Asymmetric and Symmetric Data Analysis in Equipment Fault Diagnosis and Health Management
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 43
Special Issue Editors
Interests: structural health monitoring (composite material fatigue damage diagnosis and detection); intelligent operation and maintenance system development for equipment (cloud-edge collaboration technology, edge intelligent gateway, Hadoop ecosystem, etc.)
Interests: big data analytics; machining health monitoring; intelligent fault diagnosis; remaining useful life prediction
Special Issues, Collections and Topics in MDPI journals
Interests: fault diagnosis; deep learning, reinforcement learning; health monitoring for marine equipment
Special Issue Information
Dear Colleagues,
This Special Issue seeks to present the latest breakthroughs in the diagnosis, prognosis, and health management of mechanical systems. Mechanical equipment is a cornerstone of industrial operations, and its reliability is essential for minimizing downtime and reducing operational costs. This Special Issue will spotlight advanced methodologies, with a particular focus on asymmetric data analysis and symmetric data analysis, harnessing the power of artificial intelligence (AI) and machine learning to enhance fault diagnosis, failure prediction, and prognostic health management. We encourage submissions exploring a wide range of topics, including fault detection techniques, condition monitoring, predictive maintenance, and the integration of IoT-based solutions, all underpinned by innovative data analysis approaches. This Special Issue invites high-quality research that bridges theoretical advancements with practical applications in mechanical system reliability, offering fresh perspectives on the challenges and opportunities in AI-driven health management. It aims to serve as a dynamic platform for researchers, engineers, and industry professionals to share cutting-edge solutions that elevate system performance and longevity. Topics of interest include, but are not limited to, the following:
- Asymmetric and symmetric data preprocessing and fusion;
- Fault detection and diagnosis techniques under data imbalance;
- Condition monitoring using heterogeneous data sources;
- Predictive maintenance algorithms for both symmetric and asymmetric datasets;
- Prognostic health management through relational or pairwise data analysis;
- IoT‑enabled data collection and real‑time analytics;
- Modeling mechanical system reliability with uneven or balanced data;
- Failure prediction leveraging hybrid data representations.
Dr. Zuoyi Chen
Prof. Dr. Yiwei Cheng
Dr. Guoqiang Li
Guest Editors
Dr. Weixiong Jiang
Guest Editor Assistant
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. Symmetry 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
- asymmetric data analysis
- symmetric data analysis
- fault diagnosis
- health management
- artificial intelligence (AI)
- machine learning
- predictive maintenance
- condition monitoring
- prognostic health management
- IoT integration
- mechanical system reliability
- failure prediction
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