Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques, 2nd Edition

Topic Information

Dear Colleagues,

This Topic focuses on cutting-edge subjects in the industrial sector, exploring how predictive analytics and machine fault diagnosis can enhance production efficiency and equipment reliability. With the emergence of Industry 4.0, data-driven decision-making and innovative maintenance strategies have become the cornerstone of the industry. This Topic delves into the methods of employing data analysis, predictive modeling, and machine learning techniques to achieve machine health monitoring and early fault diagnosis, ultimately reducing maintenance costs, minimizing production interruptions, and enhancing equipment reliability. We pay particular attention to critical components in rotating machinery, such as bearings, which play a pivotal role in industrial manufacturing. Through state monitoring and fault diagnosis, alongside emerging technologies like deep learning, one may accurately diagnose machine conditions and proactively engage in predictive maintenance. This Topic is designed to foster collaboration between industry and academia, driving innovation in methods and applications to meet the demands of modern industrial production. We eagerly anticipate research findings in this critical field, which will provide the industry with more efficient and reliable production methods.

Prof. Dr. Wentao Mao
Dr. Jie Liu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Energies energies	3.2	7.3	2008	16.2 Days	CHF 2600	Submit
Machines machines	2.5	4.7	2013	16.9 Days	CHF 2400	Submit
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Automation automation	2.0	4.1	2020	23.4 Days	CHF 1200	Submit

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