System Reliability and Predictive Maintenance in Industrial Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: 20 March 2025 | Viewed by 1715
Special Issue Editors
Interests: primarily concern analysis of the reliability; safety and maintenance of industrial plants; job safety; multicriteria decision making methodologies applied to industrial plant engineering and operations
Special Issues, Collections and Topics in MDPI journals
Interests: industrial plant engineering; maintenance; industrial safety and risk; reliability; manufacturing systems; additive manufacturing
Special Issues, Collections and Topics in MDPI journals
Interests: safety and risk engineering; reliability engineering
Special Issue Information
Dear Colleagues,
This issue aims at covering all the aspects related to system reliability and predictive maintenance in industrial engineering.
In today's highly dynamic market, companies are increasingly interested in achieving operational excellence by optimizing the performance of physical assets. An essential element for the achievement of this objective is the guarantee of high levels of asset reliability and availability, which can exploit the potential offered by the technological evolution on ICTs and system automation. In fact, the widespread presence of sensors and monitoring systems in industrial plants, coupled with analytics tools based on artificial intelligence and machine learning, makes it possible for decision-makers to have real-time data on operating conditions, performance and safety of their assets and advanced forecasting support for more efficient maintenance decisions.
This Special Issue wants to share the experience of industrial engineers, both from industry and academia, and discuss the state of the art about approach, methods, tools and techniques on systems reliability and predictive maintenance.
TOPICS
- Systems reliability;
- Reliability allocation and optimization;
- Risk based reliability;
- Condition monitoring;
- Anomaly detection;
- Failure prediction;
- Artificial intelligence (AI) for reliability analysis;
- Machine learning (ML) for maintenance decisions;
- Reliability data analytics;
- Predictive maintenance KPI;
- Maintenance service optimization;
- Reliability for business continuity;
- Data driven maintenance;
- Predictive maintenance;
- Innovative computing technologies in reliability;
- Statistical process quality;
- Decision support systems;
- Reliability of monitoring systems;
- Sensor network reliability;
- Asset failure;
- Asset strategy;
- Spare part management.
APPLICATION AREAS (not limited to)
- Manufacturing industry;
- Chemical and process industry;
- Oil and gas industry;
- Energy production and distribution.
Dr. Natalia Trapani
Dr. Filippo De Carlo
Dr. Ahmad BahooToroody
Dr. Mohammad Mahdi Abaei
Guest Editors
Manuscript Submission Information
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