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Sensor Data-Driven Fault Diagnosis Techniques
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Sensor Data-Driven Fault Diagnosis Techniques

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

Deadline for manuscript submissions: 20 November 2025 | Viewed by 1622

Special Issue Editor

Prof. Dr. Jang Woo Kwon

E-Mail Website
Guest Editor
Department of Computer Engineering, Inha University, Inchon, Republic of Korea
Interests: computer & information technology; fault diagnosis

Special Issue Information

Dear Colleagues,

Recent advancements in sensor technology and data analytics have significantly improved the accuracy, reliability, and responsiveness of fault diagnosis systems across various domains, including manufacturing, transportation, healthcare, energy, and smart infrastructure. As sensors become increasingly ubiquitous and capable of generating high-resolution, real-time data, sensor-driven approaches are emerging as a core enabler of intelligent fault detection, identification, and prediction. 

This Special Issue is highly relevant to the scope of Sensors, as it focuses on the pivotal role of sensor data in developing advanced fault diagnosis techniques. We invite high-quality submissions that present innovative methodologies, algorithms, and applications leveraging sensor data for fault diagnosis. Contributions may address theoretical foundations, algorithmic developments, data-driven modeling, and practical implementations in real-world systems. 

This Special Issue welcomes innovative research on sensor data utilization, from advanced sensing hardware design to interpretable AI-driven analytics. Potential topics include but are not limited to:

  • Multimodal sensor data fusion for cross-domain diagnosis
  • Predictive maintenance with distributed sensor networks
  • Sensor data transmission for fault diagnosis
  • Self-supervised learning from unlabeled sensor data
  • Edge-cloud collaborative diagnosis frameworks

Prof. Dr. Jang Woo Kwon
Guest Editor

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

  • fault diagnosis
  • intelligent monitoring
  • predictive maintenance
  • sensor data analytics
  • machine learning for fault detection

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Published Papers (3 papers)

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Research

17 pages, 4091 KiB  
Article
Novel Physics-Informed Indicators for Leak Detection in Water Supply Pipelines
by Yi Zhang and Suzhen Li
Sensors 2025, 25(16), 5069; https://doi.org/10.3390/s25165069 - 15 Aug 2025
Abstract
Accurate monitoring of leakage in urban water supply pipelines is crucial for ensuring the safety of residential water usage. This study proposes a robust physical indicator for identifying leaks in urban water pipelines, grounded in the physical background of leakage noise sources. An [...] Read more.
Accurate monitoring of leakage in urban water supply pipelines is crucial for ensuring the safety of residential water usage. This study proposes a robust physical indicator for identifying leaks in urban water pipelines, grounded in the physical background of leakage noise sources. An integral form of the leakage source noise power spectral density is established, and a rigorous theoretical analysis leads to the development of an effective physical indicator. This indicator addresses the limitation of existing leakage detection methods that overly rely on data-driven features. Experiments were conducted to validate the effectiveness and robustness of the proposed indicator. The results show that the leakage detection models trained with physical features achieved recognition accuracies of 99.89% for Support Vector Machine (SVM) and 99.97% for eXtreme Gradient Boosting (XGBoost) in the experiments. In the field test conducted on an in-service water supply pipeline with a total length of 701 m, the recognition accuracies for SVM and XGBoost were 97.92% and 99.31%, respectively. Full article
(This article belongs to the Special Issue Sensor Data-Driven Fault Diagnosis Techniques)
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25 pages, 3827 KiB  
Article
Source-Free Domain Adaptation Framework for Rotary Machine Fault Diagnosis
by Hoejun Jeong, Seungha Kim, Donghyun Seo and Jangwoo Kwon
Sensors 2025, 25(14), 4383; https://doi.org/10.3390/s25144383 - 13 Jul 2025
Viewed by 670
Abstract
Intelligent fault diagnosis for rotary machinery often suffers performance degradation under domain shifts between training and deployment environments. To address this, we propose a robust fault diagnosis framework incorporating three key components: (1) an order-frequency-based preprocessing method to normalize rotational variations, (2) a [...] Read more.
Intelligent fault diagnosis for rotary machinery often suffers performance degradation under domain shifts between training and deployment environments. To address this, we propose a robust fault diagnosis framework incorporating three key components: (1) an order-frequency-based preprocessing method to normalize rotational variations, (2) a U-Net variational autoencoder (U-NetVAE) to enhance adaptation through reconstruction learning, and (3) a test-time training (TTT) strategy enabling unsupervised target domain adaptation without access to source data. Since existing works rarely evaluate under true domain shift conditions, we first construct a unified cross-domain benchmark by integrating four public datasets with consistent class and sensor settings. The experimental results show that our method outperforms conventional machine learning and deep learning models in both F1-score and recall across domains. Notably, our approach maintains an F1-score of 0.47 and recall of 0.51 in the target domain, outperforming others under identical conditions. Ablation studies further confirm the contribution of each component to adaptation performance. This study highlights the effectiveness of combining mechanical priors, self-supervised learning, and lightweight adaptation strategies for robust fault diagnosis in the practical domain. Full article
(This article belongs to the Special Issue Sensor Data-Driven Fault Diagnosis Techniques)
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23 pages, 6050 KiB  
Article
A Digital Signal Processing-Based Multi-Channel Acoustic Emission Acquisition System with a Simplified Analog Front-End
by Gan Tang
Sensors 2025, 25(10), 3206; https://doi.org/10.3390/s25103206 - 20 May 2025
Viewed by 744
Abstract
Advanced multi-channel acoustic emission (AE) monitoring systems often rely on complex and costly architectures, especially those requiring custom FPGA-based hardware. In this work, we present a digital signal processing (DSP)-based approach to high-performance AE acquisition, implemented using a simplified analog front-end (AFE) and [...] Read more.
Advanced multi-channel acoustic emission (AE) monitoring systems often rely on complex and costly architectures, especially those requiring custom FPGA-based hardware. In this work, we present a digital signal processing (DSP)-based approach to high-performance AE acquisition, implemented using a simplified analog front-end (AFE) and a commercially available synchronous data acquisition (DAQ) card (NI USB-6356). This design eliminates the need for specialized FPGA development, improving accessibility and reducing system complexity. A key feature of the system is the replacement of traditional analog filters with a software-defined digital band-pass filtering module implemented in LabVIEW. This allows for real-time or post-processing filtering with adjustable parameters, enhancing flexibility in data analysis. The system supports 8-channel synchronous sampling at 1.25 MS/s, and performance evaluations demonstrate a dynamic range of 79.22 dB and a signal-to-noise ratio (SNR) of 85.39 dB. These results confirm the system’s ability to maintain high fidelity in AE signal acquisition without the need for dedicated hardware filtering or custom DAQ hardware. The proposed method offers a practical and validated alternative for multi-channel AE monitoring, with potential applications in structural health monitoring, materials testing, and other engineering domains. Full article
(This article belongs to the Special Issue Sensor Data-Driven Fault Diagnosis Techniques)
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