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Intelligent Sensing, Condition Monitoring, and Maintenance for Complex Industrial Systems
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Intelligent Sensing, Condition Monitoring, and Maintenance for Complex Industrial Systems

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

Deadline for manuscript submissions: 1 December 2026 | Viewed by 1506

Special Issue Editors

Dr. Junyu Qi

E-Mail Website
Guest Editor
Reutlingen Research Institute, Reutlingen University, 72762 Reutlingen, Germany
Interests: anomaly detection and fault prognostics; condition monitoring; industrial AI; large scale foundation model
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoxi Hu

E-Mail Website
Guest Editor
State Key Laboratory of Intelligent Green Vehicle and Mobility, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Interests: artificial intelligence; transportation engineering; railway engineering; control systems engineering; condition monitoring; fault diagnosis; fault detection; remaining useful life prediction; computer vision; object detection; image segmentation; transport engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dandan Peng

E-Mail Website
Guest Editor
School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
Interests: AI-based fault diagnosis and prognosis; health management; complex industrial systems
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Chen

E-Mail Website
Guest Editor
College of Engineering, Shantou University, Shantou 515063, China
Interests: signal and acoustic information processing; intelligent interaction; fault prediction and health management

Special Issue Information

Dear Colleagues,

The scope of this Special Issue aligns closely with the aims of Sensors, addressing the emerging role of advanced AI techniques in Prognostics and Health Management (PHM) for enhancing the reliability and safety of industrial assets. This Special Issue will invite contributions that advance both theory and practice, covering (but not limited to) the following aspects:

  • Monitoring
    • Real-time condition monitoring of industrial equipment;
    • Sensor fusion and measurement techniques in harsh environments.
  • Modeling
    • Physics-informed or hybrid modeling for complex systems;
    • Digital twin-enabled monitoring and decision-making.
  • AI
    • AI approaches for fault diagnosis and predictive maintenance;
    • Explainable AI for industrial monitoring, fault diagnosis, and prognostics;
    • Large-scale foundation models for intelligent maintenance and condition monitoring.
  • Applications
    • Applications in manufacturing, energy, transportation, and robotics;
    • Case studies on complex industrial systems.

Dr. Junyu Qi
Dr. Xiaoxi Hu
Dr. Dandan Peng
Dr. Peng Chen
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. 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

  • intelligent sensing
  • predictive maintenance
  • digital twins
  • prognostics and health management (PHM)

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

Published Papers (2 papers)

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Research

21 pages, 2799 KB  
Article
An Intelligent Condition-Monitoring Framework for Alkaline Water Electrolyzers Based on Hybrid Physics-Informed Health Indicators
by Jie Liu, Zhiying Wang, Tingting Ma, Xinyue Chen, Zihao Wang, Chao Huang and Yiyang Dai
Sensors 2026, 26(4), 1090; https://doi.org/10.3390/s26041090 - 7 Feb 2026
Viewed by 608
Abstract
Alkaline Water Electrolyzers (AWEs) are critical for green hydrogen production but face operational risks due to volatile renewable energy inputs. This study proposes an intelligent condition-monitoring framework that leverages a hybrid physics-informed machine learning (ML) methodology to construct Health Indicators (HIs). The core [...] Read more.
Alkaline Water Electrolyzers (AWEs) are critical for green hydrogen production but face operational risks due to volatile renewable energy inputs. This study proposes an intelligent condition-monitoring framework that leverages a hybrid physics-informed machine learning (ML) methodology to construct Health Indicators (HIs). The core innovation lies in addressing the challenge of inaccessible internal states. First, a high-fidelity Computational Fluid Dynamics (CFD) model is developed and experimentally validated, serving as a physics-informed data generator to simulate multiphysics behavior under various operating and fault conditions. From this reliable simulation basis, a comprehensive dataset is produced, and eight key operational parameters are derived as HIs. This dataset is then used to train and benchmark three ML models for rapid health state classification. The Multilayer Perceptron (MLP) model achieves superior performance with 90.43% accuracy, effectively translating the validated physical understanding into a fast, deployable intelligent monitoring agent. This work presents a viable pathway for constructing reliable HIs and implementing AI-enhanced condition monitoring for AWEs, contributing to safer and more efficient green hydrogen production. Full article
(This article belongs to the Special Issue Intelligent Sensing, Condition Monitoring, and Maintenance for Complex Industrial Systems)
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20 pages, 5876 KB  
Article
Dynamic Die-Forging Scene Semantic Segmentation via Point Cloud–BEV Feature Fusion with Star Encoding
by Xuewen Feng, Aiming Wang, Guoying Meng, Yiyang Xu, Jie Yang, Xiaohan Cheng, Yijin Xiong and Juntao Wang
Sensors 2026, 26(2), 708; https://doi.org/10.3390/s26020708 - 21 Jan 2026
Viewed by 506
Abstract
Semantic segmentation of workpieces and die cavities is critical for intelligent process monitoring and quality control in hammer die-forging. However, the field of 3D point cloud segmentation currently faces prominent limitations in forging scenario adaptation: existing state-of-the-art (SOTA) methods are predominantly optimized for [...] Read more.
Semantic segmentation of workpieces and die cavities is critical for intelligent process monitoring and quality control in hammer die-forging. However, the field of 3D point cloud segmentation currently faces prominent limitations in forging scenario adaptation: existing state-of-the-art (SOTA) methods are predominantly optimized for road driving or indoor scenes, where targets have stable poses and regular surfaces. They lack dedicated designs for capturing the fine-grained deformation characteristics of forging workpieces and alleviating multi-scale feature misalignment caused by large pose variations—key pain points in forging segmentation. Consequently, these methods fail to balance segmentation accuracy and real-time efficiency required for practical forging applications. To address this gap, this paper proposes a novel semantic segmentation framework fusing 3D point cloud and bird’s-eye-view (BEV) representations for complex die-forging scenes. Specifically, a Star-based encoding module is designed in the BEV encoding stage to enhance capture of fine-grained workpiece deformation characteristics. A hierarchical feature-offset alignment mechanism is developed in decoding to alleviate multi-scale spatial and semantic misalignment, facilitating efficient cross-layer fusion. Additionally, a weighted adaptive fusion module enables complementary information interaction between point cloud and BEV modalities to improve precision.We evaluate the proposed method on our self-constructed simulated and real die-forging point cloud datasets. The results show that when trained solely on simulated data and tested directly in real-world scenarios, our method achieves an mIoU that surpasses RPVNet by 1.1%. After fine-tuning with a small amount of real data, the mIoU further improves by 5%, reaching optimal performance. Full article
(This article belongs to the Special Issue Intelligent Sensing, Condition Monitoring, and Maintenance for Complex Industrial Systems)
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