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Machines
Special Issues
AI-Driven Intelligent Perception and Diagnosis of Mechanical Equipment
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AI-Driven Intelligent Perception and Diagnosis of Mechanical Equipment

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1328

Special Issue Editors

Dr. Xuefang Xu

E-Mail Website
Guest Editor
School of Electrical Engineering, Yanshan University, Qinhuangdao, China
Interests: artificial intelligence; fault diagnosis; computational fluid dynamics analysis; robot design and control
Dr. Zijian Qiao

E-Mail Website
Guest Editor
School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China
Interests: mechanical fault diagnosis; weak signal detection; structural health monitoring; intelligent medical equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Mengdi Li

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Tsinghua University, Beijing, China
Interests: weak signal detection; mechanical fault diagnosis.
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Mei

E-Mail Website
Guest Editor
1. Department of Electrical Electronic Telecommunications Engineering and Naval Architecture, University of Genoa, Genoa, Italy
2. Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
Interests: vehicle engineering; machine learning

Special Issue Information

Dear Colleagues,

This Special Issue will develop advanced methodologies for artificial intelligence (AI) use in the perception and diagnosis of mechanical equipment. Mechanical equipment types such as wind turbines, motors, pumps, and vehicles usually operate under tough environments and are prone to malfunction. Fault diagnosis plays a vital role in ensuring the safety operation of machinery and has been a hot research topic in recent years. Nowadays, AI is one of the most rapid and remarkable technologies available. AI is now bringing great opportunities and revolutionary changes to every field. The rapid development of AI also promotes the diagnosis of mechanical equipment. Ever-more researchers seek to develop AI-driven diagnosis methods for diagnosing faults more accurately and rapidly. Many issues such as the interpretability of AI in the diagnosis of mechanical equipment, continual learning for AI-driven diagnosis methods, and the application of large language models to diagnosis have not been well solved. This Special Issue seeks contributions from researchers and industry professionals, aiming to accelerate the development of intelligent perception and diagnosis for mechanical equipment with the help of AI. It invites original research articles, review papers, and case studies that present novel applications of AI for mechanical equipment diagnosis.

Dr. Xuefang Xu
Dr. Zijian Qiao
Dr. Mengdi Li
Dr. Peng Mei
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

  • artificial intelligence
  • machine learning
  • deep learning
  • fault diagnosis
  • mechanical equipment
  • condition monitoring
  • big data-driven fault diagnosis
  • structural health monitoring

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Published Papers (1 paper)

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Research

20 pages, 1615 KiB  
Article
Efficient and Explainable Bearing Condition Monitoring with Decision Tree-Based Feature Learning
by Trong-Du Nguyen, Thanh-Hai Nguyen, Danh-Thanh-Binh Do, Thai-Hung Pham, Jin-Wei Liang and Phong-Dien Nguyen
Machines 2025, 13(6), 467; https://doi.org/10.3390/machines13060467 - 28 May 2025
Viewed by 452
Abstract
Bearings are critical components in rotating machinery, where early fault detection is essential to prevent unexpected failures and reduce maintenance costs. This study presents an efficient and interpretable framework for bearing condition monitoring by combining the Wavelet Packet Transform (WPT)-based feature extraction with [...] Read more.
Bearings are critical components in rotating machinery, where early fault detection is essential to prevent unexpected failures and reduce maintenance costs. This study presents an efficient and interpretable framework for bearing condition monitoring by combining the Wavelet Packet Transform (WPT)-based feature extraction with a Decision Tree (DT) classifier. The WPT technique decomposes vibration signals into multiple frequency bands to extract energy-based features that capture key fault characteristics. Leveraging these features, the DT classifier provides transparent diagnostic rules, enabling a clear understanding of the decision-making process. The proposed method offers a superior balance between diagnostic accuracy, computational efficiency, and explainability compared to conventional black-box models. It is well suited for real-time and resource-constrained industrial applications. Furthermore, feature importance analysis reveals the most influential frequency components associated with different fault types, offering valuable insights for predictive maintenance strategies. The proposed WPT-DT framework represents a practical and scalable solution for intelligent fault diagnosis in the context of Industry 4.0 and smart maintenance systems. Full article
(This article belongs to the Special Issue AI-Driven Intelligent Perception and Diagnosis of Mechanical Equipment)
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