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Advances in Industrial Artificial Intelligence for Smart Manufacturing and Sustainable Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: 15 January 2026 | Viewed by 619

Special Issue Editor

Dr. Nianbo Liu

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: artificial intelligence; computer networks

Special Issue Information

Dear Colleagues,

Industrial Artificial Intelligence (AI) is revolutionizing modern manufacturing, supply chains, and energy management by enabling intelligent automation, real-time decision-making, and enhanced operational efficiency. With advancements in machine vision, predictive maintenance, real-time scheduling, and low-carbon optimization, Industrial AI has become a key driver of digital transformation in various industries. This Special Issue aims to explore cutting-edge AI methodologies and their integration with sensor technologies for industrial applications. Smart sensors, combined with AI-driven analytics, are enabling unprecedented levels of precision in monitoring, diagnosing, and optimizing industrial processes. From predictive maintenance using IoT-enabled sensors to AI-driven quality control and energy-efficient production, the convergence of AI and sensor technologies is reshaping industrial landscapes. This Special Issue welcomes original research and review articles on novel AI algorithms, sensor-based industrial monitoring, and real-world applications that enhance automation, sustainability, and efficiency in industrial settings.

Dr. Nianbo Liu
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • industrial Artificial Intelligence
  • sensor-based industrial monitoring
  • industrial settings automation, sustainability, and efficiency

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Published Papers (1 paper)

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Research

22 pages, 1785 KB  
Article
LA-EAD: Simple and Effective Methods for Improving Logical Anomaly Detection Capability
by Zhixing Li, Zan Yang, Lijie Zhang, Lie Yang and Jiansheng Liu
Sensors 2025, 25(16), 5016; https://doi.org/10.3390/s25165016 - 13 Aug 2025
Viewed by 430
Abstract
In the field of intelligent manufacturing, image anomaly detection plays a pivotal role in automated product quality inspection. Most existing anomaly detection methods are adept at capturing local features of images, achieving high detection accuracy for structural anomalies such as cracks and scratches. [...] Read more.
In the field of intelligent manufacturing, image anomaly detection plays a pivotal role in automated product quality inspection. Most existing anomaly detection methods are adept at capturing local features of images, achieving high detection accuracy for structural anomalies such as cracks and scratches. However, logical anomalies typically appear normal within local regions of an image and are difficult to represent well by the anomaly score map, requiring the model to possess the capability to extract global context features. To address this challenge while balancing the detection of both structural and logical anomalies, this paper proposes a lightweight anomaly detection framework built upon EfficientAD. This framework integrates the reconstruction difference constraint (RDC) and a logical anomaly detection module. Specifically, the original EfficientAD relies on the coarse-grained reconstruction difference between the student and the autoencoder to detect logical anomalies; but, false detection may be caused by the local fine-grained reconstruction difference between the two models. RDC can promote the consistency of the fine-grained reconstruction between the student and the autoencoder, thereby effectively alleviating this problem. Furthermore, in order to detect anomalies that are difficult to represent by feature maps more effectively, the proposed logical anomaly detection module extracts and aggregates the context features of the image, and combines the feature-based method to calculate the overall anomaly score. Extensive experiments demonstrate our method’s significant improvement in logical anomaly detection, achieving 94.2 AU-ROC on MVTec LOCO, while maintaining strong structural anomaly detection performance at 98.4 AU-ROC on MVTec AD. Compared to the baseline, like EfficientAD, our framework achieves a state-of-the-art balance between both anomaly types. Full article
(This article belongs to the Special Issue Advances in Industrial Artificial Intelligence for Smart Manufacturing and Sustainable Systems)
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