Green Fault Diagnosis: Energy-Efficient and Eco-Friendly Machinery Maintenance
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 4
Special Issue Editors
Interests: interpretable machine learning for PHM; fault diagnosis
Interests: equipment status monitoring and fault diagnosis; machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The global push toward sustainable manufacturing and carbon neutrality demands transformative approaches to machinery maintenance. This Special Issue focuses on green fault diagnosis (GFD)—a paradigm integrating cutting-edge technologies, energy-efficient strategies, and eco-friendly methodologies to achieve reliable, low-carbon machinery maintenance.
Topics of interest include, but are not limited to, the following:
- Intelligent Monitoring and Predictive Maintenance
- AI, IoT, and digital twin applications for equipment state monitoring.
- Fault prediction and diagnosis using multi-sensor data fusion (vibration, temperature, pressure).
- Low-Power Sensing Technologies
- Energy-efficient sensor hardware design (e.g., ultra-low-power circuits, event-driven sensing).
- Self-powered sensors leveraging energy harvesting (vibration, thermal, solar).
- Low-Carbon Diagnostic Algorithms
- Lightweight AI models (e.g., pruned neural networks, quantized algorithms).
- Edge computing frameworks for decentralized data processing.
- Physics-informed machine learning to enhance model interpretability and efficiency.
Dr. Ming Zeng
Dr. Haiyang Pan
Dr. Zhiyi He
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. Machines 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
- fault diagnosis
- machinery maintenance
- digital twin
- internet of things
- edge computing
- self-powered sensor
- machine learning
- data fusion
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