Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Emerging Sensing Technologies for Machine Health State Awareness
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Emerging Sensing Technologies for Machine Health State Awareness

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

Deadline for manuscript submissions: closed (30 December 2024) | Viewed by 7586

Special Issue Editors

Dr. Yuejian Chen

E-Mail Website
Guest Editor
College of Transportation, Tongji University, Shanghai 201804, China
Interests: signal analysis and processing; artificial intelligence; prediction and health management
Special Issues, Collections and Topics in MDPI journals
Dr. Zhipeng Wang

E-Mail Website
Guest Editor
State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
Interests: UAV-based automatic railway inspection; fault diagnosis; prognostics and health management
Special Issues, Collections and Topics in MDPI journals
Dr. Yuanjin Ji

E-Mail Website
Guest Editor
Institute of Rail Transit, Tongji University, Shanghai 201804, China
Interests: intelligent sensing; vehicle dynamics; machine learning

Special Issue Information

Dear Colleagues,

Modern society depends on the reliable and uninterrupted operation of complex machines such as aircraft, power plants, wind farms, and vehicles. Timely awareness of the health state of these machines is essential to prevent sudden failure and enable condition-based maintenance. Health state awareness relies on acquiring machine condition data, such as vibration, acoustic, temperature, stress, and rotating speed. Sensing technologies are fundamental for acquiring these data. While traditional sensing technologies are abundant, new sensing technologies are emerging with wider/higher sensitivity, more reliable data sensing, higher engineering applicability, more user-friendliness, lower cost, and higher robustness.

In this Special Issue, we aim to collect the latest research progress on new sensing technologies for machine health state awareness. Topics of interest include, but are not limited to, the following:

  • New sensor designs such as piezoelectric sensors, infrared cameras, and fiber Bragg gratings;
  • Sensor testing and calibration;
  • Application of new sensing technologies for machine health monitoring;
  • Unmanned aerial vehicle-based video monitoring;
  • Sensory data processing;
  • Multi-modality sensory data fusion.

Dr. Yuejian Chen
Dr. Zhipeng Wang
Dr. Yuanjin Ji
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

  • intelligent sensing
  • fault diagnostics
  • machine health monitoring

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 7193 KiB  
Article
Intelligent Fault Diagnosis of Planetary Gearbox Across Conditions Based on Subdomain Distribution Adversarial Adaptation
by Songjun Han, Zhipeng Feng, Ying Zhang, Minggang Du and Yang Yang
Sensors 2024, 24(21), 7017; https://doi.org/10.3390/s24217017 - 31 Oct 2024
Viewed by 837
Abstract
Sensory data are the basis for the intelligent health state awareness of planetary gearboxes, which are the critical components of electromechanical systems. Despite the advantages of intelligent diagnostic techniques for detecting intricate fault patterns and improving diagnostic speed, challenges still persist, which include [...] Read more.
Sensory data are the basis for the intelligent health state awareness of planetary gearboxes, which are the critical components of electromechanical systems. Despite the advantages of intelligent diagnostic techniques for detecting intricate fault patterns and improving diagnostic speed, challenges still persist, which include the limited availability of fault data, the lack of labeling information and the discrepancies in features across different signals. Targeting this issue, a subdomain distribution adversarial adaptation diagnosis method (SDAA) is proposed for faults diagnosis of planetary gearboxes across different conditions. Firstly, nonstationary vibration signals are converted into a two-dimensional time–frequency representation to extract intrinsic information and avoid frequency overlapping. Secondly, an adversarial training mechanism is designed to evaluate subclass feature distribution differences between the source and target domain. A conditional distribution adaptation is employed to account for correlations among data from different subclasses. Finally, the proposed method is validated through experiments on planetary gearboxes, and the results demonstrate that SDAA can effectively diagnose faults under crossing conditions with an accuracy of 96.7% in diagnosing gear faults and 95.2% in diagnosing planet bearing faults. It outperforms other methods in both accuracy and model robustness. This confirms that this approach can refine domain-invariant information for transfer learning with less information loss from the sub-class level of fault data instead of the overall class level. Full article
(This article belongs to the Special Issue Emerging Sensing Technologies for Machine Health State Awareness)
►▼ Show Figures

Figure 1

25 pages, 12596 KiB  
Article
Multi-Sensory Tool Holder for Process Force Monitoring and Chatter Detection in Milling
by Alexander Schuster, Andreas Otto, Hendrik Rentzsch and Steffen Ihlenfeldt
Sensors 2024, 24(17), 5542; https://doi.org/10.3390/s24175542 - 27 Aug 2024
Viewed by 1306
Abstract
Sensor-based monitoring of process and tool condition in milling is a key technology for improving productivity and workpiece quality, as well as enabling automation of machine tools. However, industrial implementation of such monitoring systems remains a difficult task, since they require high sensitivity [...] Read more.
Sensor-based monitoring of process and tool condition in milling is a key technology for improving productivity and workpiece quality, as well as enabling automation of machine tools. However, industrial implementation of such monitoring systems remains a difficult task, since they require high sensitivity and minimal impact on CNC machines and cutting conditions. This paper presents a novel multi-sensory tool holder for measurement of process forces and vibrations in direct proximity to the cutting tool. In particular, the sensor system has an integrated temperature sensor, a triaxial accelerometer and strain gauges for measurement of axial force and bending moment. It is equipped with a self-sufficient electric generator and wireless data transmission, allowing for a tool holder design without interfering contours. Milling and drilling experiments with varying cutting parameters are conducted. The measurement data are analyzed, pre-processed and verified with reference signals. Furthermore, the suitability of all integrated sensors for detection of dynamic instabilities (chatter) is investigated, showing that bending moment and tangential acceleration signals are the most sensitive regarding this monitoring task. Full article
(This article belongs to the Special Issue Emerging Sensing Technologies for Machine Health State Awareness)
►▼ Show Figures

Figure 1

14 pages, 6686 KiB  
Article
Development of Simplified Methods for Levitation Force Distribution in Maglev Vehicles Using Frequency Ratio Tests
by Wen Ji, Weihua Ma, Shihui Luo, Guofeng Zeng, Feng Ye and Mingbo Liu
Sensors 2024, 24(17), 5527; https://doi.org/10.3390/s24175527 - 26 Aug 2024
Viewed by 819
Abstract
Maglev vehicles apply the entire vehicle load uniformly onto bridges through levitation forces. In assessing the dynamic characteristics of the maglev train–bridge coupling system, it is reasonable to simplify the distributed levitation force as a concentrated force. This article theoretically derives the analytical [...] Read more.
Maglev vehicles apply the entire vehicle load uniformly onto bridges through levitation forces. In assessing the dynamic characteristics of the maglev train–bridge coupling system, it is reasonable to simplify the distributed levitation force as a concentrated force. This article theoretically derives the analytical response of bridge dynamics under the action of a single constant force and conducts numerical simulations for a moving single constant force and a series of equally spaced constant forces passing over simply supported beams and two-span continuous beams, respectively. The topic of discussion is the response of bridge dynamics when different degrees of force concentration are involved. High-precision displacement and acceleration sensors were utilized to conduct tests on the Shanghai maglev line to verify the accuracy of the simulation results. The results indicate that when simplifying the distributed levitation force into a concentrated force model, a frequency ratio can be used to analyze the conditions for resonance between the train and the bridge and to calculate the critical speed of the train; the levitation distribution force of a high-speed maglev vehicle can be simplified into four groups of concentrated forces based on the number of levitation frames to achieve sufficient accuracy, with the dynamic response of the bridge being close to that under distributed loads. Full article
(This article belongs to the Special Issue Emerging Sensing Technologies for Machine Health State Awareness)
►▼ Show Figures

Figure 1

16 pages, 2995 KiB  
Article
Train Trajectory-Following Control Method Using Virtual Sensors
by Youpei Huang, Xiaoguang Ma and Lihui Ren
Sensors 2024, 24(16), 5385; https://doi.org/10.3390/s24165385 - 20 Aug 2024
Viewed by 979
Abstract
Trajectory-following control is the basis for the practical application of an articulated virtual rail train transportation system. In this paper, a planar nonlinear dynamics model of an articulated vehicle is derived using the Euler–Lagrange method. A trajectory-following control strategy based on the first [...] Read more.
Trajectory-following control is the basis for the practical application of an articulated virtual rail train transportation system. In this paper, a planar nonlinear dynamics model of an articulated vehicle is derived using the Euler–Lagrange method. A trajectory-following control strategy based on the first following point is proposed, and a feedback linearization control algorithm is designed based on the vehicle dynamics model to achieve the trajectory following of the rear vehicle. Based on the target trajectory formed by the first following point and measured by virtual sensors, a vector analysis method grounded in geometric relationships is proposed to solve in real time for the desired position, velocity, and acceleration of the vehicle. Finally, a MATLAB/SIMPACK dynamics virtual prototype is established to test the vehicle’s trajectory-following effectiveness and dynamics performance under lane change and circular curve routes. The results indicate that the control algorithm can achieve trajectory following while maintaining good vehicle dynamics performance. It is robust to variations in vehicle mass, vehicle speed, tire cornering stiffness, and road friction coefficient. Full article
(This article belongs to the Special Issue Emerging Sensing Technologies for Machine Health State Awareness)
►▼ Show Figures

Figure 1

24 pages, 13287 KiB  
Article
A Health Monitoring Model for Circulation Water Pumps in a Nuclear Power Plant Based on Graph Neural Network Observer
by Jianyong Gao, Liyi Ma, Chen Qing, Tingdi Zhao, Zhipeng Wang, Jie Geng and Ying Li
Sensors 2024, 24(14), 4486; https://doi.org/10.3390/s24144486 - 11 Jul 2024
Viewed by 1154
Abstract
The health monitoring of CRF (circulation water) pumps is essential for prognostics and management in nuclear power plants. However, the operational status of CRF pumps can vary due to environmental factors and human intervention, and the interrelationships between monitoring parameters are often complex. [...] Read more.
The health monitoring of CRF (circulation water) pumps is essential for prognostics and management in nuclear power plants. However, the operational status of CRF pumps can vary due to environmental factors and human intervention, and the interrelationships between monitoring parameters are often complex. Consequently, the existing methods face challenges in effectively assessing the health status of CRF pumps. In this study, we propose a health monitoring model for CRF pumps utilizing a meta graph transformer (MGT) observer. Initially, the meta graph transformer, a temporal–spatial graph learning model, is employed to predict trends across the various monitoring parameters of the CRF pump. Subsequently, a fault observer is constructed to generate early warnings of potential faults. The proposed model was validated using real data from CRF pumps in a nuclear power plant. The results demonstrate that the average Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) of normal predictions were reduced to 1.2385, 0.5614, and 2.6554, respectively. These findings indicate that our model achieves higher prediction accuracy compared to the existing methods and can provide fault warnings at least one week in advance. Full article
(This article belongs to the Special Issue Emerging Sensing Technologies for Machine Health State Awareness)
►▼ Show Figures

Figure 1

13 pages, 9690 KiB  
Article
A Low-Frequency Fiber Bragg Grating Acceleration Sensor Based on Spring Support and Symmetric Compensation Structure with Flexible Hinges
by Lijun Meng, Panpan Zhu, Xin Tan and Xiao Huang
Sensors 2024, 24(10), 2990; https://doi.org/10.3390/s24102990 - 8 May 2024
Cited by 1 | Viewed by 1622
Abstract
To measure vibration signals, a low-frequency fiber Bragg grating (FBG) acceleration sensor featuring a flexible hinge with a spring support and symmetric compensation structure has been designed. Based on the mechanical model of the sensor’s structure, the expressions for sensitivity and resonant frequency [...] Read more.
To measure vibration signals, a low-frequency fiber Bragg grating (FBG) acceleration sensor featuring a flexible hinge with a spring support and symmetric compensation structure has been designed. Based on the mechanical model of the sensor’s structure, the expressions for sensitivity and resonant frequency of the sensor are derived. The structural parameters of the sensor are optimized, and a simulation analysis is conducted using ANSYS 19.2 software. According to the results of simulation analysis and size optimization, the sensor prototype is constructed. Subsequently, its amplitude-frequency response, sensitivity, and temperature characteristics are investigated through vibration experiments. The experimental results show that the resonant frequency of the sensor is 73 Hz, the operating frequency range is 0~60 Hz, and the sensitivity measures 24.24 pm/g. This design meets the requirements for measuring vibration signals at low frequencies. Full article
(This article belongs to the Special Issue Emerging Sensing Technologies for Machine Health State Awareness)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop