Intelligent Monitoring and Fault Diagnosis of Complex Industrial Processes or Equipment
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".
Deadline for manuscript submissions: 10 April 2025 | Viewed by 4311
Special Issue Editors
Interests: reliability modeling; condition monitoring; prognostics and health management; data fusion
Interests: fault intelligent diagnosis; dynamic reliability evaluation; signal processing
Interests: prognostics and health management; maintenance optimization; smart manufacturing
Interests: quality and reliability engineering; prognostics and health management; production planning; lean management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Due to the expanding processing requirements of industrial big data, the pervasive use of artificial intelligence (AI) technology has revolutionized various industries, such as manufacturing, nuclear, aerospace, railway vehicles, and smart vehicles, through applications like condition monitoring, fault diagnosis, and predictive maintenance. Although a significant number of interesting and significant works have been reported in various prestigious journals, some critical problems remain not fully explored and answered. Some of these problems include the condition monitoring of complex industrial processes with time-varying working conditions, fault diagnosis for complex equipment under composite failure modes, optimal decision-making for predictive maintenance, and so on.
This Special Issue aims to address recent developments in the theory and application of health management for complex industrial processes or equipment. Topics include, but are not limited to, the following:
- Intelligent condition monitoring for complex industrial processes
- AI-based anomaly detection for complex industrial processes
- Intelligent fault diagnosis for complex equipment or components
- Multi-sensor data fusion with artificial intelligence algorithms
- Fault prognostics for complex industrial processes
- Degradation modeling and remaining useful life prediction
- Predictive maintenance decision-making for complex systems
- Self-data-driven diagnosis approaches
Dr. Zhen Chen
Dr. Di Zhou
Dr. Biao Lu
Prof. Dr. Ershun Pan
Guest Editors
Manuscript Submission Information
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Keywords
- complex industrial processes
- complex equipment and components
- condition monitoring
- fault diagnosis
- fault prognostics
- remaining useful life prediction
- maintenance optimization
- intelligent algorithms
- deep learning
- statistical machine learning
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