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A Fault Diagnosis Method for Rotating Machinery by Multimode Feature Entropy and Mutual Cooperation Broad Learning System

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IEEE SENSORS JOURNAL 2024年第3期24卷 3084-3097页

作者： Li, Chunlin Hu, Qintai Zhao, Shuping Wu, Jigang Xiong, Jianbin Zhang, Qinghua Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China Guangdong Polytech Normal Univ Sch Automat Guangzhou 510642 Peoples R China Guangdong Univ Petrochem Technol Sch Automat Maoming 525000 Peoples R China

Rotating machinery plays a pivotal role in petrochemical units. However, compound and single faults frequently occur in rotating machinery due to the complexity of operating environments and the coupling of faults. This article presents a new compound fault diagnosis method to address the problem of poor diagnosis effect caused by mutual interference between multiple fault responses. First, the observable signals are decomposed via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Wavelet threshold filtering and reconstruction (FR) of the intrinsic mode function (IMF) are then utilized to construct a feature matrix consisting of multimodal feature entropy (MMFE). Finally, a mutual cooperation broad learning system (MC-BLS) model is developed to identify compound faults rapidly. The proposed theoretical model is validated using compound fault datasets obtained from the Key Laboratory of Guangdong University of Petrochemical Technology (PKL-data) and single fault datasets obtained from the Bearing Data Center of Case Western Reserve University (CWRU-data). Experimental evaluations conducted on these datasets demonstrate accuracy rates above 96% for both the compound fault dataset and the multiple single fault datasets. These results confirm the excellent performance of the proposed method recognizing both single and compound faults.

关键词： Compounds Fault diagnosis Feature extraction Entropy Sensors Standards signal processing algorithms Broad learning system (BLS) fault diagnosis feature entropy machine learning mode decomposition

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DoA Estimation for Hybrid Receivers: Full Spatial Coverage and Successive Refinement

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4730-4744页

作者： Abdelbadie, Ali Mostafa, Mona Bameri, Salime Gohary, Ramy H. Thomas, Dimple Ciena Canada Inc Ottawa ON K2K 3E8 Canada Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Ericsson Canada Inc Ottawa ON K2K 2V6 Canada

We develop two novel algorithms for estimating the direction of arrival (DoA) of multiple sources in fully-connected and partially-connected hybrid analog/digital (HAD) receivers. The first algorithm is based on the observation that the analog combiner projects received signals on a particular subspace, causing the signals corresponding to particular DoAs to be heavily attenuated. Thus, an analog combiner defines spatial sectors, beyond which the DoAs are practically undetectable. To address this difficulty, we perform DoA estimation over an exhaustive set of analog combiners spanning distinct subspaces. To refine the estimates generated by this algorithm, we develop an exponentially-converging algorithm wherein the search window is successively narrowed until convergence. Cram & eacute;r-Rao lower bounds on the root-mean-square error of the proposed algorithms are derived and the superiority of these algorithms over their existing counterparts is established through numerical simulations.

关键词： Direction-of-arrival estimation signal processing algorithms Radio frequency Estimation Receiving antennas Antennas signal to noise ratio DoA estimation large-scale antenna array hybrid analog/digital architectures DFT matrix CRLB

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SCA and IBCD Hybrid Algorithm Based Secure Beamforming Optimization for IRS-Assisted Multiuser CR-SWIPT System

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IEEE signal processing LETTERS 2025年 32卷 336-340页

作者： Xu, Xiaorong Yang, Shuo Liu, Zhaoting Wu, Jun Bao, Jianrong Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China

This letter investigates the design and optimization of secure beamforming in an intelligent reflecting surface (IRS)-assisted multiuser cognitive radio simultaneous wireless information and power transfer (CR-SWIPT) system. The proposed method leverages IRS to address cognitive energy harvesting nodes as potential eavesdroppers (Eve). The objective is to maximize the achievable secrecy rate while satisfying multiple constraints, such as transmit power control, energy harvesting, phase shifts, and maximum tolerable interference power. To solve this highly non-convex optimization problem, we propose a hybrid algorithm that combines successive convex approximation (SCA) and inexact block coordinate descent (IBCD). By decomposing the problem into three sub-problems, local optimal beamforming matrices and phase-shift matrices are obtained using the SCA method and complex circle manifold (CCM) method, respectively. Simulation results show that the secrecy rate at the SWIPT information decoding node in the IRS-assisted multiuser CR-SWIPT system improves significantly, with an approximate 40% enhancement compared to the random phase shift scheme with maximum transmit power. The effectiveness of the proposed algorithm is further validated through secrecy rate performance evaluations under different system configurations.

关键词： Array signal processing Optimization Energy harvesting Interference Decoding signal processing algorithms Computational complexity Transmitting antennas Three-dimensional displays Receiving antennas Intelligent reflecting surface (IRS) CR-SWIPT system secure beamforming optimization successive convex approximation (SCA) inexact block coordinate descent (IBCD)

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Robust Gaussian Mixture Modeling: A K-Divergence Based Approach

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 3578-3594页

作者： Kenig, Ori Todros, Koby Adali, Tulay Ben Gurion Univ Negev Sch Elect & Comp Engn IL-84105 Beer Sheva Israel Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

This paper addresses the problem of robust Gaussian mixture modeling in the presence of outliers. We commence by introducing a general expectation-maximization (EM)-like scheme, called K -BM, for iterative numerical computation of the minimum K -divergence estimator (MKDE). This estimator leverages Parzen's non-parametric K ernel density estimate to down-weight low density regions associated with outlying measurements. Akin to the conventional EM, the K -BM involves successive M aximizations of lower B ounds on the objective function of the M K DE. However, differently from EM, these bounds are not exclusively reliant on conditional expectations. The K -BM algorithm is applied to robust parameter estimation of a finite-order multivariate Gaussian mixture model (GMM). We proceed by introducing a new robust variant of the Bayesian information criterion (BIC) that penalizes the MKDE's objective function. The proposed criterion, called K -BIC, is conveniently applied for robust GMM order selection. In the paper, we also establish a data-driven procedure for selection of the kernel's bandwidth parameter. This procedure operates by minimizing an empirical asymptotic approximation of the mean-integrated-squared-error (MISE) between the underlying density and the estimated GMM density. Lastly, the K -BM, the K -BIC, and the MISE based selection of the kernel's bandwidth are combined into a unified framework for joint order selection and parameter estimation of a GMM. The advantages of the K -divergence based framework over other robust approaches are illustrated in simulation studies involving synthetic and real data.

关键词： Parameter estimation Numerical models Kernel Computational modeling Bandwidth Maximum likelihood estimation signal processing algorithms Divergences estimation theory robust statistics

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A Method for Estimating the Direction of Arrival Without Knowing the Source Number Using Acoustic Vector Sensor Arrays

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Chen, Feng Yang, Desen Mo, Shiqi Huang, Xingyu Wang, Mingguang Zhu, Zhongrui Li, Ying Harbin Engn Univ Natl Key Lab Underwater Acoust Technol Harbin 150001 Peoples R China Harbin Engn Univ Key Lab Marine Informat Acquisit & Secur Minist Ind & Informat Tech nol Harbin 150001 Peoples R China Harbin Engn Univ Coll Underwater Acoust Engn Harbin 150001 Peoples R China

Since it is difficult to obtain the number of sources accurately in practical situations, direction-of-arrival (DOA) estimation methods without the number of sources are very much needed for noncooperative targets and maritime attack-defense pairings. In this article, a DOA estimation method using acoustic vector sensors (AVSs) without a known number of sources is proposed. First, a reference matrix is introduced, and the eigenvalues corresponding to the noise of this matrix converge to 0. Next, a scanning matrix containing the scanning sources is created, and the analysis of noise power invariance is used to determine the power selection method of the scanning sources. The ordering law of the eigenvalues of the source matrix scanned at different angles is applied, and the purpose of estimating DOA without the number of sources is accomplished. The proposed algorithm is capable of performing DOA estimation in situations where the number of sources is uncertain and it does not rely on the array structure, which means it can be used for arbitrary arrays. Finally, the correctness and effectiveness of the proposed algorithm are verified by simulation and experiment.

关键词： Estimation Direction-of-arrival estimation Eigenvalues and eigenfunctions signal processing algorithms Vectors Acoustics Covariance matrices Acoustic vector sensors (AVSs) arbitrary arrays direction of arrival (DOA) noise power number of sources

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SSA: A Uniformly Recursive Bidirection-Sequence Systolic Sorter Array

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第10期35卷 1721-1734页

作者： Gao, Teng Huang, Lan Gao, Shang Wang, Kangping Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

The use of reconfigurable circuits with parallel computing capabilities has been explored to enhance sorting performance and reduce power consumption. Nonetheless, most sorting algorithms utilizing dedicated processors are designed solely based on the parallelization of the algorithm, lacking considerations of specialized hardware structures. This leads to problems, including but not limited to the consumption of excessive I/O interface resources, on-chip storage resources, and complex layout wiring. In this paper, we propose a Systolic Sorter Array, implemented by a Uniform Recurrence Equation (URE) with highly parameterised in terms of data size, bit width and type. Leveraging this uniformly recursive structure, the sorter can simultaneously sort two independent sequences. In addition, we implemented global and local control modes on the FPGA to achieve higher computational frequencies. In our experiments, we have demonstrated the speed-up ratio of SSA relative to other state of the art (SOTA) sorting algorithms using C++ std::sort() as benchmark. Inheriting the benefits from the Systolic Array architecture, the SSA reaches up to 810 Mhz computing frequency on the U200. The results of our study show that SSA outperforms other sorting algorithms in terms of throughput, speed-up ratio, and computation frequency.

关键词： Sorting Systolic arrays Hardware Field programmable gate arrays signal processing algorithms Complexity theory Arrays Systolic array Uniform recurrence equations FPGA Parallel sorting

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The AAV Tracking: Fusion and Adversarial of Global and Local Information

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IEEE signal processing LETTERS 2025年 32卷 96-100页

作者： Zhao, Yuhui Yang, Ruifeng Guo, Chenxia Hao, Yongchang North Univ China Sch Instrument & Elect Taiyuan 030051 Peoples R China

The widespread application of autonomous aerial vehicles (AAVs) in various fields has driven the development of target tracking based on AAV perspectives. Among the existing tracking algorithms, tracking by detection performs well, which depends heavily on the robustness of the object detection and re-identification algorithms. The images obtained from the perspective of drones have complex backgrounds and small image sizes, making tracking tasks more difficult. We introduce the parallel space and channel attention module to optimize the object detection algorithm, exploring compelling spatial and channel dimensions features. We propose the channel-spatial fully connected information extraction module, which explores the connections between pixels from a spatial perspective and considers the relationships between feature maps from a channel perspective, compensating for the shortcomings of convolutional networks in extracting global features. To obtain more discriminative feature descriptions, we design the global-local-residual feature fusion module that divides the image into global and local parts and gets the difference between local information and other information, which helps with data correlation and label matching. Experiments show that our method improves the mAP by 0.4% and AP by 2.1% in the object detection task. In the re-identification task, top-1 accuracy is improved by 5.3% and mAP by 11.3%.

关键词： Target tracking Accuracy signal processing algorithms Object detection Feature extraction Information retrieval Autonomous aerial vehicles Robustness Convolutional neural networks Drones re-identification tracking-by-detection autonomous aerial vehicle (AAV) tracking

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Outlier Indicator Based Projection Fuzzy K-Means Clustering for Hyperspectral Image

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IEEE signal processing LETTERS 2025年 32卷 496-500页

作者： Liu, Xinze Yang, Xiaojun Zhang, Jiale Wang, Jing Nie, Feiping Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong Prov Key Lab Informat Photon Technol China Key Lab Photon Technol Integrated Sensing & Minist Educ China Guangzhou 510006 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning OPTIMAL Xian 710072 Peoples R China

The application of hyperspectral image (HSI) clustering has become widely used in the field of remote sensing. Traditional fuzzy K-means clustering methods often struggle with HSI data due to the significant levels of noise, consequently resulting in segmentation inaccuracies. To address this limitation, this letter introduces an innovative outlier indicator-based projection fuzzy K-means clustering (OIPFK) algorithm for clustering of HSI data, enhancing the efficacy and robustness of previous fuzzy K-means methodologies through a two-pronged strategy. Initially, an outlier indicator vector is constructed to identify noise and outliers by computing the distances between each data point in a reduced dimensional space. Subsequently, the OIPFK algorithm incorporates the fuzzy membership relationships between samples and clustering centers within this lower-dimensional framework, along with the integration of the outlier indicator vectors, to significantly mitigates the influence of noise and extraneous features. Moreover, an efficient iterative optimization algorithm is employed to address the optimization challenges inherent to OIPKM. Experimental results from three real-world hyperspectral image datasets demonstrate the effectiveness and superiority of our proposed method.

关键词： Hyperspectral imaging Optimization Noise Vectors Clustering algorithms Robustness Linear programming signal processing algorithms Photonics Indexes Projection fuzzy K-means hyperspectral image robustness outliers clustering

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Parametric Minimum Error Entropy Criterion: A Case Study in Blind Sensor Fusion and Regression Problems

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5091-5106页

作者： Lopez, Carlos Alejandro Riba, Jaume Univ Politecn Catalunya UPC Dept Teoria Senyal & Comunicac Signal Proc & Commun Grp SPCOM Barcelona 08034 Spain

The purpose of this article is to present the Parametric Minimum Error Entropy (PMEE) principle and to show a case study of the proposed criterion in a blind sensor fusion and regression problem. This case study consists on the estimation of a temporal series with a certain temporal invariance, which is measured from multiple independent sensors with unknown variances and unknown mutual correlations of the measurement errors. In this setting, we show that a particular case of the PMEE criterion is obtained from the Conditional Maximum Likelihood (CML) principle of the measurement model, leading to a semi-data-driven solution. Despite the fact that Information Theoretic Criteria (ITC) are inherently robust, they often result in difficult non-convex optimization problems. Our proposal is to address the non-convexity by means of a Majorization-Minimization (MM) based algorithm. We prove the conditions in which the resulting solution of the proposed algorithm reaches a stationary point of the original problem. In fact, the aforementioned global convergence of the proposed algorithm is possible thanks to a reformulation of the original cost function in terms of a variable constrained in the Grassmann manifold. As shown in this paper, the latter procedure is possible thanks to a homogeneity property of the PMEE cost function.

关键词： Entropy signal processing algorithms Cost function Pollution measurement Minimization Data integration Sensor fusion Robustness Probability density function Manifolds Non-convex optimization conditional maximum likelihood majorization-minimization minimum error entropy Grassmann manifold

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MM-FGRM: Fine-Grained Respiratory Monitoring Using MIMO Millimeter Wave Radar

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Wang, Shuxuan Han, Chong Guo, Jian Sun, Lijuan Nanjing Univ Posts & Telecommun Coll Comp Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing 210023 Peoples R China

Long-term respiratory monitoring plays an extremely important role in the diagnosis of respiratory system-related diseases. It can detect diseases temporally and improve the effectiveness of treatment, which is crucial for home health monitoring. With the rapid development of radar chip technology, noncontact respiratory monitoring based on commercial millimeter-wave radar sensors has received increasing attention. However, traditional signal processing methods are difficult to extract fine respiratory waveforms from radar signals mixed with human body movements. Fortunately, deep learning provides a solution, as we can use its powerful learning ability to learn a mapping from radar signals to real respiratory waveforms. With this method, we can directly observe fine-grained respiratory waveforms and further improve the accuracy of respiratory rate detection. Therefore, we propose MM-FGRM, a respiratory monitoring system based on a commercial 77 GHz multiple input multiple output (MIMO) radar. The core of this system is a deep learning-based network called IQ-Transformer with a self-attention mechanism that aims at capturing the latent respiratory-related features from I/Q components of radar signals directly in each human body region of interest (ROI) and recovering the respiratory waveforms. We collected 12 h of data from eight subjects and conducted experiments. The experimental results show that MM-FGRM can accurately recover respiratory waveforms and provide accurate respiratory rates. In addition, we perform testing on data from two other users who do not participate in the training, and the results verify that MM-FGRM has a strong generalization ability. Our results demonstrate the feasibility of further development of home respiratory monitoring products.

关键词： Radar Monitoring signal processing algorithms Millimeter wave radar Radar signal processing Ultra wideband radar Interference Deep learning mimo radar noncontact respiratory monitoring self-attention mechanism

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