Special Issue "Machine Fault Diagnostics and Prognostics"
Deadline for manuscript submissions: 15 March 2020.
Prof. Dr. Cheol Hong Kim
School of Electronics and Computer Engineering, Chonnam National University, 77 Yongbong-ro, Yongbong-dong, Buk-gu, Kwangju, South Korea
Interests: fault diagnostics; health prognosis; mobile system design; machine learning; edge computing; embedded system
We are currently living through the fourth Industrial revolution, which is riding on the wave of cutting-edge technologies in computing, artificial intelligence, and communications. The past decade has witnessed incredible advances in the field of artificial intelligence (AI) and has seen massive proliferation of cloud computing technologies. These technological advances have further fueled the integration of the cyber and the physical worlds, with intelligence and autonomy as its key hallmarks, which would lead to more reliable, productive, and efficient industries and businesses in the future.
Machines and mechanical structures in industries undergo inevitable degradation and loss of performance during operation. The timely diagnosis of symptoms of their degradation and a reliable estimate of their future health condition are essential for Industrial productivity and reliability. Models constructed from historical measurement data using AI techniques have shown great promise in fault diagnosis and prognosis of industrial equipment. AI-based techniques are poised to gain even more significance in the future as huge amounts of measurement data are to be available for decision making due to the deployment of the internet-of-things and cloud-based technologies for condition-based maintenance (CBM).
This Special Issue will focus on the topic of fault diagnosis and prognosis of industrial equipment and mechanical structures. We invite researchers and practicing engineers to contribute original research articles that discuss issues related but not limited to condition-based monitoring, fault diagnosis and prognosis of industrial machines and mechanical structures, diagnostic and prognostic techniques based on AI, such as deep learning, transfer learning, and neuro-fuzzy inference techniques, AI-based solutions that are explainable, solutions utilizing the Internet. of Things, cloud computing, cyber physical systems, and machine-to-machine interfaces and paradigms for fault diagnosis and prognosis in the context of Industry 4.0. We would also welcome review articles that capture the current state-of-the art and outline future areas of research in the fields relevant to this Special Issue.
Prof. Dr. Jong-Myon Kim
Prof. Dr. Cheol Hong Kim
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 papers will be 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. Applied Sciences is an international peer-reviewed open access semimonthly 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 1500 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.
- Condition monitoring
- Fault diagnosis
- Health prognosis
- Remaining useful life
- Deep learning
- Artificial intelligence
- Condition-based maintenance
- Cyber physical systems