement tools, methods, and approaches, to identify areas for improvement in the production process or product quality. This may involve analyzing customer feedback or examining production data to identify process trends and opportunities for improvement. These practices rely on the collection and analysis of data, process monitoring, and process control to identify areas for improvement as well as ensure consistent and high-quality output.

This Special Issue on “Data-Based Process Monitoring, Process Control and Quality Improvement in Industry” aims to curate a comprehensive overview of the latest research and developments in applications of data-based process monitoring, process control, and quality improvement, highlighting data-driven approaches utilized to optimize process performance, reduce waste, and improve product as well as process quality, and provides valuable insights for researchers and practitioners working in these areas. Topics include, but not are limited to, the following:

  • Real-time monitoring;
  • Statistical process control;
  • Big data analytics, machine learning, and artificial intelligence;
  • Smart manufacturing;
  • Process optimization;
  • Fault detection and diagnosis;
  • Process control;
  • Quality improvement approaches;
  • Lean Six Sigma;
  • Design for Six Sigma;
  • Industry 4.0;
  • Blockchain-based traceability.

Prof. Dr. Shari Mohd Yusof
Dr. Sarina Abdul Halim-Lim
Dr. Cherrafi Anass
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • process monitoring
  • process control
  • quality improvement tools
  • big data analytics
  • process excellence

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Published Papers (3 papers)

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Research

16 pages, 4417 KiB  
Article
Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder
by Jinlin Zhu, Zhong Liu, Xuyang Lou, Furong Gao and Zheng Zhang
Processes 2024, 12(4), 682; https://doi.org/10.3390/pr12040682 - 28 Mar 2024
Cited by 1 | Viewed by 1035
Abstract
This paper studies the use of varying threshold in the statistical process control (SPC) of batch processes. The motivation is driven by how when multiple phases are implicated in each repetition, the distributions of the features behind vary with phases or even the [...] Read more.
This paper studies the use of varying threshold in the statistical process control (SPC) of batch processes. The motivation is driven by how when multiple phases are implicated in each repetition, the distributions of the features behind vary with phases or even the time; thus, it is inconsistent to uniformly bound them by an invariant threshold. In this paper, we paved a new path for learning and monitoring batch processes based on an efficient framework integrating a model termed conditional dynamic variational auto-encoder (CDVAE). Phase indicators are first used to split the data and are then separated, serving as an extra input for the model in order to alleviate the learning complexity. Dissimilar to the routine using features across all timescales, only features relevant to local timestamps are aggregated for threshold calculation, producing a varying threshold that is more specific for the process variations occurring among the timeline. Leveraged upon this idea, a fault detection panel is devised, and a deep reconstruction-based contribution diagram is illustrated for locating the faulty variables. Finally, the comparative results from two case studies highlight the superiority in both detection accuracy and diagnostic performance. Full article
(This article belongs to the Special Issue Data-Based Process Monitoring, Process Control, and Quality Improvement in Industry)
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20 pages, 7400 KiB  
Article
Attention-Based Two-Dimensional Dynamic-Scale Graph Autoencoder for Batch Process Monitoring
by Jinlin Zhu, Xingke Gao and Zheng Zhang
Processes 2024, 12(3), 513; https://doi.org/10.3390/pr12030513 - 2 Mar 2024
Cited by 1 | Viewed by 1070
Abstract
Traditional two-dimensional dynamic fault detection methods describe nonlinear dynamics by constructing a two-dimensional sliding window in the batch and time directions. However, determining the shape of a two-dimensional sliding window for different phases can be challenging. Samples in the two-dimensional sliding windows are [...] Read more.
Traditional two-dimensional dynamic fault detection methods describe nonlinear dynamics by constructing a two-dimensional sliding window in the batch and time directions. However, determining the shape of a two-dimensional sliding window for different phases can be challenging. Samples in the two-dimensional sliding windows are assigned equal importance before being utilized for feature engineering and statistical control. This will inevitably lead to redundancy in the input, complicating fault detection. This paper proposes a novel method named attention-based two-dimensional dynamic-scale graph autoencoder (2D-ADSGAE). Firstly, a new approach is introduced to construct a graph based on a predefined sliding window, taking into account the differences in importance and redundancy. Secondly, to address the training difficulties and adapt to the inherent heterogeneity typically present in the dynamics of a batch across both its time and batch directions, we devise a method to determine the shape of the sliding window using the Pearson correlation coefficient and a high-density gridding policy. The method is advantageous in determining the shape of the sliding windows at different phases, extracting nonlinear dynamics from batch process data, and reducing redundant information in the sliding windows. Two case studies demonstrate the superiority of 2D-ADSGAE. Full article
(This article belongs to the Special Issue Data-Based Process Monitoring, Process Control, and Quality Improvement in Industry)
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14 pages, 5919 KiB  
Article
Unlocking Insights: A Cloud Tool for Data Visualisation in a Smart Meter Project
by Beni Luyo, Alex Pacheco, Cesar Cardenas, Edwin Roque and Guido Larico
Processes 2023, 11(11), 3059; https://doi.org/10.3390/pr11113059 - 25 Oct 2023
Viewed by 1515
Abstract
Nowadays, the large amount of data generated by society has led to a dependency on data analysis and visualisation tools. Therefore, the objective of this research was to implement a cloud-based tool to improve the visualisation of data obtained from 4G network simulation [...] Read more.
Nowadays, the large amount of data generated by society has led to a dependency on data analysis and visualisation tools. Therefore, the objective of this research was to implement a cloud-based tool to improve the visualisation of data obtained from 4G network simulation on smart meters. Two stages were carried out in order to analyse and process the data using a cloud-based tool to support data visualisation and to understand and facilitate effective decision-making. This resulted in a remarkable 27.39% increase in average data quality, thanks to the authenticity and reliability of the data obtained through the 4G LTE network on smart meters. It also had a significant impact on the percentage of data read and displayed, with an increase of 63.70%. Finally, the percentage of useful data when applying the tool in the cloud also increased by 47.30%. This allows end users to visualise and understand the behaviour of electricity meters with an LTE network through a customised dashboard. Full article
(This article belongs to the Special Issue Data-Based Process Monitoring, Process Control, and Quality Improvement in Industry)
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