Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Recent Trends and Advances in Fault Detection and Diagnostics

Special Issue Editors
Special Issue Information
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 23082

Share This Special Issue

Special Issue Editors

Prof. Dr. Huimin Zhao

E-Mail Website
Guest Editor

College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
Interests: feature extraction; fault diagnosis; signal analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Wu Deng

E-Mail Website
Guest Editor

College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
Interests: intelligent optimization; resource scheduling; intelligent diagnosis; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of science and technology has developed rapidly in the last few decades. In order to keep pace with new developments, more complex mechanical equipment with higher accuracy and efficiency is required. However, the reliable operation of this equipment has become a topic of concern. Health monitoring and fault diagnosis technology are key technologies in ensuring the safe and reliable operation of equipment such as bearings, gears and rotors and so on. Research on health assessments and fault diagnosis methods and technologies is essential to ensure the safe operation of equipment and prevent major accidents. This Special Issue welcomes any original and high-quality papers that address, but are not limited to, the following:

(1) Weak fault detection method;

(2) Advanced signal processing techniques;

(3) Data-driven diagnosis method;

(4) Hybrid diagnosis method;

(5) Health condition monitoring;

(6) Deep learning and transfer learning;

(7) Algorithm applications.

Dr. Huimin Zhao
Prof. Dr. Wu Deng
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.

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 polices can be found here.

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

23 pages, 991 KiB

Open AccessArticle

Improved Skip-Gram Based on Graph Structure Information

by Xiaojie Wang, Haijun Zhao and Huayue Chen

Sensors 2023, 23(14), 6527; https://doi.org/10.3390/s23146527 - 19 Jul 2023

Viewed by 2003

Abstract

Applying the Skip-gram to graph representation learning has become a widely researched topic in recent years. Prior works usually focus on the migration application of the Skip-gram model, while Skip-gram in graph representation learning, initially applied to word embedding, is left insufficiently explored. To compensate for the shortcoming, we analyze the difference between word embedding and graph embedding and reveal the principle of graph representation learning through a case study to explain the essential idea of graph embedding intuitively. Through the case study and in-depth understanding of graph embeddings, we propose Graph Skip-gram, an extension of the Skip-gram model using graph structure information. Graph Skip-gram can be combined with a variety of algorithms for excellent adaptability. Inspired by word embeddings in natural language processing, we design a novel feature fusion algorithm to fuse node vectors based on node vector similarity. We fully articulate the ideas of our approach on a small network and provide extensive experimental comparisons, including multiple classification tasks and link prediction tasks, demonstrating that our proposed approach is more applicable to graph representation learning. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Fault Detection and Diagnostics)

► Show Figures

Figure 1

23 pages, 2784 KiB

Open AccessArticle

Interval Type-II Fuzzy Fault-Tolerant Control for Constrained Uncertain 2-DOF Robotic Multi-Agent Systems with Active Fault Detection

by Wen Yan, Haiyan Tu, Peng Qin and Tao Zhao

Sensors 2023, 23(10), 4836; https://doi.org/10.3390/s23104836 - 17 May 2023

Cited by 1 | Viewed by 1440

Abstract

This study proposed a novel adaptive interval Type-II fuzzy fault-tolerant control for constrained uncertain 2-DOF robotic multi-agent systems with an active fault-detection algorithm. This control method can realize the predefined-accuracy stability of multi-agent systems under input saturation constraint, complex actuator failure and high-order uncertainties. Firstly, a novel active fault-detection algorithm based on pulse-wave function was proposed to detect the failure time of multi-agent systems. To the best of our knowledge, this was the first time that an active fault-detection strategy had been used in multi-agent systems. Then, a switching strategy based on active fault detection was presented to design the active fault-tolerant control algorithm of the multi-agent system. In the end, based on the interval type-II fuzzy approximated system, a novel adaptive fuzzy fault-tolerant controller was proposed for multi-agent systems to deal with system uncertainties and redundant control inputs. Compared with other relevant fault-detection and fault-tolerant control methods, the proposed method can achieve predefinition of stable accuracy with smoother control input. The theoretical result was verified by simulation. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Fault Detection and Diagnostics)

► Show Figures

Figure 1

16 pages, 4030 KiB

Open AccessArticle

A New Fast Control Strategy of Terminal Sliding Mode with Nonlinear Extended State Observer for Voltage Source Inverter

by Chunguang Zhang, Donglin Xu, Jun Ma and Huayue Chen

Sensors 2023, 23(8), 3951; https://doi.org/10.3390/s23083951 - 13 Apr 2023

Cited by 2 | Viewed by 1445

Abstract

To overcome the sensitivity of voltage source inverters (VSIs) to parameter perturbations and their susceptibility to load variations, a fast terminal sliding mode control (FTSMC) method is proposed as the core and combined with an improved nonlinear extended state observer (NLESO) to resist aggregate system perturbations. Firstly, a mathematical model of the dynamics of a single-phase voltage type inverter is constructed using a state-space averaging approach. Secondly, an NLESO is designed to estimate the lumped uncertainty using the saturation properties of hyperbolic tangent functions. Finally, a sliding mode control method with a fast terminal attractor is proposed to improve the dynamic tracking of the system. It is shown that the NLESO guarantees convergence of the estimation error and effectively preserves the initial derivative peak. The FTSMC enables the output voltage with high tracking accuracy and low total harmonic distortion and enhances the anti-disturbance ability. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Fault Detection and Diagnostics)

► Show Figures

Figure 1

17 pages, 7305 KiB

Open AccessArticle

Numerical Simulation and Analysis of Turbulent Characteristics near Wake Area of Vacuum Tube EMU

by Hongjiang Cui, Guanxin Chen, Ying Guan and Huimin Zhao

Sensors 2023, 23(5), 2461; https://doi.org/10.3390/s23052461 - 23 Feb 2023

Cited by 1 | Viewed by 1712

Abstract

Due to aerodynamic resistance, aerodynamic noise, and other problems, the further development of traditional high-speed electric multiple units (EMUs) on the open line has been seriously restricted, and the construction of a vacuum pipeline high-speed train system has become a new solution. In this paper, the Improved Detached Eddy Simulation (IDDES) is used to analyze the turbulent characteristics of the near wake region of EMU in vacuum pipes, so as to establish the important relationship between the turbulent boundary layer, wake, and aerodynamic drag energy consumption. The results show that there is a strong vortex in the wake near the tail, which is concentrated at the lower end of the nose near the ground and falls off from the tail. In the process of downstream propagation, it shows symmetrical distribution and develops laterally on both sides. The vortex structure far from the tail car is increasing gradually, but the strength of the vortex is decreasing gradually from the speed characterization. This study can provide guidance for the aerodynamic shape optimization design of the rear of the vacuum EMU train in the future and provide certain reference significance for improving the comfort of passengers and saving the energy consumption caused by the speed increase and length of the train. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Fault Detection and Diagnostics)

► Show Figures

Figure 1

14 pages, 3917 KiB

Open AccessArticle

Safety Helmet Detection Based on YOLOv5 Driven by Super-Resolution Reconstruction

by Ju Han, Yicheng Liu, Zhipeng Li, Yan Liu and Bixiong Zhan

Sensors 2023, 23(4), 1822; https://doi.org/10.3390/s23041822 - 6 Feb 2023

Cited by 20 | Viewed by 3928

Abstract

High-resolution image transmission is required in safety helmet detection problems in the construction industry, which makes it difficult for existing image detection methods to achieve high-speed detection. To overcome this problem, a novel super-resolution (SR) reconstruction module is designed to improve the resolution of images before the detection module. In the super-resolution reconstruction module, the multichannel attention mechanism module is used to improve the breadth of feature capture. Furthermore, a novel CSP (Cross Stage Partial) module of YOLO (You Only Look Once) v5 is presented to reduce information loss and gradient confusion. Experiments are performed to validate the proposed algorithm. The PSNR (peak signal-to-noise ratio) of the proposed module is 29.420, and the SSIM (structural similarity) reaches 0.855. These results show that the proposed model works well for safety helmet detection in construction industries. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Fault Detection and Diagnostics)

► Show Figures

Figure 1

23 pages, 10360 KiB

Open AccessArticle

Investigation of the Information Possibilities of the Parameters of Vibroacoustic Signals Accompanying the Processing of Materials by Concentrated Energy Flows

by Sergey N. Grigoriev, Mikhail P. Kozochkin, Artur N. Porvatov, Sergey V. Fedorov, Alexander P. Malakhinsky and Yury A. Melnik

Sensors 2023, 23(2), 750; https://doi.org/10.3390/s23020750 - 9 Jan 2023

Cited by 4 | Viewed by 1448

Abstract

Creating systems for monitoring technology processes based on concentrated energy flows is an urgent and challenging task for automated production. Similar processes accompany such processing technologies: intensive thermal energy transfer to the substance, heating, development of the melting and evaporation or sublimation, ionization, and expansion of the released substance. It is accompanied by structural and phase rearrangements, local changes in volumes, chemical reactions that cause perturbations of the elastic medium, and the propagation of longitudinal and transverse waves in a wide frequency range. Vibrational energy propagates through the machine’s elastic system, making it possible to register vibrations on surfaces remotely. Vibration parameters can be used in monitoring systems to prevent negative phenomena during processing and to be a tool for understanding the processes’ kinetics. In some cases, it is the only source of information about the progress in the processing zone. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Fault Detection and Diagnostics)

► Show Figures

Figure 1

18 pages, 2636 KiB

Open AccessArticle

An Efficient End-to-End Multitask Network Architecture for Defect Inspection

by Chunguang Zhang, Heqiu Yang, Jun Ma and Huayue Chen

Sensors 2022, 22(24), 9845; https://doi.org/10.3390/s22249845 - 14 Dec 2022

Viewed by 2368

Abstract

Recently, computer vision-based methods have been successfully applied in many industrial fields. Nevertheless, automated detection of steel surface defects remains a challenge due to the complexity of surface defects. To solve this problem, many models have been proposed, but these models are not good enough to detect all defects. After analyzing the previous research, we believe that the single-task network cannot fully meet the actual detection needs owing to its own characteristics. To address this problem, an end-to-end multi-task network has been proposed. It consists of one encoder and two decoders. The encoder is used for feature extraction, and the two decoders are used for object detection and semantic segmentation, respectively. In an effort to deal with the challenge of changing defect scales, we propose the Depthwise Separable Atrous Spatial Pyramid Pooling module. This module can obtain dense multi-scale features at a very low computational cost. After that, Residually Connected Depthwise Separable Atrous Convolutional Blocks are used to extract spatial information under low computation for better segmentation prediction. Furthermore, we investigate the impact of training strategies on network performance. The performance of the network can be optimized by adopting the strategy of training the segmentation task first and using the deep supervision training method. At length, the advantages of object detection and semantic segmentation are tactfully combined. Our model achieves mIOU 79.37% and [email protected] 78.38% on the NEU dataset. Comparative experiments demonstrate that this method has apparent advantages over other models. Meanwhile, the speed of detection amount to 85.6 FPS on a single GPU, which is acceptable in the practical detection process. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Fault Detection and Diagnostics)

► Show Figures

Figure 1

16 pages, 2024 KiB

Open AccessArticle

A New SCAE-MT Classification Model for Hyperspectral Remote Sensing Images

by Huayue Chen, Ye Chen, Qiuyue Wang, Tao Chen and Huimin Zhao

Sensors 2022, 22(22), 8881; https://doi.org/10.3390/s22228881 - 17 Nov 2022

Cited by 5 | Viewed by 1556

Abstract

Hyperspectral remote sensing images (HRSI) have the characteristics of foreign objects with the same spectrum. As it is difficult to label samples manually, the hyperspectral remote sensing images are understood to be typical “small sample” datasets. Deep neural networks can effectively extract the deep features from the HRSI, but the classification accuracy mainly depends on the training label samples. Therefore, the stacked convolutional autoencoder network and transfer learning strategy are employed in order to design a new stacked convolutional autoencoder network model transfer (SCAE-MT) for the purposes of classifying the HRSI in this paper. In the proposed classification method, the stacked convolutional au-to-encoding network is employed in order to effectively extract the deep features from the HRSI. Then, the transfer learning strategy is applied to design a stacked convolutional autoencoder network model transfer under the small and limited training samples. The SCAE-MT model is used to propose a new HRSI classification method in order to solve the small samples of the HRSI. In this study, in order to prove the effectiveness of the proposed classification method, two HRSI datasets were chosen. In order to verify the effectiveness of the methods, the overall classification accuracy (OA) of the convolutional self-coding network classification method (CAE), the stack convolutional self-coding network classification method (SCAE), and the SCAE-MT method under 5%, 10%, and 15% training sets are calculated. When compared with the CAE and SCAE models in 5%, 10%, and 15% training datasets, the overall accuracy (OA) of the SCAE-MT method was improved by 2.71%, 3.33%, and 3.07% (on average), respectively. The SCAE-MT method is, thus, clearly superior to the other methods and also shows a good classification performance. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Fault Detection and Diagnostics)

► Show Figures

Figure 1

24 pages, 3753 KiB

Open AccessArticle

SA-FEM: Combined Feature Selection and Feature Fusion for Students’ Performance Prediction

by Mingtao Ye, Xin Sheng, Yanjie Lu, Guodao Zhang, Huiling Chen, Bo Jiang, Senhao Zou and Liting Dai

Sensors 2022, 22(22), 8838; https://doi.org/10.3390/s22228838 - 15 Nov 2022

Cited by 6 | Viewed by 2579

Abstract

Around the world, the COVID-19 pandemic has created significant obstacles for education, driving people to discover workarounds to maintain education. Because of the excellent benefit of cheap-cost information distribution brought about by the advent of the Internet, some offline instructional activity started to go online in an effort to stop the spread of the disease. How to guarantee the quality of teaching and promote the steady progress of education has become more and more important. Currently, one of the ways to guarantee the quality of online learning is to use independent online learning behavior data to build learning performance predictors, which can provide real-time monitoring and feedback during the learning process. This method, however, ignores the internal correlation between e-learning behaviors. In contrast, the e-learning behavior classification model (EBC model) can reflect the internal correlation between learning behaviors. Therefore, this study proposes an online learning performance prediction model, SA-FEM, based on adaptive feature fusion and feature selection. The proposed method utilizes the relationship among features and fuses features according to the category that achieved better performance. Through the analysis of experimental results, the feature space mined by the fine-grained differential evolution algorithm and the adaptive fusion of features combined with the differential evolution algorithm can better support online learning performance prediction, and it is also verified that the adaptive feature fusion strategy based on the EBC model proposed in this paper outperforms the benchmark method. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Fault Detection and Diagnostics)

► Show Figures

Figure 1

26 pages, 9862 KiB

Open AccessArticle

An Improved Density Peak Clustering Algorithm for Multi-Density Data

by Lifeng Yin, Yingfeng Wang, Huayue Chen and Wu Deng

Sensors 2022, 22(22), 8814; https://doi.org/10.3390/s22228814 - 15 Nov 2022

Cited by 5 | Viewed by 3007

Abstract

Density peak clustering is the latest classic density-based clustering algorithm, which can directly find the cluster center without iteration. The algorithm needs to determine a unique parameter, so the selection of parameters is particularly important. However, for multi-density data, when one parameter cannot satisfy all data, clustering often cannot achieve good results. Moreover, the subjective selection of cluster centers through decision diagrams is often not very convincing, and there are also certain errors. In view of the above problems, in order to achieve better clustering of multi-density data, this paper improves the density peak clustering algorithm. Aiming at the selection of parameter d_c, the K-nearest neighbor idea is used to sort the neighbor distance of each data, draw a line graph of the K-nearest neighbor distance, and find the global bifurcation point to divide the data with different densities. Aiming at the selection of cluster centers, the local density and distance of each data point in each data division is found, a γ map is drawn, the average value of the γ height difference is calculated, and through two screenings the largest discontinuity point is found to automatically determine the cluster center and the number of cluster centers. The divided datasets are clustered by the DPC algorithm, and then the clustering results are perfected and integrated by using the cluster fusion rules. Finally, a variety of experiments are designed from various perspectives on various artificial simulated datasets and UCI real datasets, which demonstrate the superiority of the F-DPC algorithm in terms of clustering effect, clustering quality, and number of samples. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Fault Detection and Diagnostics)

► Show Figures

Journal Menu

Journal Browser

Recent Trends and Advances in Fault Detection and Diagnostics

Share This Special Issue

Special Issue Editors

Special Issue Information

Benefits of Publishing in a Special Issue

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI