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Advances in Sensors for Online Condition Monitoring and Fault Diagnosis: 2nd Edition

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

Deadline for manuscript submissions: 20 September 2026 | Viewed by 2329

Editors

Prof. Dr. Yuan Yao

E-Mail Website
Guest Editor
Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
Interests: nondestructive testing data analysis; process data analytics; multivariate analysis; machine learning; process monitoring; soft sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Jia-Lin Kang

E-Mail Website
Guest Editor
Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
Interests: artificial intelligence applications to chemical engineering; process design and control; fluid mechanics for scaling up designs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing demand for reliability and efficiency in industrial systems has heightened the importance of online condition monitoring and fault diagnosis. Sensors play a pivotal role in capturing real-time data, enabling the detection and diagnosis of anomalies to prevent system failures and downtime. This Special Issue, “Advances in Sensors for Online Condition Monitoring and Fault Diagnosis: 2nd Edition”, aims to showcase state-of-the-art research and practical applications in this domain.

We invite contributions that explore innovative sensor technologies, advanced data acquisition systems, and integration of artificial intelligence (AI) algorithms to enhance fault detection and diagnostic capabilities. Topics include, but are not limited to, the development of novel sensor materials and designs, AI-based data analytics techniques, edge computing for real-time monitoring, and case studies demonstrating the application of these technologies in industries such as manufacturing, energy, transportation, and construction.

This Special Issue seeks to foster interdisciplinary collaboration and provide a platform for sharing breakthroughs in sensor-based condition monitoring systems. We encourage submissions that emphasize the synergistic use of sensors and AI to address the challenges of real-time monitoring and predictive maintenance.

Prof. Dr. Yuan Yao
Dr. Jia-Lin Kang
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-anonymized 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

  • sensors
  • online condition monitoring
  • fault diagnosis
  • artificial intelligence
  • predictive maintenance
  • edge computing
  • signal processing
  • machine learning
  • industrial applications
  • data analytics

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Related Special Issue

Published Papers (5 papers)

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Research

26 pages, 2537 KB  
Article
Working-Condition-Indexed Generative Domain Generalization for Intelligent Fault Diagnosis Under Unseen Conditions
by Duo Zhao and Fan Yang
Sensors 2026, 26(15), 4693; https://doi.org/10.3390/s26154693 - 23 Jul 2026
Viewed by 327
Abstract
Domain generalization techniques have gained widespread application in cross-domain fault diagnosis in recent years. These methods enhance model generalization capabilities without accessing target domain data, thereby improving fault diagnosis performance under unknown operating conditions. However, most existing approaches overlook the fundamental relationships between [...] Read more.
Domain generalization techniques have gained widespread application in cross-domain fault diagnosis in recent years. These methods enhance model generalization capabilities without accessing target domain data, thereby improving fault diagnosis performance under unknown operating conditions. However, most existing approaches overlook the fundamental relationships between operating conditions across different domains, resulting in directionless domain generalization. To address this limitation, we propose a working-condition-indexed generative domain generalization (WCIGDG) method for fault diagnosis in data-free conditions using given working-condition parameters. Within this framework, a domain index is employed to model relationships between operating conditions across domains. A domain index predictor is constructed to guide the data generation model, enabling the generated data to better adapt to the target operating condition, while adversarial training is introduced to enhance the effectiveness of data generation. Subsequently, the generated data is combined with source domain data to train the fault diagnosis model, strengthening its adaptability to the target operating condition. Experiments are conducted on two publicly available rotating machinery fault diagnosis datasets, namely the CWRU bearing dataset and the WT planetary gearbox dataset, using rotational speed as the domain index across 68 cross-speed domain generalization tasks. The results demonstrate the effectiveness of WCIGDG for both bearing and gear fault diagnosis. Compared with SDGN, the best-performing baseline among the compared methods, WCIGDG improves the average accuracy/F1-Score by 1.22/2.72 percentage points on CWRU and 1.33/1.93 percentage points on WT. Full article
(This article belongs to the Special Issue Advances in Sensors for Online Condition Monitoring and Fault Diagnosis: 2nd Edition)
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41 pages, 13560 KB  
Article
Measurement-Efficient Few-Shot Vibration Fault Diagnosis via Physics-Informed Self-Supervised Learning and Adaptive Early Stopping
by Zongzhe Ni, Xiancheng Ji, Jianjun Yi, Nuozhou Li, Hongxing Wang, Yifan Liu and Ying Yan
Sensors 2026, 26(13), 4252; https://doi.org/10.3390/s26134252 - 4 Jul 2026
Viewed by 337
Abstract
Vibration-based fault diagnosis is widely used for rotating machinery health monitoring, but practical diagnosis is often limited by scarce fault labels and uncertain measurement length. Longer vibration records can improve decision reliability but increase sensing and computational cost, whereas overly short records may [...] Read more.
Vibration-based fault diagnosis is widely used for rotating machinery health monitoring, but practical diagnosis is often limited by scarce fault labels and uncertain measurement length. Longer vibration records can improve decision reliability but increase sensing and computational cost, whereas overly short records may yield unreliable predictions under noise and measurement corruptions. This paper studies few-shot fault diagnosis as a measurement-constrained decision task, in which the model identifies the fault class and determines when sufficient vibration evidence has been acquired. We propose a measurement-efficient diagnosis framework that combines prior knowledge from unlabeled healthy signals, physically constrained augmentation of scarce labeled samples, and adaptive early stopping in a shared one-dimensional feature extractor. The framework is evaluated on the UORED-VAFCLS and Paderborn University bearing datasets under 6-, 8-, and 10-shot settings with controlled corruption levels. Results show robust diagnostic performance with fewer acquired vibration windows than with fixed-length inference. In the representative PU-Hard 8-shot setting, the proposed method achieves 80.26% accuracy with an average of 1.2432 acquired windows and reduces the evaluation cost J from 0.3929 to 0.2596 compared with fixed four-window inference. These results indicate that adaptive measurement improves the accuracy–cost trade-off in few-shot vibration diagnosis. Full article
(This article belongs to the Special Issue Advances in Sensors for Online Condition Monitoring and Fault Diagnosis: 2nd Edition)
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13 pages, 7807 KB  
Article
Fabrication and Characterization of an Ag–AgPd Thick-Film Thermopile Heat-Flux Sensor for High-Temperature Applications
by Zhichun Liu, Fei Chen, Zhixuan Su, Heng Wang, Jinghan Si, Junyang Chen, Zihan Du and Zhenyin Hai
Sensors 2026, 26(13), 4030; https://doi.org/10.3390/s26134030 - 25 Jun 2026
Viewed by 405
Abstract
High-temperature metallic surfaces in aero-engine hot sections and related thermal systems are subjected to MW/m2-level heat-flux loads and transient thermal conditions, creating a need for sensors capable of quantifying heat flux under high-temperature conditions. This study aims to develop a screen-printed [...] Read more.
High-temperature metallic surfaces in aero-engine hot sections and related thermal systems are subjected to MW/m2-level heat-flux loads and transient thermal conditions, creating a need for sensors capable of quantifying heat flux under high-temperature conditions. This study aims to develop a screen-printed Ag–AgPd thick-film thermopile heat-flux sensor (HFS) for MW/m2-level heat-flux measurement on high-temperature metallic surfaces. Its main feature is the integration of an Ag–AgPd thermopile sensing layer, an insulating layer, and a thermal-resistance layer on a SUS430 stainless-steel substrate through a screen-printing-based multilayer fabrication route. Microstructural characterization, annealing condition comparison, laser comparison calibration, repeated loading, dynamic-response testing, and flame-heating testing were conducted to evaluate the sensor structure and performance. Under laser comparison calibration, the sensor achieved a MW/m2-level calibrated heat-flux response over 0.32–1.37 MW/m2, with a near-linear output relationship of R2>0.998, a sensitivity of 2.67 μV/(kW/m2), a nonlinearity of 1.83%, a hysteresis error below 0.29%, a repeatability error below 0.43%, a sample-to-sample consistency error of 1.06%, a maximum accuracy-test deviation of 1.84%, and a maximum repeated-loading stability error of 1.33%. The sensor also exhibited a time constant of 0.806 s under laser step excitation, and the baseline-corrected equivalent heat-flux response remained stable during approximately 120 s of flame heating at about 800 °C. These results indicate that the proposed HFS provides a feasible thick-film thermopile sensing approach for MW/m2-level heat-flux measurement on high-temperature metallic surfaces. Full article
(This article belongs to the Special Issue Advances in Sensors for Online Condition Monitoring and Fault Diagnosis: 2nd Edition)
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19 pages, 5188 KB  
Article
PHM Services Based on Cyber–Physical Machine Tool System
by Chuting Wang, Ruijuan Xue, Xuesong Mei and Zuguang Huang
Sensors 2026, 26(12), 3885; https://doi.org/10.3390/s26123885 - 18 Jun 2026
Viewed by 451
Abstract
Heterogeneous fault information and a lack of real-time synchronization in CNC machine tools hinder effective Prognostics and Health Management (PHM). This paper designs and implements a digital twin-driven PHM framework for machine tools that integrates a unified machine-tool fault information dictionary and a [...] Read more.
Heterogeneous fault information and a lack of real-time synchronization in CNC machine tools hinder effective Prognostics and Health Management (PHM). This paper designs and implements a digital twin-driven PHM framework for machine tools that integrates a unified machine-tool fault information dictionary and a mechanism-data dual-driven diagnostic model (ResNet-TCN). A cyber–physical platform was developed using OPC UA and RESTful APIs to ensure real-time data synchronization. Experiments on the PHM 2010 dataset demonstrate that the proposed ResNet-TCN model achieves a root mean square error (RMSE) of 5.46 μm for tool wear prediction. Its performance surpasses that of traditional LSTM models, and the proposed framework effectively eliminates information silos, providing a responsive, scalable and accurate PHM solution for smart manufacturing. Full article
(This article belongs to the Special Issue Advances in Sensors for Online Condition Monitoring and Fault Diagnosis: 2nd Edition)
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27 pages, 3261 KB  
Article
A Data-Driven Spatiotemporal Risk Assessment Framework for Transformer Overload in Distributed Renewable Energy System
by Chengjun Xie, Chenhao Sun and Yanzheng Liu
Sensors 2026, 26(11), 3505; https://doi.org/10.3390/s26113505 - 2 Jun 2026
Viewed by 393
Abstract
In distributed renewable energy systems, load fluctuations caused by energy resources and energy storage increase the overload risk of distribution transformers, which may accelerate insulation aging and cause overheating, and undermine operational reliability. For transformer condition monitoring, this risk is reflected not by [...] Read more.
In distributed renewable energy systems, load fluctuations caused by energy resources and energy storage increase the overload risk of distribution transformers, which may accelerate insulation aging and cause overheating, and undermine operational reliability. For transformer condition monitoring, this risk is reflected not by a single variable but by heterogeneous sensing observations acquired from electrical, thermal, and equipment status monitoring channels. Because full-scale inspection of latent defects is impractical under limited staffing and equipment resources, accurate overload risk prediction is important for sensor-driven maintenance allocation. With such motivations, this paper proposes a Transformer Overload Risk Assessment (TORA) approach for robust overload risk prediction under nonstationary load conditions. First, a feature matrix is constructed by jointly incorporating static features that capture long-term drift and dynamic features extracted from multisource sensing and supervisory signals that reflect short-term fluctuations. Then, static and dynamic features are assessed with Edge-based Static Feature Risk Assessment (E-SFRA) model and Cloud-based Dynamic Feature Risk Assessment (C-DFRA) model, respectively, according to their temporal and statistical characteristics. Next, a periodic calibration model (CE-PAA) is established through a cloud–edge loop, which uses low-latency edge updates and high-capacity cloud computation as feedback. Finally, risk score fusion (RSF) fuses generated static and dynamic risk scores to integrate cloud and edge strengths. The case study results indicate that TORA can transform heterogeneous monitoring signals into calibrated risk information in the studied single power plant scenario, providing useful support for multisource sensor data fusion, transformer condition monitoring, and maintenance decision making. Further validation using multi source field datasets is still needed to assess its cross scenario generalization ability. Full article
(This article belongs to the Special Issue Advances in Sensors for Online Condition Monitoring and Fault Diagnosis: 2nd Edition)
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