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Machines
Special Issues
Artificial Intelligence for Diagnosis, Detection, Monitoring and Maintenance
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Artificial Intelligence for Diagnosis, Detection, Monitoring and Maintenance

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1712

Special Issue Editors

Dr. Xuefeng Ni

E-Mail Website
Guest Editor
School of Artificial Intelligence and Robotics, Hunan University, Changsha, China
Interests: artificial intelligence; computer vision; industrial anomaly detection; robotic perception and control; multimodal information fusion
Dr. Kechen Song

E-Mail Website
Guest Editor
School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China
Interests: applied surface science; vision detection for surface defects; multi-modal image analysis and application
Special Issues, Collections and Topics in MDPI journals
Dr. Yunkang Cao

E-Mail Website
Guest Editor
School of Artificial Intelligence and Robotics, Hunan University, Changsha, China
Interests: visual anomaly detection; image generation; multimodal large language models; defect detection; computer vision

Special Issue Information

Dear Colleagues,

Artificial intelligence is transforming how machines are diagnosed, monitored, and maintained across sectors, from manufacturing and energy to transportation and civil infrastructure. Building on decades of model‑based diagnostics and condition monitoring, recent advances in deep learning, multimodal sensing, and edge computing now enable robust, real‑time decisions to be made when there is still uncertainty. This Special Issue aims to collate cutting‑edge research that bridges algorithms and deployment, emphasizing reliability, data efficiency, and interpretability.

We welcome contributions that focus on the following topics: (i) foundations and benchmarks for fault diagnosis, anomaly/defect detection, prognostics, and health management; (ii) infrastructure status monitoring using multimedia data (vision, audio, vibration, thermal, radar, and fused modalities); (iii) self‑supervised, few/zero‑shot, and federated learning for scarce or private data; (iv) trustworthy AI—uncertainty quantification, explainability, robustness, and safety; (v) edge/cloud architectures, digital twins, and physics‑informed learning for scalable maintenance; and (vi) lifecycle considerations, including dataset shift, continual learning, and standards.

We solicit the submission of rigorous original research, comprehensive surveys, reproducible benchmarks, and impactful application studies. Submissions should clearly articulate problem definitions, validated methodology, and practical implications, preferably with open datasets or code. Case studies in fault diagnosis, defect/anomaly detection, predictive maintenance, and infrastructure monitoring using multimodal/multimedia data are especially encouraged.

Dr. Xuefeng Ni
Dr. Kechen Song
Dr. Yunkang Cao
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
  • anomaly and defect detection
  • multimodal/multimedia sensing
  • infrastructure monitoring
  • industrial vision inspection
  • predictive maintenance
  • digital twins
  • edge intelligence
  • self‑supervised learning
  • uncertainty and explainability
  • robust and trustworthy AI

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

Published Papers (3 papers)

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Research

30 pages, 8722 KB  
Article
MulPViT-SimAM: An Electronic Substrate Defect Detection Framework for Addressing Class Imbalance Problems
by Yuting Wang, Liming Sun, Bang An and Ruiyun Yu
Machines 2026, 14(4), 456; https://doi.org/10.3390/machines14040456 - 20 Apr 2026
Viewed by 268
Abstract
As the cornerstone of contemporary electronics, the quality of electronic substrates—including Printed Circuit Boards (PCBs) and Ceramic Package Substrates (CPSs)—is intrinsic to product reliability. However, automated inspection is currently impeded by two persistent obstacles: the drastic multi-scale variation in defects and the acute [...] Read more.
As the cornerstone of contemporary electronics, the quality of electronic substrates—including Printed Circuit Boards (PCBs) and Ceramic Package Substrates (CPSs)—is intrinsic to product reliability. However, automated inspection is currently impeded by two persistent obstacles: the drastic multi-scale variation in defects and the acute class imbalance within defect datasets. Conventional deep learning approaches often fail to reconcile these challenges simultaneously, leading to suboptimal recognition of rare defect categories. To bridge this gap, we propose Multi-scale Partial Vision Transformer—Simple, Parameter-free Attention Module (MulPViT-SimAM), a robust framework designed for class-imbalanced electronic substrate defect detection. Our method features a novel multi-scale backbone (MulPViT) that synergizes partial convolutions with hierarchical attention mechanisms, facilitating the efficient extraction of both fine-grained local textures and global contextual dependencies. Additionally, we embed the Simple, Parameter-free Attention Module (SimAM) into the feature fusion stage to adaptively highlight defect-specific features while dampening background noise. To further mitigate data imbalance, we utilize the Equalized Focal Loss (EFL) function, which employs a category-specific modulating factor to dynamically equilibrate the learning focus across different classes. Comprehensive benchmarking reveals state-of-the-art performance, achieving mAP@0.5 scores of 95.7% on the standard PKU-MARKET-PCB dataset and 54.2% on the highly challenging CPS2D-AD dataset. Significantly, our approach effectively mitigates class imbalance, narrowing the performance deviation of rare categories to just 4.3% on the PKU-Market-PCB dataset and 1.4% on the CPS2D-AD dataset, compared to 11.8% and 7.5% in baseline models. These findings position MulPViT-SimAM as a viable and efficient solution for industrial quality control. Full article
(This article belongs to the Special Issue Artificial Intelligence for Diagnosis, Detection, Monitoring and Maintenance)
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24 pages, 13317 KB  
Article
Hybrid-Mechanism Deep Learning Modeling for Machine Tool Thermal Error: Robust Prediction via Few-Sample Learning
by Hongru Chen, Yubin Huang, Chaochao Qiu, Xueyan Ning, Pingjiang Wang and Ke Yang
Machines 2026, 14(4), 399; https://doi.org/10.3390/machines14040399 - 6 Apr 2026
Viewed by 355
Abstract
To address spindle thermal error in precision machining, this study proposes a hybrid modeling method. It combines a physical model for linear deformation with a GAT-LSTM network. Experiments show the hybrid model achieved RMSE/MAE of 4.6/4.0 µm under full training (12 conditions), 5.5/4.9 [...] Read more.
To address spindle thermal error in precision machining, this study proposes a hybrid modeling method. It combines a physical model for linear deformation with a GAT-LSTM network. Experiments show the hybrid model achieved RMSE/MAE of 4.6/4.0 µm under full training (12 conditions), 5.5/4.9 µm under 3 training condition and 4.8/4.3 µm under 1 training condition, substantially reducing the data requirements for thermal error modeling. The compensation experiment conducted using a high real-time surrogate-model-based architecture reduced thermal error by 78% (from 54 µm to 12 µm), demonstrating high precision and minimal data requirements suitable for real-time applications. Full article
(This article belongs to the Special Issue Artificial Intelligence for Diagnosis, Detection, Monitoring and Maintenance)
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28 pages, 3933 KB  
Article
ESI-YOLOv11n: Efficient Multi-Scale Fusion Method for PCB Defect Detection
by Chuxin Liu, Wenjing Liu and Linguang Lian
Machines 2026, 14(2), 240; https://doi.org/10.3390/machines14020240 - 20 Feb 2026
Viewed by 598
Abstract
The printed circuit board (PCB), a core component of electronic products, is playing an increasingly critical role in quality defect detection. Traditional methods suffer from low efficiency and high missed detection rates, rendering them insufficient to meet the industrial requirements for PCB defect [...] Read more.
The printed circuit board (PCB), a core component of electronic products, is playing an increasingly critical role in quality defect detection. Traditional methods suffer from low efficiency and high missed detection rates, rendering them insufficient to meet the industrial requirements for PCB defect detection. To address this issue, this paper proposes an ESI-YOLOv11n model for PCB defect detection that incorporates multi-scale feature fusion. The specific improvements are as follows: First, Spatial and Channel Reconstruction Convolution (ScConv) is incorporated to optimize the C3k2 module, creating a dynamic adaptive feature extraction unit that strengthens its ability to capture features of small defects. Second, an Efficient Multi-Scale Attention (EMA) mechanism is integrated into the Neck layer, dynamically adjusting the weight distribution of multi-scale feature maps to enhance efficiency of feature fusion and improve detection performance. Finally, the Inner concept is integrated with the CIoU loss function, resulting in the novel Inner-CIoU loss function. This loss function optimizes the model by utilizing auxiliary box mechanisms and geometric constraints, leading to more accurate regression results. Experimental results show that the improved model achieves an average precision of 95.9% and a recall rate of 93.3%, which are 9.3% and 11.5% higher than those of the original model, respectively, while having a parameter size of only 13.3 Mb. The model effectively reduces the missed detection rate and false detection rate, significantly enhances the PCB defect detection performance, and demonstrates superior comprehensive performance compared with current mainstream detection models. Full article
(This article belongs to the Special Issue Artificial Intelligence for Diagnosis, Detection, Monitoring and Maintenance)
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