Artificial Intelligence Enhanced Health Monitoring and Diagnostics: 2nd Edition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: 5 December 2024 | Viewed by 165
Special Issue Editors
Interests: structure health monitoring; online fault monitoring and diagnosis; maintenance decision
Special Issues, Collections and Topics in MDPI journals
Interests: quality and reliability engineering; prognostics and health management; predictive modeling; applied operations research and statistics
Special Issues, Collections and Topics in MDPI journals
Interests: signal processing; fault feature extraction; fault prognosis; life prediction; fault transfer diagnosis; vision measurement; digital twin; energy harvesting for sensors
Special Issues, Collections and Topics in MDPI journals
Interests: predictive maintenance; system reliability; industrial IoT; cyber physical system; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Following people’s awareness of the importance of the reliabilty, safety and maintainability of industrial systems for a long period of time, innovative technologies for health monitoring and diagnostics of industrial systems have attracted increasing attention. In particular, with the rapid advances of artificial intelligence, intelligent Internet of Things (IoT) and industrial big data technologies, there have been increasing interests in the development of advanced artificial intelligence algorithms in order to address the challenges in the fields of condition monitoring, anomaly detection, fault prognostics and diagnostics of various industrial systems. Recently, diverse kinds of artificial intelligence algorithms, such as convolution neural network, adversarial adaptation network and extreme learning machine, have been developed for health monitoring and diagnostics in the light of massive monitoring data collected by sensors and IoT devices.
The aim of this Special Issue is to provide a platform for scientists, engineers and industrial practitioners to present their latest theoretical and technological advancements in artificial intelligence-enhanced health monitoring and diagnositics for industrial systems. High-quality research articles, short communication and reviews are welcome. Research studies that seek to address recent developments in advanced artificial intelligence algorithms are of special interest, such as deep learning, ensemble learning, transfer learning and reinforcement learning, and are well suited for enhancing the health monitoring, diagnositics and prognostics of industrial systems.
Papers are solicited in but are not limited to the following and related topics:
- Artificial intelligence algorithms for health monitoring and fault diagnosis;
- Big data mining methods for anomaly detection;
- Deep learning methods for intelligent fault prognostics;
- Deep transfer learning algorithms for fault diagnosis with small fault samples;
- Deep reinforcement learning methods for prognostics and health management;
- Artificial intelligence-based edge and cloud computing for health monitoring.
Prof. Dr. Jun Wu
Dr. Zhaojun Steven Li
Prof. Dr. Yi Qin
Dr. Carman K.M. Lee
Guest Editors
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. Sensors 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 2600 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
- artificial intelligence
- deep learning
- health monitoring
- anomaly detection
- fault diagnosis
