Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Intelligent Sensors for Structural Health Monitoring and Mechanical 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 December 2024 | Viewed by 7491

Special Issue Editors

Dr. Zijian Qiao

E-Mail Website
Guest Editor
School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, China
Interests: mechanical fault diagnosis; weak signal detection; nonlinear dynamics; AI-enabled fault diagnosis and intelligent maintenance
Special Issues, Collections and Topics in MDPI journals
Dr. Ke Feng

E-Mail Website
Guest Editor
School of Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: machine condition monitoring; vibration analysis; fault diagnosis and prognostics; digital twin; dynamic; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Zhihui Lai

E-Mail Website
Guest Editor
College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Interests: energy harvesting; nonlinear dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) aims to identify the damage caused to fixed objects including aerospace, civil and mechanical engineering infrastructure, whereas mechanical fault diagnosis (MFD) seeks to monitor the health states and diagnose the damage to rotating objects including wind turbines, aero-engines and high-speed trains. These methods have attracted sustained and growing interest. However, it is vital for investigators to use advanced and intelligent sensors to acquire accurate and multi-source data from fixed and rotating objects. Moreover, the data quality is highly dependent on the sensors. Up to now, many scholars have applied advanced sensors to SHM and MFD, including acoustic emissions, vibration, strain, temperature, images, audio, electric currents, chemical analysis, optical fibers, oil analysis sensors, etc.

This Special Issue therefore aims to compile original research and review articles on the recent advances, technologies, solutions, applications, and new challenges in the field of intelligent sensors for SHM and MFD. These topics include, but are not limited to:

  • Novel sensors and sensing technologies in SHM and MFD;
  • Intelligent SHM and MFD methods;
  • Improved and enhanced data quality methods in SHM and MFD;
  • Advanced signal processing techniques in SHM and MFD;
  • Sensor network design and optimization in SHM and MFD;
  • Remaining useful life prediction in SHM and MFD;
  • Weak signal detection and enhancement in SHM and MFD;
  • Built-in SHM and MFD intelligent maintaining and health management.

Dr. Zijian Qiao
Dr. Ke Feng
Dr. Zhihui Lai
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
  • advanced sensors
  • structural health monitoring
  • mechanical fault diagnosis
  • data quality improvement

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

20 pages, 16044 KiB  
Article
A Comprehensive Approach for Detecting Brake Pad Defects Using Histogram and Wavelet Features with Nested Dichotomy Family Classifiers
by Sakthivel Gnanasekaran, Lakshmi Pathi Jakkamputi, Jegadeeshwaran Rakkiyannan, Mohanraj Thangamuthu and Yogesh Bhalerao
Sensors 2023, 23(22), 9093; https://doi.org/10.3390/s23229093 - 10 Nov 2023
Cited by 1 | Viewed by 912
Abstract
The brake system requires careful attention for continuous monitoring as a vital module. This study specifically focuses on monitoring the hydraulic brake system using vibration signals through experimentation. Vibration signals from the brake pad assembly of commercial vehicles were captured under both good [...] Read more.
The brake system requires careful attention for continuous monitoring as a vital module. This study specifically focuses on monitoring the hydraulic brake system using vibration signals through experimentation. Vibration signals from the brake pad assembly of commercial vehicles were captured under both good and defective conditions. Relevant histograms and wavelet features were extracted from these signals. The selected features were then categorized using Nested dichotomy family classifiers. The accuracy of all the algorithms during categorization was evaluated. Among the algorithms tested, the class-balanced nested dichotomy algorithm with a wavelet filter achieved a maximum accuracy of 99.45%. This indicates a highly effective method for accurately categorizing the brake system based on vibration signals. By implementing such a monitoring system, the reliability of the hydraulic brake system can be ensured, which is crucial for the safe and efficient operation of commercial vehicles in the market. Full article
(This article belongs to the Special Issue Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis)
►▼ Show Figures

Figure 1

24 pages, 8960 KiB  
Article
Regional Thermal Radiation Characteristics of FY Satellite Remote Sensing Based on Big Data Analysis
by Tao Wen, Congxin Wei, Zhenyi Wang, Linzhu Wang, Zihan Yang and Tingting Gu
Sensors 2023, 23(20), 8446; https://doi.org/10.3390/s23208446 - 13 Oct 2023
Viewed by 902
Abstract
It is of great significance to study the thermal radiation anomalies of earthquake swarms in the same area in terms of selecting abnormal characteristic determination parameters, optimizing and determining the processing model, and understanding the abnormal machine. In this paper, we investigated short-term [...] Read more.
It is of great significance to study the thermal radiation anomalies of earthquake swarms in the same area in terms of selecting abnormal characteristic determination parameters, optimizing and determining the processing model, and understanding the abnormal machine. In this paper, we investigated short-term and long-term thermal radiation anomalies induced by earthquake swarms in Iran and Pakistan between 2007 and 2016. The anomalies were extracted from infrared remote sensing black body temperature data from the China Geostationary Meteorological Satellites (FY-2C/2E/2F/2G) using the multiscale time-frequency relative power spectrum (MS T-FRPS) method. By analyzing and summarizing the thermal radiation anomalies of series earthquake groups with consistency law through a stable and reliable MS T-FRPS method, we first obtained the relationship between anomalies and ShakeMaps from USGS and proposed the anomaly regional indicator (ARI) to determine seismic anomalies and the magnitude decision factor (MDF) to determine seismic magnitude. In addition, we explored the following discussions: earthquake impact on regional thermal radiation background and the relationship between thermal anomalies and earthquake magnitude and the like. Future research directions using the MS T-FRPS method to characterize regional thermal radiation anomalies induced by strong earthquakes could help improve the accuracy of earthquake magnitude determination. Full article
(This article belongs to the Special Issue Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis)
►▼ Show Figures

Figure 1

16 pages, 2770 KiB  
Article
A Novel Deep Convolutional Neural Network Combining Global Feature Extraction and Detailed Feature Extraction for Bearing Compound Fault Diagnosis
by Shuzhen Han, Pingjuan Niu, Shijie Luo, Yitong Li, Dong Zhen, Guojin Feng and Shengke Sun
Sensors 2023, 23(19), 8060; https://doi.org/10.3390/s23198060 - 24 Sep 2023
Viewed by 963
Abstract
This study researched the application of a convolutional neural network (CNN) to a bearing compound fault diagnosis. The proposed idea lies in the ability of CNN to automatically extract fault features from complex raw signals. In our approach, to extract more effective features [...] Read more.
This study researched the application of a convolutional neural network (CNN) to a bearing compound fault diagnosis. The proposed idea lies in the ability of CNN to automatically extract fault features from complex raw signals. In our approach, to extract more effective features from a raw signal, a novel deep convolutional neural network combining global feature extraction with detailed feature extraction (GDDCNN) is proposed. First, wide and small kernel sizes are separately adopted in shallow and deep convolutional layers to extract global and detailed features. Then, the modified activation layer with a concatenated rectified linear unit (CReLU) is added following the shallow convolution layer to improve the utilization of shallow global features of the network. Finally, to acquire more robust features, another strategy involving the GMP layer is utilized, which replaces the traditional fully connected layer. The performance of the obtained diagnosis was validated on two bearing datasets. The results show that the accuracy of the compound fault diagnosis is over 98%. Compared with three other CNN-based methods, the proposed model demonstrates better stability. Full article
(This article belongs to the Special Issue Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis)
►▼ Show Figures

Figure 1

17 pages, 827 KiB  
Article
Controlled Symmetry with Woods-Saxon Stochastic Resonance Enabled Weak Fault Detection
by Jian Liu, Jiaqi Guo, Bing Hu, Qiqing Zhai, Can Tang and Wanjia Zhang
Sensors 2023, 23(11), 5062; https://doi.org/10.3390/s23115062 - 25 May 2023
Cited by 1 | Viewed by 1053
Abstract
Weak fault detection with stochastic resonance (SR) is distinct from conventional approaches in that it is a nonlinear optimal signal processing to transfer noise into the signal, resulting in a higher output SNR. Owing to this special characteristic of SR, this study develops [...] Read more.
Weak fault detection with stochastic resonance (SR) is distinct from conventional approaches in that it is a nonlinear optimal signal processing to transfer noise into the signal, resulting in a higher output SNR. Owing to this special characteristic of SR, this study develops a controlled symmetry with Woods-Saxon stochastic resonance (CSwWSSR) model based on the Woods-Saxon stochastic resonance (WSSR), where each parameter of the model may be modified to vary the potential structure. Then, the potential structure of the model is investigated in this paper, along with the mathematical analysis and experimental comparison to clarify the effect of each parameter on it. The CSwWSSR is a tri-stable stochastic resonance, but differs from others in that each of its three potential wells is controlled by different parameters. Moreover, the particle swarm optimization (PSO), which can quickly find the ideal parameter matching, is introduced to attain the optimal parameters of the CSwWSSR model. Fault diagnosis of simulation signals and bearings was carried out to confirm the viability of the proposed CSwWSSR model, and the results revealed that the CSwWSSR model is superior to its constituent models. Full article
(This article belongs to the Special Issue Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis)
►▼ Show Figures

Figure 1

19 pages, 5246 KiB  
Article
High-Performance Adaptive Weak Fault Diagnosis Based on the Global Parameter Optimization Model of a Cascaded Stochastic Resonance System
by Zhihui Lai, Zhangjun Huang, Min Xu, Chen Wang, Junchen Xu, Cailiang Zhang, Ronghua Zhu and Zijian Qiao
Sensors 2023, 23(9), 4429; https://doi.org/10.3390/s23094429 - 30 Apr 2023
Cited by 3 | Viewed by 1392
Abstract
Stochastic resonance (SR), as a type of noise-assisted signal processing method, has been widely applied in weak signal detection and mechanical weak fault diagnosis. In order to further improve the weak signal detection performance of SR-based approaches and realize high-performance weak fault diagnosis, [...] Read more.
Stochastic resonance (SR), as a type of noise-assisted signal processing method, has been widely applied in weak signal detection and mechanical weak fault diagnosis. In order to further improve the weak signal detection performance of SR-based approaches and realize high-performance weak fault diagnosis, a global parameter optimization (GPO) model of a cascaded SR system is proposed in this work. The cascaded SR systems, which involve multiple multi-parameter-adjusting SR systems with both bistable and tri-stable potential functions, are first introduced. The fixed-parameter optimization (FPO) model and the GPO models of the cascaded systems to achieve optimal SR outputs are proposed based on the particle swarm optimization (PSO) algorithm. Simulated results show that the GPO model is capable of achieving a better SR output compared to the FPO model with rather good robustness and stability in detecting low signal-to-noise ratio (SNR) weak signals, and the tri-stable cascaded SR system has a better weak signal detection performance compared to the bistable cascaded SR system. Furthermore, the weak fault diagnosis approach based on the GPO model of the tri-stable cascaded system is proposed, and two rolling bearing weak fault diagnosis experiments are performed, thus verifying the effectiveness of the proposed approach in high-performance adaptive weak fault diagnosis. Full article
(This article belongs to the Special Issue Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis)
►▼ Show Figures

Figure 1

12 pages, 4925 KiB  
Communication
Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of Bearings
by Min Xu, Chao Zheng, Kelei Sun, Li Xu, Zijian Qiao and Zhihui Lai
Sensors 2023, 23(8), 3860; https://doi.org/10.3390/s23083860 - 10 Apr 2023
Cited by 3 | Viewed by 1386
Abstract
Although stochastic resonance (SR) has been widely used to enhance weak fault signatures in machinery and has obtained remarkable achievements in engineering application, the parameter optimization of the existing SR-based methods requires the quantification indicators dependent on prior knowledge of the defects to [...] Read more.
Although stochastic resonance (SR) has been widely used to enhance weak fault signatures in machinery and has obtained remarkable achievements in engineering application, the parameter optimization of the existing SR-based methods requires the quantification indicators dependent on prior knowledge of the defects to be detected; for example, the widely used signal-to-noise ratio easily results in a false SR and decreases the detection performance of SR further. These indicators dependent on prior knowledge would not be suitable for real-world fault diagnosis of machinery where their structure parameters are unknown or are not able to be obtained. Therefore, it is necessary for us to design a type of SR method with parameter estimation, and such a method can estimate these parameters of SR adaptively by virtue of the signals to be processed or detected in place of the prior knowledge of the machinery. In this method, the triggered SR condition in second-order nonlinear systems and the synergic relationship among weak periodic signals, background noise and nonlinear systems can be considered to decide parameter estimation for enhancing unknown weak fault characteristics of machinery. Bearing fault experiments were performed to demonstrate the feasibility of the proposed method. The experimental results indicate that the proposed method is able to enhance weak fault characteristics and diagnose weak compound faults of bearings at an early stage without prior knowledge and any quantification indicators, and it presents the same detection performance as the SR methods based on prior knowledge. Furthermore, the proposed method is more simple and less time-consuming than other SR methods based on prior knowledge where a large number of parameters need to be optimized. Moreover, the proposed method is superior to the fast kurtogram method for early fault detection of bearings. Full article
(This article belongs to the Special Issue Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis)
►▼ 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