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Enhanced gear fault diagnosis via heterodyne downconversion: Theoretical verification and optimized ultrasonic signal acquisition

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Measurement 2025年 256卷

作者： Jingchao Zhang Shaoqun Zhang Yixue Dong Chen Li Guoqian Jiang Yingwei Li Xiaoli Li School of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao 066004 China Intelligent Control System R&D Department Computer Application Technology Research Institute China Ordnance Industry Beijing 100089 China School of Electrical Engineering Yanshan University Qinhuangdao 066004 China School of Automation Science and Engineering South China University of Technology Guangzhou 510641 China Pazhou Lab Guangzhou 510330 China

Ultrasonic fault diagnosis has emerged as a promising technique for gear fault detection, owing to its capability to capture modulated high-frequency transients induced by incipient or localized defects. However, its practical application is constrained by the requirement for extremely high sampling rates. Heterodyne downconversion offers a potential solution by translating ultrasonic spectral components to lower frequencies, though its effectiveness in preserving diagnostic features remains insufficiently validated. This study conducts a theoretical analysis of the influence of heterodyne downconversion on ultrasonic signal characteristics and proposes an optimized heterodyne-based ultrasonic acquisition system with enhanced charge amplification and frequency conversion circuits. Experimental evaluations using a gear fault test platform demonstrate that the downconverted signals preserve the envelope spectral features of the original ultrasonic signals. Furthermore, comparative analyses indicate that the proposed method achieves superior fault detection sensitivity compared to conventional vibration-based techniques, particularly under high rotational speeds. These findings validate the feasibility and diagnostic advantages of the proposed heterodyne-based approach for efficient and accurate ultrasonic condition monitoring.

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RRI-Net: Classification of Multi-class Retinal Diseases with Deep Recurrent Residual Inception Network using OCT Scans

RRI-Net: Classification of Multi-class Retinal Diseases with...

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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Bilal Hassan Shiyin Qin Ramsha Ahmed School of Automation Science and Electrical Engineering Beihang University (BUAA) Beijing China School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan China School of Computer and Communication Engineering University of Science & Technology Beijing Beijing China

ISBN: (纸本)9781665415897;9781665446839

Optical coherence tomography (OCT) is a label-free, non-invasive imaging technique that is widely used in the diagnosis of various ophthalmic diseases. The diagnostic information related to these diseases is embodied in the texture and geometric features of the OCT scans, which are used by the retinal experts for interpretation and classification. However, due to the large number of OCT scans obtained every day, doctors and hospital staff are unable to meaningfully examine the potential retinal pathological conditions (RPCs), resulting in unexpected delays in the diagnosis and treatment of RPCs. In this paper, we propose an automated deep recurrent residual inception network, RRI-Net, for the classification of retinal OCT scans into diagnostically relevant classes, including healthy, age-related macular degeneration (AMD), diabetic macular edema (DME) and choroidal neovascularization (CNV). The proposed RRI-Net employs residual connections with cascaded multi-kernel convolutions to provide optimal training and classification results. In addition, we conducted extensive training of RRI-Net using 108,312 OCT scans, and tested the performance of the proposed framework over 1,000 OCT scans. The results show that RRI-Net achieves 98.8% accuracy in multi-class classification problem between healthy, AMD, DME and CNV, with 97.6% true positive rate and 99.2% true negative rate, outperforming other state-of-the-art methods.

关键词： Training Pathology Hospitals Optical coherence tomography Imaging Retina Diabetes

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Adaptive Backstepping Sliding Mode control for Fractional Order Systems

Adaptive Backstepping Sliding Mode Control for Fractional Or...

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第三十九届中国控制会议

作者： Xiang Chen Shiqi Zheng Feng Liu Xiongliang Zhang Yuan Sun School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems School of Electrical and Electronic Engineering The University of Adelaide

This paper concentrates on the control problem of a class of fractional order nonlinear systems. A new adaptive backstepping sliding mode control is proposed, which inherits both the advantages of backstepping and sliding mode ***, a fractional order sliding mode disturbance observer is proposed to estimate unknown external disturbances. Meanwhile, RBF neural network is adopted to approximate unknown nonlinear terms. Finally, an illustrative simulation result based on a practical fractional order permanent magnet synchronous motor model is presented to verify the validity of proposed controller.

关键词： Fractional order systems adaptive sliding mode control disturbance observer backstepping

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Preface

Engineering Applications of Computational Methods

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engineering Applications of Computational Methods 2023年 14卷 VII-X页

作者： Li, Yuanzheng Zhao, Yong Wu, Lei Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ United States

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A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning

A Precision Diagnostic Framework of Renal Cell Carcinoma on ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jialun Wu Ruonan Zhang Tieliang Gong Xinrui Bao Zeyu Gao Haichuan Zhang Chunbao Wang Chen Li School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China Library of Xi’an Jiaotong University Xi’an China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University Park PA USA the First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9781665429825

Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. However, pathological diagnosis is subjective, and differences in observation and diagnosis between pathologists are common. This phenomenon is more evident in hospitals with insufficient medical resources. Deep learning (DL) can be used to identify and classify structures in digital pathology. In order to solve the above difficulties, in this work, we propose a DL framework for generating pathological diagnosis by analyzing histopathological images of renal cell carcinoma. A deep neural network is trained on a large high-quality annotated dataset for accurate tumor area detection, subtyping, and grading. The results show that our framework has achieved pathologist-level accuracy in diagnosis, can generate pathology reports with tumor indicators, and provide pathologists with interpretable auxiliary diagnoses

关键词： Deep learning Pathology Visualization Pipelines Neural networks Ontologies Prostate cancer

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How to secure distributed filters under sensor attacks

arXiv

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arXiv 2020年

作者： He, Xingkang Ren, Xiaoqiang Sandberg, Henrik Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Affiliated with Digital Futures Sweden Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

—We study how to secure distributed filters for linear time-invariant systems with bounded noise under false-data injection attacks. A malicious attacker is able to arbitrarily manipulate the observations for a time-varying and unknown subset of the sensors. We first propose a recursive distributed filter consisting of two steps at each update. The first step employs a saturation-like scheme, which gives a small gain if the innovation is large corresponding to a potential attack. The second step is a consensus operation of state estimates among neighboring sensors. We prove the estimation error is upper bounded if the filter parameters satisfy a condition. We further analyze the feasibility of the condition and connect it to sparse observability in the centralized case. When the attacked sensor set is known to be time-invariant, the secured filter is modified by adding an online local attack detector. The detector is able to identify the attacked sensors whose observation innovations are larger than the detection thresholds. Also, with more attacked sensors being detected, the thresholds will adaptively adjust to reduce the space of the stealthy attack signals. The resilience of the secured filter with detection is verified by an explicit relationship between the upper bound of the estimation error and the number of detected attacked sensors. Moreover, for the noise-free case, we prove that the state estimate of each sensor asymptotically converges to the system state under certain conditions. Numerical simulations are provided to illustrate the developed results. Copyright © 2020, The Authors. All rights reserved.

关键词： State estimation

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A Family of Switched-capacitor-based Hybrid DC-DC Converters with Continuously Adjustable Gain

A Family of Switched-capacitor-based Hybrid DC-DC Converters...

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International Conference on Power Electronics and Motion control (IPEMC)

作者： Shouxiang Li Zhenning Li Cheng Huang Shuhua Zheng Pengyu Jia School of Automation Beijing Institute of Technology Beijing China School of Electrical and Control Engineering North China University of Technology Beijing China

ISBN: (数字)9781728153018

ISBN: (纸本)9781728153025

The poor voltage regulation capability of traditional switched-capacitor converters (SCCs) is one severe disadvantage that limits the applicable fields. In this paper, one of the diodes in the five common step-up SCCs - Ladder, Dickson, Fibonacci, Series-parallel, Voltage Doubler- is replaced by a filter inductor. As a result, the new formed switched-capacitor-based (SC-based) hybrid converters attain the wide and continuous voltage regulation capability. Different replaced diode positions lead to different characteristics, so a brief comparison of voltage gain is given. The added inductor may conduct reverse current and thus deteriorate the efficiency, so a boundary map is derived to avoid such region. A hybrid converter based on the 3X Ladder SCC was built to verify the analysis.

关键词： Switches DC-DC power converters Power electronics Hybrid power systems Voltage control Inductors Motion control

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False Noise Attack Detection for differentially-private distributed control of microgrids

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Automatica 2025年 179卷

作者： Ye, Feng Cao, Xianghui Cai, Lin Chow, Mo-Yuen School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Department of Electrical and Computer Engineering University of Victoria Victoria BCV8W 3P6 Canada UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Privacy preserving in distributed control is getting more attention, and differential privacy (DP) is the common tool to protect data privacy, in which additive noise is applied in the algorithm function. However, DP can be leveraged by false noise (FN) attacks because attack vectors can be disguised as artificial noise in DP. FN attacks are a concern as the stealth attacks are hard to detect. Moreover, DP in distributed control makes FN attack detection more difficult. Hence, detecting FN attacks in privacy-preserving distributed control is critical and challenging. In this paper, taking distributed energy management systems as the control object, we propose a novel peer-to-peer attack detection approach, named False Noise Attack Detection (FNAD). In FNAD, each device observes the power decisions of its neighbors based on the data from its two-hop neighbors, estimates the power decisions of its neighbors by a Kalman filter, and updates the detection index of each neighbor according to the residues of the Kalman filter at each iteration. The detection index is developed based on information entropy, without any prior knowledge of the FN attacks. If a device's detection index is out of well-defined thresholds, its neighbors can perform a majority vote to decide whether it is malicious. We theoretically prove the detection effect of FNAD against three representative attacks in the literature and analyze the advantages of FNAD compared with the traditional methods. The effectiveness of FNAD is demonstrated by extensive simulations. © 2025 Elsevier Ltd

关键词： State estimation

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Correction: A Novel Integrated Energy Management Strategy of Energy Storage System for a Pure Electric-Driven Mining Hydraulic Excavator

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Chinese Journal of Mechanical engineering 2025年第1期38卷 1-1页

作者： Qin, Tao Li, Yunhua Quan, Long Yao, Yu Yang, Liman School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Tianma Intelligent Control Technology Co. Ltd. Beijing China Key Lab of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Tai Yuan China

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DNAD: Differentiable Neural Architecture Distillation

arXiv

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arXiv 2025年

作者： Rao, Xuan Zhao, Bo Liu, Derong School of Systems Science Beijing Normal University Beijing100875 China School of Automation and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518000 China Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60607 United States

To meet the demand for designing efficient neural networks with appropriate trade-offs between model performance (e.g., classification accuracy) and computational complexity, the differentiable neural architecture distillation (DNAD) algorithm is developed based on two cores, namely search by deleting and search by imitating. Primarily, to derive neural architectures in a space where cells of the same type no longer share the same topology, the super-network progressive shrinking (SNPS) algorithm is developed based on the framework of differentiable architecture search (DARTS), i.e., search by deleting. Unlike conventional DARTS-based approaches which yield neural architectures with simple structures and derive only one architecture during the search procedure, SNPS is able to derive a Pareto-optimal set of architectures with flexible structures by forcing the dynamic super-network shrink from a dense structure to a sparse one progressively. Furthermore, since knowledge distillation (KD) has shown great effectiveness to train a compact network with the assistance of an over-parameterized model, we integrate SNPS with KD to formulate the DNAD algorithm, i.e., search by imitating. By minimizing behavioral differences between the super-network and teacher network, the over-fitting of one-level DARTS is avoided and well-performed neural architectures are derived. Experiments on CIFAR-10 and ImageNet classification tasks demonstrate that both SNPS and DNAD are able to derive a set of architectures which achieve similar or lower error rates with fewer parameters and FLOPs. Particularly, DNAD achieves the top-1 error rate of 23.7% on ImageNet classification with a model of 6.0M parameters and 598M FLOPs, which outperforms most DARTS-based methods. Copyright © 2025, The Authors. All rights reserved.

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