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Safety and Health of Machine and Environment: From Sensing Data...
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Safety and Health of Machine and Environment: From Sensing Data to Information

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

Deadline for manuscript submissions: closed (29 May 2023) | Viewed by 18660

Special Issue Editors

Dr. Tiantian Wang

E-Mail Website
Guest Editor
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Interests: intelligent sensor network; big data management and analysis; rail transit equipment health monitoring; SHM (Structure health monitoring) of railway vehicle; rotating machinery fault diagnosis; multi-source data fusion; extreme environment driving safety
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Longjun Dong

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: mining safety and sustainability; applied acoustics; rock mechanics; engineering seismicity; nondestructive evaluation; structure reliability; applied geophysics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Information extraction and mining from sensing data are very important for the safety state perception and health assessment of machines and their environments, including rail transit equipment, aerospace equipment, large venue buildings, tunnels, bridges, and their environments.

It is important to guarantee the reliable service and public safety of large equipment/engineering projects. Through the accurate perception and reliable analysis of the structural state, we can grasp the current state of key equipment and predict the future state in real-time. This can not only provide key information to support equipment maintenance but can also evaluate the risks associated with large equipment/engineering projects in a timely manner. On the premise of ensuring safety, the maintenance cycle and maintenance cost can be significantly reduced. Therefore, environmental sensing has important engineering value and academic significance.

Reliable perception and health assessments rely on advanced sensing technologies, acquisition technology, signal processing technology, big data analysis, and health assessment technologies. Sensing and acquisition are the basis of data acquisition. Developing efficient sensing and acquisition for different objects is of great significance in sensing cost control and accurate data acquisition. Signal processing and big data analysis technology provide important technical support for data management, information extraction, and implicit information mining. Health assessment technology is the key to realizing state analysis and providing decision-making support.

We have thus organized this Special Issue to which scholars can contribute papers on key technologies for the health and safety of machines as well as the environment, including but not limited to:

  • Advanced sensor design;
  • Source localization;
  • Data mining;
  • Safety classifications;
  • Engineering monitoring, including slope, mines, buildings, etc.;
  • Tomography/imaging;
  • Acquisition system design and development;
  • Noise reduction, time-frequency analysis and other signal processing methods;
  • Intelligent feature extraction method;
  • Information mining technology;
  • Design of big data platforms for environmental sensing;
  • Data management methods for environmental sensing;
  • Data fusion technology in health/safety assessment;
  • Machine learning-based health state recognition;
  • Deep learning-based health state assessment;
  • Machine learning based health/safety state prediction.

Dr. Tiantian Wang
Prof. Dr. Longjun Dong
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

  • sensor
  • source localization
  • safety classifications
  • engineering monitoring
  • information mining

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

Published Papers (6 papers)

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16 pages, 4264 KiB  
Article
A Mechanism-Based Automatic Fault Diagnosis Method for Gearboxes
by Lei Xu, Tiantian Wang, Jingsong Xie, Jinsong Yang and Guangjun Gao
Sensors 2022, 22(23), 9150; https://doi.org/10.3390/s22239150 - 25 Nov 2022
Cited by 4 | Viewed by 2701
Abstract
Convenient and fast fault diagnosis is the key to improving the service safety and maintenance efficiency of gearboxes. However, the environment and working conditions under complex service conditions are variable, and there is a lack of fault samples in engineering applications. These factors [...] Read more.
Convenient and fast fault diagnosis is the key to improving the service safety and maintenance efficiency of gearboxes. However, the environment and working conditions under complex service conditions are variable, and there is a lack of fault samples in engineering applications. These factors lead to difficulties in intelligent diagnosis methods based on machine learning, while traditional mechanism-based fault diagnosis requires high expertise and long time periods for the manual analysis of data. For the requirements of diagnostic convenience, an automatic fault diagnosis method for gearboxes is proposed in this paper. The method achieves accurate acquisition of rotational speed by constructing a rotational frequency search algorithm. The self-referencing characteristic frequency identification method is proposed to avoid manual signal analysis. On this basis, a framework of anti-interference automatic diagnosis is constructed to realize automatic diagnosis of gear faults. Finally, a gear fault experiment is carried out based on a high-fidelity experimental bench of bogie to verify the effectiveness of the proposed method. The proposed automatic diagnosis method does not rely on a large number of fault samples and avoids the need for diagnosis through professional knowledge, thus saving time for data analysis and promoting the application of fault diagnosis methods. Full article
(This article belongs to the Special Issue Safety and Health of Machine and Environment: From Sensing Data to Information)
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20 pages, 8971 KiB  
Article
An Outlier Cleaning Based Adaptive Recognition Method for Degradation Stage of Bearings
by Jingsong Xie, Yujie Xie, Tiantian Wang and Yougang Xiao
Sensors 2022, 22(17), 6480; https://doi.org/10.3390/s22176480 - 28 Aug 2022
Cited by 2 | Viewed by 1946
Abstract
Accurate identification of the degradation stage is key to the prediction of the remaining useful life (RUL) of bearings. The 3σ method is commonly used to identify the degradation point. However, the recognition accuracy is seriously disturbed by the random outliers in the [...] Read more.
Accurate identification of the degradation stage is key to the prediction of the remaining useful life (RUL) of bearings. The 3σ method is commonly used to identify the degradation point. However, the recognition accuracy is seriously disturbed by the random outliers in the normal stage. Therefore, this paper proposes an adaptive recognition method for the degradation stage based on outlier cleaning. Firstly, an improved multi-scale kernel regression outlier detection method is adopted to roughly search the abnormal signal segments. Then, a method for the accurate locating of the start and end points of abnormal impulses is established. After that, indexes are constructed for screening abnormal segments and an iterative strategy is proposed to achieve an accurate and efficient removal of abnormal impulses. After outlier cleaning, the 3σ approach is used to set the degradation warning threshold adaptively to realize the degradation stage recognition of the bearings. The PHM 2012 rotating machinery dataset is used to verify the effectiveness of the proposed method. Experimental results show that the proposed method can accurately locate and remove the outliers adaptively. After the cleaning of the outliers, the identification of the degradation stage is no longer disturbed by the selection of the reference signal of the normal stage and the robustness and the accuracy of the degradation stage identification have been improved significantly. Full article
(This article belongs to the Special Issue Safety and Health of Machine and Environment: From Sensing Data to Information)
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11 pages, 3760 KiB  
Article
A Novel Method to Measure the Static Coefficient of Friction for Socks
by Jinsu Eun, Jaejin Ryue, Sangsoo Park and Kikwang Lee
Sensors 2022, 22(15), 5525; https://doi.org/10.3390/s22155525 - 25 Jul 2022
Cited by 1 | Viewed by 3123
Abstract
Mechanical testers have commonly been used to measure the frictional properties of socks. However, the friction values may be susceptible to the level of stretchiness of tested fabrics or human variability. Thus, the aim of this study was to propose a novel method [...] Read more.
Mechanical testers have commonly been used to measure the frictional properties of socks. However, the friction values may be susceptible to the level of stretchiness of tested fabrics or human variability. Thus, the aim of this study was to propose a novel method that enables friction measurement of socks in a sock-wearing condition with less human variability effects. Five socks with different frictional properties were chosen. Three experimental ramp tests were performed with an artificial structure shaped like the foot-ankle complex (last) and a ramp tester to quantify the static coefficient of friction (COF) at the foot against sock, at the sock against an insole, and the foot wearing socks against the insole, respectively. The angle where the last slipped while the ramp surface was gradually inclined was used to compute the static COF values for each sock. The reliability was 0.99, and COF values ranged from 0.271 to 0.861 at the foot-sock interface, 0.342 to 0.639 at the sock-insole interface, and 0.310 to 0.614 in the third test. Socks with different frictional properties were successfully distinguished each other. Thus, the suggested protocol could be a reliable option for measuring the static COF values in the tension similar with it found in a sock-waring condition with reduced effects of human variability. Full article
(This article belongs to the Special Issue Safety and Health of Machine and Environment: From Sensing Data to Information)
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18 pages, 6393 KiB  
Article
Investigation on Rupture Initiation and Propagation of Traffic Tunnel under Seismic Excitation Based on Acoustic Emission Technology
by Xiling Liu, Yuan Zeng, Ling Fan, Shuquan Peng and Qinglin Liu
Sensors 2022, 22(12), 4553; https://doi.org/10.3390/s22124553 - 16 Jun 2022
Cited by 3 | Viewed by 2172
Abstract
Traffic tunnels are important engineering structures in transportation, and their stability is critical to traffic safety. In particular, when these tunnels are in an earthquake-prone area, the rupture process under seismic excitation needs to be studied in depth for safer tunnel design. In [...] Read more.
Traffic tunnels are important engineering structures in transportation, and their stability is critical to traffic safety. In particular, when these tunnels are in an earthquake-prone area, the rupture process under seismic excitation needs to be studied in depth for safer tunnel design. In this paper, based on a construction project on the Nairobi-Malaba railway in East Africa, a laboratory shaking table test with 24 working cases of seismic excitation on a mountain tunnel is designed, and the acoustic emission (AE) technique is employed to investigate the tunnel rupture process. The results show that the high frequency components between 20 and 30 kHz of AE signals are the tunnel rupturing signals under the seismic excitation under such conditions. The tunnel vault and the arch foot are prone to rupture during the seismic excitation, and the initial rupture in the arch foot and vault of the tunnel occur under the horizontal and vertical Kobe wave seismic excitation, respectively, with a maximum acceleration of 0.4 g. After the rupture initiation, the tunnel arch foot continues to rupture in the subsequent working cases regardless of whether the excitation direction is horizontal or vertical, while the tunnel vault does not rupture continuously with the implementation of the subsequent excitations. Moreover, the Kobe seismic wave has a higher degree of damage potential to underground structures than the El seismic wave. Full article
(This article belongs to the Special Issue Safety and Health of Machine and Environment: From Sensing Data to Information)
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16 pages, 4252 KiB  
Article
Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification
by Jinsong Yang, Yizhen Peng, Jingsong Xie and Pengxi Wang
Sensors 2022, 22(12), 4549; https://doi.org/10.3390/s22124549 - 16 Jun 2022
Cited by 60 | Viewed by 5517
Abstract
To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is difficult to monitor in [...] Read more.
To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is difficult to monitor in real-time. Meanwhile, external uncertainties significantly impact bearing degradation. Therefore, this paper proposes a new bearing RUL prediction method based on long-short term memory (LSTM) with uncertainty quantification. First, a fusion metric related to runtime (or degradation) is proposed to reflect the latent degradation process. Then, an improved dropout method based on nonparametric kernel density is developed to improve estimation accuracy of RUL. The PHM2012 dataset is adopted to verify the proposed method, and comparison results illustrate that the proposed prediction model can accurately obtain the point estimation and probability distribution of the bearing RUL. Full article
(This article belongs to the Special Issue Safety and Health of Machine and Environment: From Sensing Data to Information)
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18 pages, 4885 KiB  
Essay
Wave Propagation Characteristics and Compaction Status of Subgrade during Vibratory Compaction
by Junkai Yao, Mao Yue, Hongsheng Ma and Changwei Yang
Sensors 2023, 23(4), 2183; https://doi.org/10.3390/s23042183 - 15 Feb 2023
Cited by 8 | Viewed by 1956
Abstract
Vibratory compaction status has a significant influence on the construction quality of subgrade engineering. This study carried out field experiments to study the propagation characteristics of the vertical vibration wave in the soil field along the traveling direction of the vibratory roller. The [...] Read more.
Vibratory compaction status has a significant influence on the construction quality of subgrade engineering. This study carried out field experiments to study the propagation characteristics of the vertical vibration wave in the soil field along the traveling direction of the vibratory roller. The propagation coefficients of the peak acceleration at different positions and compacting rounds are compared in both the time and frequency domains. The compaction status is estimated in the form of dynamic modulus of deformation (Evd) obtained by plate load tests. The experiment results show that the propagation coefficient of peak acceleration is affected by the traveling speed, excitation amplitude, and frequency of the vibratory roller, as well as the compacting rounds. An exponential relationship between the wave amplitudes of the fundamental mode and higher-order modes is revealed. The amplitude of the fundamental wave is maximum at the speed of 3 km/h, whereas the amplitudes of higher-order waves have a maximum of 1.5 km/h. The influences of compaction rounds on the average value of Evd are also investigated to provide a practical reference for engineering construction. Full article
(This article belongs to the Special Issue Safety and Health of Machine and Environment: From Sensing Data to Information)
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