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Digital Twin-Enabled Deep Learning for Machinery Health Monitoring
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Digital Twin-Enabled Deep Learning for Machinery Health Monitoring

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

Deadline for manuscript submissions: 10 March 2025 | Viewed by 1215

Special Issue Editors

Dr. Ke Zhao

E-Mail Website
Guest Editor
School of Mechanical Engineering, Changan University, Xi'an, China
Interests: fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Xingkai Yang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: condition monitoring and fault diagnosis; gearbox dynamics and diagnostics; gear tribology; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Zongzhen Zhang

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Interests: fault diagnosis; mechanical engineering; signal processing; eature extraction; condition monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinrui Wang

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Interests: fault diagnosis; mechanical engineering; deep learning; feature extraction; condition monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital twin technology, which creates high-fidelity virtual replicas of physical assets, is transforming numerous industries by enabling real-time monitoring, diagnostics, and optimization. When coupled with deep learning, a subset of artificial intelligence adept at handling large datasets and uncovering intricate patterns, digital twins can significantly enhance machinery health monitoring. This integration allows for more accurate fault detection, predictive maintenance, and overall system reliability, ultimately reducing downtime and maintenance costs.

This Special Issue will present the latest research findings, technological advancements, and practical applications related to integrating digital twins with deep learning for machinery health monitoring. This Special Issue encourages submissions that cover, among others, the following topics:

  • Advancements in digital twin for machine fault diagnosis;
  • Integration of digital twins and deep learning algorithms for health monitoring;
  • Advanced sensing and monitoring techniques under variable working conditions;
  • Transfer-learning-based mechanical fault diagnosis and prognosis.

Dr. Ke Zhao
Dr. Xingkai Yang
Dr. Zongzhen Zhang
Prof. Dr. Jinrui Wang
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

  • digital twin for machine fault diagnosis
  • digital twin for health monitoring
  • advanced sensing and monitoring techniques

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Published Papers (2 papers)

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Research

16 pages, 6001 KiB  
Article
Experimental Investigation on the Effect of Coil Shape on Planar Eddy Current Sensor Characteristic for Blade Tip Clearance
by Lingqiang Zhao, Yaguo Lyu, Fulin Liu, Zhenxia Liu and Ziyu Zhao
Sensors 2024, 24(18), 6133; https://doi.org/10.3390/s24186133 - 23 Sep 2024
Viewed by 326
Abstract
Given the increasing application of eddy current sensors for measuring turbine tip clearance in aero engines, enhancing the performance of these sensors is essential for improving measurement accuracy. This study investigates the influence of coil shape on the measurement performance of planar eddy [...] Read more.
Given the increasing application of eddy current sensors for measuring turbine tip clearance in aero engines, enhancing the performance of these sensors is essential for improving measurement accuracy. This study investigates the influence of coil shape on the measurement performance of planar eddy current sensors and identifies an optimal coil shape to enhance sensing capabilities. To achieve this, various coil shapes—specifically circular, square, rectangular wave, and triangular wave—were designed and fabricated, featuring different numbers of turns for the experiment at room temperature. By employing a method for calculating coil inductance, the performance of each sensor was evaluated based on key metrics: measurement range, sensitivity, and linearity. Experimental results reveal that the square coil configuration outperforms other shapes in overall measurement performance. Notably, the square coil demonstrated a measurement range of 0 mm to 8 mm, a sensitivity of 0.115685 μH/mm, and an impressive linearity of 98.41% within the range of 0 mm to 2 mm. These findings indicate that the square coil configuration enhances measurement capabilities. The conclusions drawn from this study provide valuable insights for selecting coil shapes and optimizing the performance of planar eddy current sensors, thereby contributing to the advancement of turbine tip clearance measurement techniques in aero engines. Full article
(This article belongs to the Special Issue Digital Twin-Enabled Deep Learning for Machinery Health Monitoring)
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13 pages, 3423 KiB  
Article
A Novel Cross-Domain Mechanical Fault Diagnosis Method Fusing Acoustic and Vibration Signals by Vision Transformer
by Zhenyun Chu, Shuo Xing, Baokun Han and Jinrui Wang
Sensors 2024, 24(16), 5120; https://doi.org/10.3390/s24165120 - 7 Aug 2024
Viewed by 622
Abstract
Changes in operating conditions often cause the distribution of signal features to shift during the bearing fault diagnosis process, which will result in reduced diagnostic accuracy of the model. Therefore, this paper proposes a dual-channel parallel adversarial network (DPAN) based on vision transformer, [...] Read more.
Changes in operating conditions often cause the distribution of signal features to shift during the bearing fault diagnosis process, which will result in reduced diagnostic accuracy of the model. Therefore, this paper proposes a dual-channel parallel adversarial network (DPAN) based on vision transformer, which extracts features from acoustic and vibration signals through parallel networks and enhances feature robustness through adversarial training during the feature fusion process. In addition, the Wasserstein distance is used to reduce domain differences in the fused features, thereby enhancing the network’s generalization ability. Two sets of bearing fault diagnosis experiments were conducted to validate the effectiveness of the proposed method. The experimental results show that the proposed method achieves higher diagnostic accuracy compared to other methods. The diagnostic accuracy of the proposed method can exceed 98%. Full article
(This article belongs to the Special Issue Digital Twin-Enabled Deep Learning for Machinery Health Monitoring)
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