Intelligent Maintenance of Machines with Big-Data Era
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 20 October 2024 | Viewed by 83
Special Issue Editors
Interests: intelligent diagnosis and prognostic methodology; big data-driven intelligent maintenance
Interests: intelligent maintenance; decision optimization; power system reliability
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid development of cutting-edge information technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), has reformed the preventive maintenance mode of machines to be intelligent and predictive in the current big data era. The big data are drawn from the entire life cycle of a machine, which contains abundant information regarding activities in design, services, and maintenance. Intelligent maintenance is expected to adaptively represent useful information from machine big data and further give feedback on design optimization, running safety guarantees, and optimal maintenance decision-making. Due to the huge potential value of big data for machines, intelligent maintenance has been a hot topic in prognostics and health management. The referenced methodologies and technologies include advanced measurement and data collection, intelligent health monitoring and fault diagnosis, remaining useful life prediction and prognostics, maintenance optimization, etc. This Special Issue aims to collect the latest research achievements regarding intelligent maintenance of machines. The potential scopes are suggested but not limited to the following:
- Digital twin modeling of mechanical systems;
- Intelligent fault diagnosis with limited data;
- Transfer learning-based fault diagnosis of machines;
- Large-scale diagnosis foundation models;
- Data-driven remaining useful life prediction;
- Data-model-fusion prognostics of machines;
- Health assessment and maintenance asset optimization;
- Intelligent maintenance and repairment decision making
Dr. Bin Yang
Dr. Li Yang
Guest Editors
Manuscript Submission Information
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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
- intelligent maintenance
- condition monitoring
- fault diagnosis
- prognostics
- remaining useful life prediction
- maintenance decision making
