Fault Diagnosis of Equipment in the Process Industry
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 124
Special Issue Editors
Interests: prognostic and health management; battery management system; fault diagnosis and prognosis
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent PHM; few-shot fault diagnosis; UAV data analysis; meta-learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue on the "Fault Diagnosis of Equipment in the Process Industry" seeks to present the latest innovations and trends in fault detection, diagnosis, and predictive maintenance techniques for industrial processes. With the growing complexity and automation of modern industrial systems, effective fault diagnosis has become essential for ensuring operational safety, reliability, and efficiency. This Special Issue will focus on novel approaches and methodologies aimed at improving fault detection accuracy, reducing downtime, and enhancing the overall reliability of process equipment.
We welcome submissions that address both theoretical advancements and practical applications, particularly in areas involving intelligent algorithms, real-time monitoring, and the integration of fault diagnosis technologies within industrial systems. Researchers and practitioners from academia and industry are encouraged to contribute their latest work on fault diagnosis systems, predictive maintenance, and machine learning applications. With a focus on the fault diagnosis of equipment in the process industry, this Special Issue will encompass a wide range of topics, including, but not limited to, the following:
- Data-driven and model-based fault diagnosis techniques.
- Intelligent algorithms and machine learning for condition monitoring.
- AI and big data analytics in fault diagnosis for process equipment.
- Anomaly detection and location in the process industry.
- Remaining useful life prediction for key equipment.
- Small data challenges in PHM.
- Predictive maintenance and health management in the process industry.
- Fault diagnosis and prognosis systems and applications in the process industry.
- Reliability and safety assessment in complex systems.
Dr. Heng Zhang
Prof. Dr. Chuanjiang Li
Guest Editors
Dr. Yuhang Xu
Guest Editor Assistant
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
- fault diagnosis
- process industry
- anomaly detection
- prognosis and health management
- AI technology
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