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Recent Trends and Advances in Fault Detection and Diagnostics

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 15879

Special Issue Editors

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

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

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Published Papers (10 papers)

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Research

23 pages, 991 KiB  
Article
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 1076
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. [...] Read more.
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)
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23 pages, 2784 KiB  
Article
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
Viewed by 966
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 [...] Read more.
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)
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16 pages, 4030 KiB  
Article
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
Viewed by 970
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 [...] Read more.
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)
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17 pages, 7305 KiB  
Article
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 1196
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 [...] Read more.
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)
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14 pages, 3917 KiB  
Article
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 - 06 Feb 2023
Cited by 8 | Viewed by 2486
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 [...] Read more.
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)
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23 pages, 10360 KiB  
Article
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 - 09 Jan 2023
Cited by 4 | Viewed by 1064
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, [...] Read more.
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)
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18 pages, 2636 KiB  
Article
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 1886
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 [...] Read more.
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)
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16 pages, 2024 KiB  
Article
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 4 | Viewed by 1134
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 [...] Read more.
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)
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24 pages, 3753 KiB  
Article
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 2 | Viewed by 1880
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 [...] Read more.
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)
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26 pages, 9862 KiB  
Article
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 4 | Viewed by 1911
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 [...] Read more.
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 dc, 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)
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