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AI-Enabled Smart Sensors for Industry Monitoring and Fault Diagnosis
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AI-Enabled Smart Sensors for Industry Monitoring and Fault Diagnosis

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

Deadline for manuscript submissions: 25 March 2026 | Viewed by 683

Special Issue Editors

Dr. Wen-qiang Liu

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: artificial intelligence algorithms; advanced sensing technologies; anomaly detection; predictive maintenance
Dr. Zhiwei Han

E-Mail Website
Guest Editor
School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
Interests: artificial intelligence; computer vision; imaging; signal processing; applications in fault diagnosis and maintenance of railway infrastructures
Dr. Jun-Fang Wang

E-Mail Website
Guest Editor
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China
Interests: novel AI algorithms for sensor data analysis; anomaly detection; structural health monitoring; railway engineering
Dr. Xiangyun Deng

E-Mail Website
Guest Editor
School of Civil Engineering, Southeast University, Nanjing, China
Interests: intelligent monitoring and diagnosis of wheel-rail damage and degradation

Special Issue Information

Dear Colleagues,

The advent of Industry 4.0 demands intelligent, reliable, and real-time monitoring solutions for complex industrial systems. This Special Issue focuses on the transformative integration of Artificial Intelligence (AI) with smart sensor technologies to revolutionize industrial monitoring and fault diagnosis. We seek high-quality research addressing the design, deployment, and application of AI-driven smart sensors capable of acquiring, processing, and interpreting complex data streams autonomously or at the edge.

Topics of interest include, but are not limited to, the following:

  • Advanced sensing technologies with embedded AI capabilities;
  • Novel AI algorithms for sensor data analysis, anomaly detection, and predictive maintenance;
  • Application of AI-driven smart sensors;
  • Edge AI/edge computing;
  • Deep learning for sensing;
  • Explainable AI (XAI) for industry;
  • Industrial IoT (IIoT) monitoring.

This Issue aims to showcase cutting-edge research bridging sensor technology, AI, and industrial engineering to enhance operational efficiency, safety, and reliability through proactive fault diagnosis and intelligent monitoring. Original research and review articles are invited.

Dr. Wen-qiang Liu
Dr. Zhiwei Han
Dr. Jun-Fang Wang
Dr. Xiangyun Deng
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. Sensors is an international peer-reviewed open access semimonthly 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 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

  • advanced sensing technologies with embedded AI capabilities
  • novel AI algorithms for sensor data analysis, anomaly detection, and predictive maintenance
  • application of AI-driven smart sensors
  • edge AI/edge computing
  • deep learning for sensing
  • explainable AI (XAI) for industry
  • industrial IoT (IIoT) monitoring

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

Published Papers (1 paper)

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Research

20 pages, 5150 KB  
Article
VSM-UNet: A Visual State Space Reconstruction Network for Anomaly Detection of Catenary Support Components
by Shuai Xu, Jiyou Fei, Haonan Yang, Xing Zhao, Xiaodong Liu and Hua Li
Sensors 2025, 25(19), 5967; https://doi.org/10.3390/s25195967 - 25 Sep 2025
Viewed by 384
Abstract
Anomaly detection of catenary support components (CSCs) is an important component in railway condition monitoring systems. However, because the abnormal features of CSCs loosening are not obvious, and the current CNN models and visual Transformer models have problems such as limited remote modeling [...] Read more.
Anomaly detection of catenary support components (CSCs) is an important component in railway condition monitoring systems. However, because the abnormal features of CSCs loosening are not obvious, and the current CNN models and visual Transformer models have problems such as limited remote modeling capabilities and secondary computational complexity, it is difficult for existing deep learning anomaly detection methods to effectively exert their performance. The state space model (SSM) represented by Mamba is not only good at long-range modeling, but also maintains linear computational complexity. In this paper, using the state space model (SSM), we proposed a new visual state space reconstruction network (VSM-UNet) for the detection of CSC loosening anomalies. First, based on the structure of UNet, a visual state space block (VSS block) is introduced to capture extensive contextual information and multi-scale features, and an asymmetric encoder–decoder structure is constructed through patch merging operations and patch expanding operations. Secondly, the CBAM attention mechanism is introduced between the encoder–decoder structure to enhance the model’s ability to focus on key abnormal features. Finally, a stable abnormality score calculation module is designed using MLP to evaluate the degree of abnormality of components. The experiment shows that the VSM-UNet model, learning strategy and anomaly score calculation method proposed in this article are effective and reasonable, and have certain advantages. Specifically, the proposed method framework can achieve an AUROC of 0.986 and an FPS of 26.56 in the anomaly detection task of looseness on positioning clamp nuts, U-shaped hoop nuts, and cotton pins. Therefore, the method proposed in this article can be effectively applied to the detection of CSCs abnormalities. Full article
(This article belongs to the Special Issue AI-Enabled Smart Sensors for Industry Monitoring and Fault Diagnosis)
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