Machine Learning Based Predictive Maintenance and Condition Monitoring
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 30 June 2024 | Viewed by 191
Special Issue Editor
Interests: iIntelligent decision making and predictive operation; health management and intelligent maintenance; condition monitoring and fault diagnosis
Special Issue Information
Dear Colleagues,
With the advent of advanced data analytics and machine learning algorithms, industries across various sectors are leveraging these technologies to optimize maintenance practices and enhance operational safety. Machine learning has become an essential tool for predictive maintenance and condition monitoring. It enables the proactive identification of potential equipment failures, facilitates data-driven decision making, reduces downtime, and optimizes maintenance schedules, which can both enhance operational safety and reduce maintenance costs.
This Special Issue aims to explore the state of the art in machine learning for predictive maintenance and condition monitoring. We encourage the submission of original research articles, reviews, and short communications focused on the integration of machine learning into predictive maintenance strategies. Topics of interest for this Special Issue include, but are not limited to, the following:
- Industrial big data analysis and data mining;
- Intelligent fault detection and diagnosis of machines;
- Prognostics and health management of mechanical systems;
- Real-time condition monitoring and anomaly detection;
- Deep learning approaches for predictive maintenance;
- Industrial applications of machine learning-based maintenance strategies;
- Intelligent decision making for maintenance optimization;
- Predictive and forecasting techniques for equipment reliability;
- Sensor fusion and data preprocessing techniques in predictive maintenance.
Prof. Dr. Wei Cheng
Guest Editor
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. Machines 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
- machine learning
- predictive maintenance
- fault diagnosis
- condition monitoring
- health management
