Special Issue "Advanced Process Monitoring for Industry 4.0"
Deadline for manuscript submissions: 25 June 2021.
Interests: Process Analytics; Process Systems Engineering; Fault Detection, Diagnosis and Prognosis; Industrial Data Science; Chemometrics
This Special Issue aims to bring together recent advances in the broad field of Advanced Process Monitoring for Industry 4.0, including all the activities related to fault detection, diagnosis, and prognosis.
Papers on data-driven, model-based, and hybrid monitoring approaches for continuous, batch, and discrete processes are welcomed, especially when addressing emerging challenges of the new industrial technology landscape, such as, but not limited to:
• High-dimensional process monitoring;
• Dealing with heterogeneous data sources;
• Image-based process monitoring;
• Monitoring 3D objects (e.g., from additive manufacturing);
• Artificial Intelligence for process monitoring;
• Fault diagnosis and troubleshooting;
• Fault prognosis and predictive maintenance;
• Monitoring process health and equipment health;
• Integration of statistical process control and engineering process control;
• Monitoring multistage and/or distributed processes;
• Monitoring the supply chain;
• Handling stationary and nonstationary dynamics;
• Monitoring for cybersecurity;
• Applications in health.
Prof. Dr. Marco S. Reis
Prof. Dr. Furong Gao
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 papers will be 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 2000 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.
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
1. Title: Multi-scale Convolutional and Recurrent Neural Network for Quality Prediction of Cold Rolling Process
Abstract: The quality prediction in cold rolling process is of great significance to effective process control and total quality improvement. Flatness is a key geometrical feature of strip products in a cold rolling process. Due to the uncertainty and non-linear relationship between the flatness of strip products and various factors, prediction of flatness quality is a challenge. However, traditional prediction models based on domain knowledge and expertise are difficult to adapt to the changes in multiple operating conditions and raw materials from various enterprises. To meet the challenge, we propose a flatness pattern prediction framework composed of a multiscale convolutional and recurrent neural network (MCRNN) to handle the varying operating condition during the rolling of products with different specifications and to realize an effective flatness feedback control strategy. The proposed framework MCRNN consists of three parts: data acquisition, quality prediction, and dynamic control. In addition, in the case of the natural imbalance of continuous casting data, we generate different class distributions based on random under-sampling (RUS) method to mitigate the impact of the skewed data distribution. The experimental results and the comprehensive comparison with the state-of-the-art methods show the superiority of the proposed MCRNN framework, which has not only the satisfactory prediction performance, but good potentials to improve process understanding and strip flatness quality.