Fault Diagnosis and Health Monitoring of Mechanical Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 20 September 2024 | Viewed by 116
Special Issue Editors
Interests: signal processing; data mining; edge computing; digital twin; interpretable deep learning; and intelligent monitoring system for fault diagnosis and health monitoring of machines
Special Issue Information
Dear Colleagues,
In recent years, the fault diagnosis and health monitoring of mechanical systems have played an increasingly important role in automated and intelligent industrial applications. Although many studies related to mechanism modeling and data-driven and artificial intelligence have been proposed, there are still issues that need further study. These issues include the intelligent fault diagnosis of mechanical systems, such as nonlinear system dynamics modeling analysis, early weak fault detection, multi-source information decoupling and separation, trend assessment and prediction under complex operating conditions, interpretable deep learning, and digital twin algorithms.
This Special Issue aims to publish the latest advancements and research findings in the field of mechanical systems’ fault diagnosis, as well as the health monitoring and interpretable intelligent recognition. It aims to explore innovative theories, methodologies, and technologies employed in ensuring the safety and longevity of mechanical systems. Topics covered may include, but are not limited to, dynamics mechanism analysis, signal adaptive filtering, blind source separation, predictive maintenance techniques, information fusion, intelligent systems’ fault diagnosis, predictive techniques, IoT applications, interpretable deep learning algorithms, and digital twin approaches to mechanical systems’ health maintenance. The Special Issue provides a platform for experts, scholars, and research groups in related fields to share their insights, experiences, and solutions contributing to the advancement of the intelligent fault diagnosis and health monitoring of mechanical systems.
Dr. Xiaoxi Ding
Dr. Jun Zhu
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. 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 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
- dynamics mechanism modeling
- signal adaptive filtering
- mode decomposition
- blind source separation
- information fusion
- intelligent fault diagnosis
- predictive maintenance techniques
- interpretable deep learning
- internet of things applications
- digital twin approaches to mechanical systems health maintenance
