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Advances in Sensors for Online Condition Monitoring and Fault Diagnosis
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Advances in Sensors for Online Condition Monitoring and Fault Diagnosis

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

Deadline for manuscript submissions: 20 May 2025 | Viewed by 470

Special Issue 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 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, 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 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

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

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Published Papers (1 paper)

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Research

20 pages, 535 KiB  
Article
Unsupervised Process Anomaly Detection and Identification Using the Leave-One-Variable-Out Approach
by Jacob A. Farber and Ahmad Y. Al Rashdan
Sensors 2025, 25(7), 2098; https://doi.org/10.3390/s25072098 - 27 Mar 2025
Viewed by 240
Abstract
Automated anomaly detection and identification can signal equipment issues and pinpoint causes in large-scale industrial systems. For systems with limited failure history, unsupervised machine learning methods can be utilized as they do not require past failures. This study introduces the leave-one-variable-out (LOVO) model, [...] Read more.
Automated anomaly detection and identification can signal equipment issues and pinpoint causes in large-scale industrial systems. For systems with limited failure history, unsupervised machine learning methods can be utilized as they do not require past failures. This study introduces the leave-one-variable-out (LOVO) model, which masks one variable at a time to predict the others, learning underlying process correlations. Detection performance was assessed with synthetic and experimental data, while identification performance used only synthetic data due to its ability to generate labeled anomaly types. For detection using synthetic data, the LOVO model generally outperformed comparative models; while using experimental data, the comparative methods outperformed the LOVO model. However, the comparative methods required selecting a latent size, and these conclusions pertain to using the optimal size. In practice, it would not be feasible to always select the optimal value, and incorrect selections impacted performance. In contrast, the LOVO model does not require a latent space. For identification using synthetic data, the LOVO model was slightly outperformed in interpretability and repeatability but still demonstrated impressive results. These outcomes suggest that the LOVO model is an effective model and may be more easily implemented without the challenging tuning process of selecting a latent size. Full article
(This article belongs to the Special Issue Advances in Sensors for Online Condition Monitoring and Fault Diagnosis)
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