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AI-Enabled Smart Sensors for Industry Monitoring and Fault Diagnosis

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

Deadline for manuscript submissions: 25 January 2027 | Viewed by 4457

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Special Issue Editors

Dr. Wen-qiang Liu

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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

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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

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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

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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

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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.

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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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Published Papers (6 papers)

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Research

18 pages, 9279 KB

Open AccessArticle

CNN Bearing Fault Diagnosis Based on Symmetric Point Pattern Feature Fusion with Multi-Source Resonance Sparse Components

by Yan Liu, Yuxuan Li, Qiang Sun, Lingrui Yang, Qitong Jia, Xiaoxun Zhu, Yan Yang and Panpan Yang

Sensors 2026, 26(10), 2995; https://doi.org/10.3390/s26102995 - 9 May 2026

Viewed by 526

Abstract

To address the issue of low recognition accuracy caused by incomplete information, a CNN-based fault diagnosis method for rolling bearings using multi-source resonance sparse component feature fusion (RSSD-P) is proposed in this paper, which effectively resolves the problem of impact features being masked. In noise-contaminated environments, bearing vibration signals exhibit nonstationarity, obscuring fault characteristics. To overcome this, resonance sparse decomposition was employed to extract impact-related fault features. Furthermore, to fully utilize multi-sensor information and enhance fault representation, a symmetric dot pattern (SDP) method was introduced to fuse multi-source fault impact features, achieving effective integration of impact characteristics from multi-source vibration signals. A CNN-based approach incorporating multi-source resonance sparse component and SDP feature fusion was developed, and a bearing fault diagnosis model was established accordingly. Experimental results demonstrate that the proposed method achieves a fault recognition accuracy of 98.63% under varying operating conditions. Compared with other bearing fault diagnosis methods, the recognition precision is improved by 8.49%~17.8%, confirming its superior performance. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensors for Industry Monitoring and Fault Diagnosis)

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29 pages, 6442 KB

Open AccessArticle

Unsupervised Acoustic Anomaly Detection for Rotating Machinery Under Submarine-like Environments: Considering Data Scarcity and Background Noise via Proxy Data Generation

by Kwang Sik Kim and Jang Hyun Lee

Sensors 2026, 26(9), 2659; https://doi.org/10.3390/s26092659 - 24 Apr 2026

Viewed by 675

Abstract

This study proposes a noise-robust unsupervised acoustic anomaly detection framework for early identification of abnormal operating conditions in rotating machinery under submarine-like environments with severe data scarcity. In such environments, underwater background noise and onboard interference sources significantly degrade signal quality, while limited computing resources constrain the deployment of high-complexity deep learning models. To address the lack of labeled fault data, the publicly available MIMII dataset was adopted as a proxy platform, and representative submarine interference sources were physically modeled, including colored background noise, structure-borne resonance, band-limited auxiliary noise, tonal components, and sensor noise. These components were combined and scaled to predefined SNR levels (−6 to 6 dB) to generate realistic noise-augmented data. Three unsupervised approaches were compared under edge deployment constraints: (i) Gaussian Mixture Model (GMM) with statistical MFCC features, (ii) statistical-feature-based Ensemble Autoencoder, and (iii) Conv1D-based Ensemble Autoencoder using 1-s log Mel-spectrogram segments. Performance was evaluated in terms of AUC, F1-score, and computational cost. Results show that GMM provides competitive detection performance with minimal computational burden, whereas Conv1D achieves superior accuracy when temporal fault patterns dominate, at the expense of higher complexity. The study provides practical design guidelines for acoustic anomaly detection under multi-noise and resource-constrained conditions. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensors for Industry Monitoring and Fault Diagnosis)

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29 pages, 23265 KB

Open AccessArticle

Machine-Learning-Based Color Sensing Using Wearable SENSIPATCH Spectrometer Module: An Experimental Study

by Hamza Mustafa, Federico Fina, Mario Molinara, Luigi Ferrigno, Andrea Ria, Paolo Bruschi, Simone Contardi, Fabio Leccese and Hafiz Tayyab Mustafa

Sensors 2026, 26(9), 2576; https://doi.org/10.3390/s26092576 - 22 Apr 2026

Viewed by 249

Abstract

Accurate color classification plays a critical role across diverse fields, from textile manufacturing and environmental monitoring to biomedical diagnostics. This study introduces a machine-learning-driven approach to spectral color sensing using SENSIPATCH, a compact, wearable sensor system; while SENSIPATCH integrates multiple sensing modalities, including bioimpedance, electrochemical, thermal, humidity, and vibrational sensors, this work specifically utilizes its spectrometer module, which comprises multi-wavelength LEDs and photodiodes. Targeting the classification of 100 standardized PANTONE colors, the proposed framework is evaluated under controlled lighting conditions to ensure repeatable spectral acquisition. The experimental design includes both firm and loose contact scenarios to emulate variability in wearable placement. A structured data-preprocessing pipeline involving baseline correction, bootstrapping, and Z-score normalization was employed to enhance signal quality and improve model generalization. Five machine learning models were evaluated: Random Forest, SVM, MLP, CNN, and LSTM. The MLP demonstrated the strongest classification performance. Notably, the MLP achieved consistent accuracy across both contact conditions, indicating robustness against sensor placement variations. These results highlight the feasibility of compact LED-based wearable spectroscopy for reliable color classification under controlled measurement conditions, providing a baseline for future extensions to more diverse lighting conditions. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensors for Industry Monitoring and Fault Diagnosis)

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19 pages, 3294 KB

Open AccessArticle

UAV-Based Oil Leakage Spot Detection Under Complex Illumination via a Collaborative Low-Light Enhancement and Detection Framework

by Yunsheng Ha, Ling Zhao and Huili Zhang

Sensors 2026, 26(6), 1819; https://doi.org/10.3390/s26061819 - 13 Mar 2026

Viewed by 385

Abstract

Accurate detection of oil leakage spots is essential for oilfield safety and environmental protection. However, UAV-based inspection in onshore oilfields often suffers from complex illumination conditions, such as low light, backlighting, and mixed shadows, which simultaneously degrade image visibility and obscure leakage-sensitive features, thereby causing missed detection of minute and weak-texture oil leakage targets. Unlike generic low-light enhancement or object detection tasks, the core challenge of onshore UAV oil leakage inspection lies in preserving leakage-oriented fine cues during enhancement while improving the detector’s ability to distinguish leakage targets from highly confusing oilfield backgrounds. To address this task-specific challenge, we propose a collaborative low-light enhancement and detection framework that jointly optimizes leakage-detail-preserving enhancement and multi-scale interference-suppressed detection. Specifically, an improved Retinex-based enhancement network is designed by integrating multi-scale feature aggregation, NAFNet-based denoising, and a CBAM attention mechanism to enhance brightness while preserving leakage details. The enhanced images are then fed into an improved YOLOv11 detector, where an AC-FPN module is adopted to strengthen multi-scale feature fusion and suppress background interference. Experiments on UAV oilfield datasets demonstrate that the proposed method achieves a precision of 94.25% and a mean average precision (mAP) of 87.54%, outperforming existing approaches. The proposed framework provides an effective and robust solution for oil leakage spot detection under complex illumination. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensors for Industry Monitoring and Fault Diagnosis)

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25 pages, 14250 KB

Open AccessArticle

AI-Based 3D Modeling Strategies for Civil Infrastructure: Quantitative Assessment of NeRF and Photogrammetry

by Edison Atencio, Fabrizzio Duarte, Fidel Lozano-Galant, Rocio Porras and Ye Xia

Sensors 2026, 26(3), 852; https://doi.org/10.3390/s26030852 - 28 Jan 2026

Viewed by 917

Abstract

Three-dimensional (3D) modeling technologies are increasingly vital in civil engineering, providing precise digital representations of infrastructure for analysis, supervision, and planning. This study presents a comparative assessment of Neural Radiance Fields (NeRFs) and digital photogrammetry using a real-world case study involving a terrace at the Civil Engineering School of the Pontificia Universidad Católica de Valparaíso. The comparison is motivated by the operational complexity of image acquisition campaigns, where large image datasets increase flight time, fieldwork effort, and survey costs. Both techniques were evaluated across varying levels of data availability to analyze reconstruction behavior under progressively constrained image acquisition conditions, rather than to propose new algorithms. NeRF and photogrammetry were compared based on visual quality, point cloud density, geometric accuracy, and processing time. Results indicate that NeRF delivers fast, photorealistic outputs even with reduced image input, enabling efficient coverage with fewer images, while photogrammetry remains superior in metric accuracy and structural completeness. The study concludes by proposing an application-oriented evaluation framework and potential hybrid workflows to guide the selection of 3D modeling technologies based on specific engineering objectives, survey design constraints, and resource availability while also highlighting how AI-based reconstruction methods can support emerging digital workflows in infrastructure monitoring under variable or limited data conditions. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensors for Industry Monitoring and Fault Diagnosis)

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20 pages, 5150 KB

Open AccessArticle

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 1006

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 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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