Intelligent Maintenance and Health Management in Smart Manufacturing

Topic Information

Dear Colleagues,

The advent of Industry 4.0 and smart manufacturing has placed unprecedented demands regarding the reliability, availability, and efficiency of industrial systems. Unexpected equipment failures lead to significant economic losses, safety hazards, and operational downtime. To address these challenges, intelligent maintenance and Prognostics and Health Management (PHM) have emerged as critical paradigms, shifting from traditional reactive or schedule-based maintenance to data-driven, predictive, and proactive strategies.

This Topic aims to explore cutting-edge research and innovative applications at the intersection of advanced data analytics and industrial systems. We welcome contributions that leverage modern techniques such as deep learning, signal processing, and large language models to enhance condition monitoring, anomaly detection, fault diagnosis, and remaining useful life (RUL) prediction. In particular, we focus on the development and implementation of digital twins as a core enabler for real-time system simulation, health assessment, and decision-making. We seek high-quality original research and review articles that demonstrate novel methodologies, frameworks, or case studies applied to key components and systems in smart manufacturing, including but not limited to bearings, wind turbines, gearboxes, and batteries. The goal is to compile a collection of works that bridge the gap between theoretical advancements and practical, scalable solutions for the next generation of industrial maintenance.

The Topic will cover, but is not limited to, the following topics:

• Multi-sensor fusion and signal processing for comprehensive condition monitoring in dynamic environments;
• Advanced vibration and thermal imaging analysis for early-stage fault detection in industrial machinery;
• Deep Learning for fault classification, incipient fault identification, and RUL prediction in complex systems;
• Physics-Informed Neural Networks (PINNs) integrating mechanistic knowledge with data-driven approaches;
• Large Models (LMs) for processing maintenance logs, technical reports, and multimodal industrial data;
• Digital Twin frameworks for real-time simulation and virtual sensing;
• Optimization of operational workflows through AI-driven predictive analytics to minimize downtime;
• Uncertainty quantification and robust decision-making in complex systems.

Dr. Zhuyun Chen
Dr. Junyu Qi
Dr. Yafei Xu
Dr. Fei Jiang
Dr. Dimitrios Alexios Karras
Dr. Chong Chen
Dr. Yujie Zhang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Journal of Manufacturing and Materials Processing jmmp	3.3	5.2	2017	15.9 Days	CHF 1800	Submit
Machines machines	2.5	4.7	2013	17.6 Days	CHF 2400	Submit
Processes processes	2.8	5.5	2013	14.9 Days	CHF 2400	Submit
Technologies technologies	3.6	8.5	2013	19.1 Days	CHF 1800	Submit

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Published Papers (1 paper)

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All Applied Sciences JMMP Machines Processes Technologies

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30 pages, 2384 KB  

Open AccessReview

Applications of Deep Learning to Metal Surface Defect Detection: Status and Challenges

by Yizhe Wang, Mengchu Zhou, Chenyang Zhang and Khaled Sedraoui

Processes 2026, 14(8), 1305; https://doi.org/10.3390/pr14081305 - 19 Apr 2026

Viewed by 566

Abstract

The detection technology for metal surface defects plays a crucial role in improving metal product quality and production efficiency in various manufacturing and 3-D printing factories. Metal defect detection faces scale variation and irregular shapes, which limit the adaptability of general object detection [...] Read more.

The detection technology for metal surface defects plays a crucial role in improving metal product quality and production efficiency in various manufacturing and 3-D printing factories. Metal defect detection faces scale variation and irregular shapes, which limit the adaptability of general object detection models in industrial scenarios. Deep learning-based methods are widely used for metal surface defect detection due to their strong adaptability and high automation. Yet, their existing studies pay limited attention to adaptability, evaluation, and recommendations across different detection methods for metal surface defects. This work mainly discusses YOLO, R-CNN, and transformers, as well as FPN, and analyzes their applications in metal surface defect detection according to their respective characteristics, to provide guidance for future research. YOLO has advantages in real-time industrial online detection, while R-CNN and transformer models show potential advantages in handling complex defect cases. Additionally, this work summarizes commonly used datasets and evaluation metrics for metal surface defect detection and analyzes the benchmark performance of different types of detection methods. It also discusses future research directions, including the current status and improvement paths of different models in terms of accuracy, real-time performance, and adaptability. Future models should focus on balancing accuracy and real-time performance, exploring new hybrid architectures, and improving adaptability to different metal surface defects to support further development in this field. Full article

(This article belongs to the Topic Intelligent Maintenance and Health Management in Smart Manufacturing)

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