Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.5
Journals
Machines
Special Issues
Green Fault Diagnosis: Energy-Efficient and Eco-Friendly Machinery Maintenance
share announcement

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: closed (31 January 2026) | Viewed by 883

Special Issue Editors

Dr. Ming Zeng

E-Mail Website
Guest Editor
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Interests: interpretable machine learning for PHM; fault diagnosis
Dr. Haiyang Pan

E-Mail Website
Guest Editor
School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
Interests: equipment status monitoring and fault diagnosis; machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiyi He

E-Mail Website
Guest Editor
College of Mechanical and Vehicle Engineering, Changsha University of Science and Technology, Changsha 410114, China
Interests: incremental learning for intelligent diagnosis; tensor learning

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 250 words) can be sent to the Editorial Office for assessment.

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

35 pages, 9559 KB  
Article
A Framework for Anomaly Detection and Evaluation of Rotating Machinery Based on Data-Accumulation-Aware Generative Adversarial Networks and Similarity Estimation
by Lei Hu, Lingjie Tan, Xiangyan Meng, Jiyu Zeng, Peng Luo and Yi Yang
Machines 2026, 14(1), 61; https://doi.org/10.3390/machines14010061 - 2 Jan 2026
Viewed by 529
Abstract
Rotating machinery plays a critical role in industrial systems, and effective anomaly detection and assessment are indispensable for ensuring operational safety and reliability. However, the performance of existing methods is often constrained by the difficulty in acquiring fault samples—such samples are typically scarce [...] Read more.
Rotating machinery plays a critical role in industrial systems, and effective anomaly detection and assessment are indispensable for ensuring operational safety and reliability. However, the performance of existing methods is often constrained by the difficulty in acquiring fault samples—such samples are typically scarce during the initial operational phase of equipment. To address this challenge, this paper proposes a novel anomaly detection and evaluation framework based on Data-Accumulation-Aware Generative Adversarial Networks (DAA-GANs) and similarity estimation. The core innovation of this framework lies in its adaptability across different data accumulation stages. During the early operational phase dominated by normal samples, only normal data is used to train the DAA-GAN to establish a baseline detector. As fault data gradually accumulates, the detection threshold undergoes adaptive adjustment through collaborative optimization of normal and abnormal samples, thereby enhancing the detector’s generalization capability. Upon amassing annotated fault samples of varying severity, the framework assesses anomaly severity by analyzing the similarity between test outputs of unknown samples and known fault samples. The framework is validated through two case studies: a fault simulation model for a torque-splitting transmission system and the publicly available Case Western Reserve University (CWRU) bearing dataset. In the simulation case, the detection accuracy reaches 100% for the gear tooth breakage levels. On the CWRU dataset, the proposed method achieves an overall average detection accuracy of 99.83% across three operating speeds (1730/1750/1772 rpm), and the similarity-based assessment provides consistent severity identification. These results demonstrate that the proposed framework can support reliable anomaly detection and severity assessments under progressive data accumulation. Full article
(This article belongs to the Special Issue Green Fault Diagnosis: Energy-Efficient and Eco-Friendly Machinery Maintenance)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop