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A novel DOA estimation algorithm using directional antennas in cylindrical conformal arrays

Defence technology（防务技术） 2021年第3期17卷 1042-1051页

作者： Xiao-feng Gao Ping Li Xin-hong Hao Guo-lin Li Zhi-jie Kong Science and Technology on Electromechanical Dynamic Control Laboratory School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing100081China China Academy of Launch Vehicle Technology Beijing100076China

In this paper, a novel direction of arrival(DOA) estimation algorithm using directional antennas in cylindrical conformal arrays(CCAs) is proposed. To eliminate the shadow effect, we divide the CCAs into several subarrays to obtain the complete output vector. Considering the anisotropic radiation pattern of a CCA, which cannot be separated from the manifold matrix, an improved interpolation method is investigated to transform the directional subarray into omnidirectional virtual nested arrays without non-orthogonal perturbation on the noise vector. Then, the cross-correlation matrix(CCM) of the subarrays is used to generate the consecutive co-arrays without redundant elements and eliminate the noise vector. Finally, the full-rank equivalent covariance matrix is constructed using the output of co-arrays,and the unitary estimation of the signal parameters via rotational invariance techniques(ESPRIT) is performed on the equivalent covariance matrix to estimate the DOAs with low computational complexity. Numerical simulations verify the superior performance of the proposed algorithm, especially under a low signal-to-noise ratio(SNR) environment.

关键词： Direction of arrival(DOA) Conformal antenna array Interpolation technique Nested array

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Rolling Bearing Fault Diagnosis Method Based on Wavelet Time–Frequency Map and Improved ConvNext 1

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Qin, Feifan Zhang, Chao Wang, Jianguo Zhao, Wentao Li, Jianjun Liu, Tongtong College of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014010 China Inner Mongolia Key Laboratory for Intelligent Diagnosis and Control of Electromechanical Systems Baotou014010 China College of Information Engineering Inner Mongolia University of Science and Technology Baotou014010 China

ISBN: (数字)9783031494215

ISBN: (纸本)9783031494208

The purpose of this article is to address the issue of traditional fault diagnosis methods for rolling bearings, which are unable to accurately diagnose the fault signals of non-linear and non-stationary rolling bearings. To achieve this goal, this article proposes a rolling bearing fault diagnosis method based on wavelet time–frequency maps and an improved ConvNext model. This method first leverages the advantages of wavelet time–frequency analysis to transform the original signal into a two-dimensional wavelet time–frequency map. Subsequently, a new generation of convolutional neural networks, ConvNext, is utilized as the foundational network for rolling bearing fault diagnosis models. The network is enhanced by introducing lightweight attention modules, such as the Convolutional Block Attention Module (CBAM) and the Efficient Channel Attention Network (ECA), to improve the Block modules in the basic network. The wavelet time–frequency map is then used as a feature map input to the improved ConvNext network for training, aiming to achieve rolling bearing fault diagnosis recognition. Finally, experimental results demonstrate that the proposed method exhibits a high level of fault recognition accuracy and stability in laboratory data. It also demonstrates a certain degree of robustness, offering a new solution for rolling bearing fault diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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An open dataset for intelligent recognition and classification of abnormal condition in longwall mining

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Coal International 2024年第4期272卷 22-35页

作者： Yang, Wenjuan Zhang, Xuhui Ma, Bing Wang, Yanqun Wu, Yujia Yan, Jianxing Liu, Yongwei Zhang, Chao Wan, Jicheng Wang, Yue Huang, Mengyao Li, Yuyang Zhao, Dian School of Mechanical Engineering Xi’an University of Science and Technology No.58 Mid-Yanta Road Xi’an710054 China Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control No.58 Yanta Road Xi’an710054 China MARCO automatic control system development Co. LTD No.20 Fenghui South Road Xi’an710054 China

The underground coal mine production of the fully mechanised mining face exists many problems, such as poor operating environment, high accident rate and so on. Recently, the intelligent autonomous coal mining is gradually replacing the traditional mining process. The artificial intelligence technology is an active research area and is expected to identify and warn the underground abnormal conditions for intelligent longwall mining. It is inseparable from the construction of datasets, but the downhole dataset is still blank at present. This work develops an image dataset of underground longwall mining face (DsLMF+), which consists of 138004 images with annotation 6 categories of mine personnel, hydraulic support guard plate, large coal, towline, miners’ behaviour, and mine safety helmet. All the labels of dataset are publicly available in YOLO format and COCO format. The availability and accuracy of the datasets were reviewed by experts in coal mine field. The dataset is open access and aims to support further research and advancement of the intelligent identification and classification of abnormal conditions for underground mining. © 2024 Tradelink Publications Ltd.. All rights reserved.

关键词： Longwall mining

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A Lightweight Network for the Hot-Rolled Steel Strip Surface Defect Detection

A Lightweight Network for the Hot-Rolled Steel Strip Surface...

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作者： Liu, Jiaxue Zhang, Chao Li, Jianjun College of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014000 China Inner Mongolia Key Laboratory for Intelligent Diagnosis and Control of Electromechanical Systems Baotou014010 China College of Information Engineering Inner Mongolia University of Science and Technology Baotou014000 China

ISBN: (纸本)9783031494123

With the amazing development of deep learning, more and more research apply it to the field of computer vision in recent years. However, most object detection networks are difficult to apply in our real life because of the complex structures and numerous parameters. So, it is meaningful to focus our attention on designing some lightweight network architecture in the future. In this paper, we will propose a new convolutional neural network that named M2I3-SSD (MobileNetV2-InceptionV3 Single Shot MultiBox Detector) and its variant named M2I3R-SSD (MobileNetV2-InceptionV3-NoRelu6 Single Shot MultiBox Detector) to detect the surface defection of the hot-rolled steel strip. Then fine-tune as a transfer learning method was used in our paper to reduce the overfitting problem caused by small database. And we do a lot of experiments to verify the performance about the proposed model in the end. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Object detection

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Bearing Remaining Life Prediction Based on Temporal Convolutional Networks with Hybrid Dilated Convolutions

Bearing Remaining Life Prediction Based on Temporal Convolut...

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作者： Zhang, Jing Li, Haiguang Zhang, Chao Wu, Yangbiao Liu, Guiyi College of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014010 China College of Energy and Environment Inner Mongolia University of Science and Technology Baotou014010 China Inner Mongolia Key Laboratory for Intelligent Diagnosis and Control of Electromechanical Systems Baotou014010 China

ISBN: (纸本)9783031494208

Rolling bearings play a crucial role in mechanical equipment, impacting both running time and costs significantly. Accurate prediction of remaining life allows timely maintenance actions to be taken, thereby reducing downtime and maintenance costs, and is essential for effective equipment maintenance and operation. In this paper, we propose a new bearing life prediction model based on hybrid dilated time convolutional network structure. We address the issue of convolutional kernel discontinuity caused by empty convolutions in the Temporal Convolutional Network by introducing the Hybrid Dilated Convolution technique. This alleviates the problem of local feature loss to some extent and enables more global features to be captured. Finally, the effectiveness of the model was validated on the publicly available FEMTO-ST dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolution

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A hybrid piecewise FDD strategy for refrigeration charge fault of airborne vapor-compression cycle system

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INTERNATIONAL JOURNAL OF REFRIGERATION 2022年 135卷 164-173页

作者： Sun, Chengbin Wang, Hexu Jiang, Yanlong Gao, Zanjun Wang, Ju [a]Key Laboratory of Aircraft Environment Control and Life Support MIIT Nanjing University of Aeronautics and Astronautics 29 Yudao St. Nanjing 210016 China [b]Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing 211106 China

Many researchers have carried out study on refrigerant charge fault for variable refrigerant flow (VRF) airconditioning system, while few pay attention to refrigerant charge fault for vapor-compression refrigerant cycle system (VCS) of enhanced vapor injection (EVI) with a flash tank onboard. To develop a strategy for refrigerant charge fault of the above system, the optimization methods have been implemented to predict the refrigerant charge level (RCL) based on virtual refrigerant charge (VRC) models. Firstly, Pearson correlation coefficient analysis is employed to obtain the characteristic parameters. Secondly, a piecewise modified VRC model with the different characteristic parameters from VRC models is obtained for two cases of RCL < 75% and RCL > 75%. Meanwhile, combined with the principal component analysis (PCA) and the support vector machine (SVM) algorithm, a PCA-SVM model is developed to improve the prediction accuracy, and the average root mean squared error (RMSE) is only 7.8 compared to 14.9 of the modified VRC model when RCL > 75%, meanwhile, the correctly detected ratio is improved from 62.8% to 84.5%. Then, for solving the difficulty of fault detection and diagnosis (FDD) when RCL > 160%, the moving time window average (MA) algorithm is introduced and an online PCA-MA-SVM model is developed. Finally, a hybrid piecewise FDD strategy combined with three models for refrigeration charge fault in airborne VCS of EVI with a flash tank is obtained.

关键词： Vapor-compression refrigeration cycle system Enhanced vapor injection Refrigerant charge fault Support vector machine Moving time window average

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Modeling and validation of key components of thermal management systems

Modeling and validation of key components of thermal managem...

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2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024

作者： Zekun, Mei Shuai, Xu Fanxin, Meng Guangya, Zhu Dalin, Zhang Weijian, Chen Key Laboratory of Aircraft Environment Control and Life Support MIIT Nanjing University of Aeronautics and Astronautics Nanjing China 805th Research Institute of China Aerospace Science and Technology Corporation Shanghai China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration No.33 Shuige Road Nanjing China

ISBN: (数字)9781837242108

In this paper, two key components of thermal management system are modeled by the underlying logic operation of SIMULINK. Simulink-PS signal conversion module is used to connect the electric grid and the calculation part of the heat grid to build the fuel tank heat grid model. The temperature transient simulation model of phase-change heat exchanger was established by using the integral module in Simulink library. Experimental verification and literature data verification are provided for these two components. The results show that the error between simulation results and experimental data is between 0.1% and 3%, which proves the accuracy and feasibility of the system modeling in this paper. It lays a foundation for the simulation of the overall thermal management scheme. © The Institution of Engineering & technology 2024.

关键词： Fuel tanks

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Sea Surface Scattering Characteristics Based on Slope Statistics of Sliding Window 13

Sea Surface Scattering Characteristics Based on Slope Statis...

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13th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2021

作者： Xie, Yue Li, Tie Tian, Bo Li, Tuan Science and Technology on Electromechanical Dynamic Control Laboratory Xi'an China

ISBN: (纸本)9781665418126

In view of the insufficient applicability of the current engineer laser short-range sea surface scattering echo related calculation model, it is impossible to effectively evaluate the performance of the system under different conditions, the simulated wave pool environment is used to obtain the detection system at a fixed height and attitude. Data has limitations such as limited sample points and high funding. This paper proposes a sea surface scattering echo model based on three-point sliding window function statistics. By discretizing the sampling of the wave cross section and obtaining the slope statistics under different intersection conditions, the ray tracing method and the slope value are used to determine the effective surface element. The statistical value of the detection probability and the value of the SNR interval under different sea conditions and attitude angles are obtained by simulation, this provides adaptability assessment and related technical index reference for laser short-range sea surface detection. © 2021 IEEE.

关键词： Surface scattering

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Unsupervised Feature Selection with self-Weighted and 2,0-Norm Constraint 48

Unsupervised Feature Selection with self-Weighted and 2,0-No...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yuan, Yongjin Wang, Zheng Nie, Feiping Li, Xuelong Northwestern Polytechnical University School of Computer Science Xi'an710072 China Xi'an710072 China Science and Technology on Electromechanical Dynamic Control Laboratory China Ministry of Industry and Information Technology Key Laboratory of Intelligent Interaction and Applications Northwestern Polytechnical University Xi'an710072 China

ISBN: (纸本)9781728163277

At data mining field, it is a fundamental problem to dispose of high-dimensional data. Many existing unsupervised methods select features by manifold learning or exploring spectral analysis, thus preserving the intrinsic structure of raw data. But most of them follow an assumption that all features are equally importance. To settle this problem, we draw a novel feature selection module that simultaneously performs learning of feature weights matrix, similarity graph structure and projection matrix, so that the local structure after feature weighting and subspace sparse projection is received. Finally, we solve the model based on 2,0-norm directly by an iterative optimization algorithm and demonstrate the feasibility and effectiveness of our approach via extensive experiments. © 2023 IEEE.

关键词： Feature Selection

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Experimental Research on Millimeter Wave Scattering Characteristics of Small UAV

Experimental Research on Millimeter Wave Scattering Characte...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Wenhui Hu Bo Sun Changhao Ma Yongdiao Wen Jing Li Tie Li Science and Technology on Electromechanical Dynamic Control Laboratory Xi'an Institute of Mechanical and Electrical Information Technology Xian China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

In view of the weak echo and low detectability of low, small and slow targets, this paper takes a typical small UAV as the target, carries out low-altitude flight detection and Ka-band scattering imaging experiments based on the turntable model, and obtains its dynamic scattering characteristics and high-resolution two-dimensional scattering imaging. The results can provide basic data support for subsequent target detection and recognition.

关键词： Shafts Target recognition Antenna theory Scattering Object detection Autonomous aerial vehicles Cameras Ka-band

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