Advanced Sensing and Fault Diagnosis for Complex Manufacturing Processes
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: 10 September 2024 | Viewed by 350
Special Issue Editors
Interests: fault diagnosis; process monitoring; data-driven performance monitoring and management
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; data mining and analytic; PHM and fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Interests: soft sensors; modelling and system identification design of experiments; control, smart Factory/Industry 4.0; fault detection and isolation
Special Issue Information
Dear Colleagues,
Due to the development of advanced sensing techniques, vast quantities of data are produced daily in complex manufacturing processes. To make the most and the best use of the available data, data-driven techniques have been the subject of extensive research in recent years. Compared with traditional model-based techniques, data-driven methods can not only save in costly modelling processes, but also obtain valuable information from the available process data for real-time process maintenance. Then, abnormal events including different types of faults can be diagnosed in a timely manner. Due to the ever-increasing complexity that exists in manufacturing processes, there are many new challenging problems to be solved in this field, such as fault root-cause analysis for large-scale, plant-wide processes; advanced sensing, such as image and voiceprint-based process monitoring; and fault diagnosis in the distributed framework, among others.
This Special Issue aims to provide a platform for the presentation of recent findings and emerging research developments in advanced sensing and data-driven fault diagnosis for complex manufacturing processes, especially process monitoring, fault detection, fault diagnosis, and deep learning-relevant fault diagnosis techniques and their application in complex manufacturing processes.
Potential topics to be covered:
(1) Advanced sensing techniques
(2) Data-driven fault diagnosis methods
(3) Deep learning-based fault diagnosis methods
(4) Data-driven fault identification and root-cause analysis
(5) Data-driven fault degrade evaluation methods
(6)Image and voiceprint-based fault diagnosis
(7) Fault diagnosis methods with application to different sectors
Dr. Kai Zhang
Dr. Zhiwen Chen
Prof. Dr. Yuri A. W. Shardt
Guest Editors
Manuscript Submission Information
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