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Fault-induced gear meshing modulation sideband extraction and evaluation for fault diagnosis of planetary gearboxes

Science China Technological Sciences 2025年第1期 339-355页

作者： Peng ZHOU Xiaoluo YU Yang YANG qingbo he Zhike PENG State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Science and Technology on Vehicle Transmission Laboratory China North Vehicle Institute School of Mechanical Engineering Ningxia University

Gearboxes, known for their compact size and stable transmission capability, are widely used as power transmission structures in various types of mechanical equipment, such as wind turbines, helicopters, and special vehicles. However, due to harsh and non-stationary working conditions, gear surfaces often deteriorate, leading to faults such as wear, pitting, and cracking. Therefore, it is vital to monitor the working status of gearboxes and diagnose gear faults as early as possible. Gear faults can induce characteristic modulation effects near the gear meshing frequency(GMF), resulting in the appearance of faultinduced sidebands in a vibration spectrum. Extraction of these sidebands allows for the diagnosis of gear faults in a ***, when faced with a planetary gearbox having a complex configuration, strong background noise and additional faultunrelated sidebands can interfere with fault-induced sidebands, making accurate diagnosis difficult. To address these challenging issues, this paper proposes a novel fault-induced gear meshing modulation sideband extraction method. In the process,the sidebands directly related to the gear fault near the GMF are first identified and then extracted by a variational harmonic mode decomposition(VHMD) method. Accordingly, a fault-related gear meshing modulation component(GMMC) can be accurately reconstructed by summing up the extracted fault-induced sidebands. Using the GMMC, the gear fault severity can be assessed by evaluating its amplitude modulation(AM) effect. The superior performance of the proposed method is finally demonstrated by experimental data.

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An Interpretable Denoising Layer for Neural Networks Based on Reproducing Kernel Hilbert Space and its Application in Machine Fault Diagnosis

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Chinese Journal of Mechanical Engineering 2021年第3期34卷 104-114页

作者： Baoxuan Zhao Changming Cheng Guowei Tu Zhike Peng qingbo he Guang Meng State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China

Deep learning algorithms based on neural networks make remarkable achievements in machine fault diagnosis,while the noise mixed in measured signals harms the prediction accuracy of *** denoising methods in neural networks,such as using complex network architectures and introducing sparse techniques,always suffer from the difficulty of estimating hyperparameters and the lack of physical *** address this issue,this paper proposes a novel interpretable denoising layer based on reproducing kernel Hilbert space(RKHS)as the first layer for standard neural networks,with the aim to combine the advantages of both traditional signal processing technology with physical interpretation and network modeling strategy with parameter *** investigating the influencing mechanism of parameters on the regularization procedure in RKHS,the key parameter that dynamically controls the signal smoothness with low computational cost is selected as the only trainable parameter of the proposed ***,the forward and backward propagation algorithms of the designed layer are formulated to ensure that the selected parameter can be automatically updated together with other parameters in the neural ***,exponential and piecewise functions are introduced in the weight updating process to keep the trainable weight within a reasonable range and avoid the ill-conditioned *** studies verify the effectiveness and compatibility of the proposed layer design method in intelligent fault diagnosis of machinery in noisy environments.

关键词： Machine fault diagnosis Reproducing kernel Hilbert space(RKHS) Regularization problem Denoising layer Neural network

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Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals

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Frontiers of Mechanical Engineering 2022年第4期17卷 143-162页

作者： Xiaoluo YU Yifan HUANGFU Yang YANG Minggang DU qingbo he Zhike PENG State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China Science and Technology on Vehicle Transmission Laboratory China North Vehicle Research InstituteBeijing 100072China

Gearbox fault diagnosis based on vibration sensing has drawn much attention for a long *** highly integrated complicated mechanical systems,the intercoupling of structure transfer paths results in a great reduction or even change of signal characteristics during the process of original vibration ***,using gearbox housing vibration signal to identify gear meshing excitation signal is of great significance to eliminate the influence of structure transfer paths,but accompanied by huge scientific *** paper establishes an analytical mathematical description of the whole transfer process from gear meshing excitation to housing *** gear meshing stiffness(GMS)identification approach is proposed by using housing vibration signals for two stages of inversion based on the mathematical ***,the linear system equations of transfer path analysis are first inverted to identify the bearing dynamic *** the dynamic differential equations are inverted to identify the *** simulation and experimental results demonstrate the proposed method can realize gear fault diagnosis better than the original housing vibration signal and has the potential to be generalized to other speeds and *** interesting properties are discovered in the identified GMS spectra,and the results also validate the rationality of using meshing stiffness to describe the actual gear meshing *** identified GMS has a clear physical meaning and is thus very useful for fault diagnosis of the complicated equipment.

关键词： gearbox fault diagnosis meshing stiffness identification transfer path signal processing

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Fault-induced gear meshing modulation sideband extraction and evaluation for fault diagnosis of planetary gearboxes

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Science China Technological Sciences 2024年

Gearboxes, known for their compact size and stable transmission capability, are widely used as power transmission structures in various types of mechanical equipment, such as wind turbines, helicopters, and special vehicles. However, due to harsh and non-stationary working conditions, gear surfaces often deteriorate, leading to faults such as wear, pitting, and cracking. Therefore, it is vital to monitor the working status of gearboxes and diagnose gear faults as early as possible. Gear faults can induce characteristic modulation effects near the gear meshing frequency (GMF), resulting in the appearance of faultinduced sidebands in a vibration spectrum. Extraction of these sidebands allows for the diagnosis of gear faults in a ***, when faced with a planetary gearbox having a complex configuration, strong background noise and additional faultunrelated sidebands can interfere with fault-induced sidebands, making accurate diagnosis difficult. To address these challenging issues, this paper proposes a novel fault-induced gear meshing modulation sideband extraction method. In the process,the sidebands directly related to the gear fault near the GMF are first identified and then extracted by a variational harmonic mode decomposition (VHMD) method. Accordingly, a fault-related gear meshing modulation component (GMMC) can be accurately reconstructed by summing up the extracted fault-induced sidebands. Using the GMMC, the gear fault severity can be assessed by evaluating its amplitude modulation (AM) effect. The superior performance of the proposed method is finally demonstrated by experimental data.

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A time domain correction method for Doppler-distortion signal from acoustic moving source

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Chinese Journal of Acoustics 2014年第1期33卷 32-41页

作者： LIU Fang he qingbo SheN Changqing HU Fei ZHANG Ao KONG Fanrang Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China

Doppler effect widely exists in the signal from the moving acoustic source. In order to solve such problems as frequency shift and frequency band expansion, a time domain cor- rection method is presented in this paper. First, the discrete time vector for interpolation and the amplitude restoration formula is derived based on the moving relationship and the Morse acoustic theory, then the amplitude weights are corrected and the distortion signal is interpolated. Every point of the discrete signal is operated separately in time domain. Compared with the existing frequency domain methods, this method does not need to know the characteristic frequency beforehand and would not be influenced by the blending of the frequency band. hence, this method can be employed to correct multiple frequency signals and it is also a simple and effective Doppler effect reduction method.

关键词： time A time domain correction method for Doppler-distortion signal from acoustic moving source

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人工智能可以通过操纵波来实现吗?

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上海交通大学学报 2021年第S1期55卷 1-2页

作者：何清波姜添曦上海交通大学机械与动力工程学院

简述了基于波物理特性人工智能超材料的研究背景,总结了人工智能超材料的核心思想在于"学习感知",指出了实现人工智能超材料的关键在于如何构建超材料动力学模型,并分析其与人工智能模型的关联.基于波操纵的人工智能超材料将会为突破信... 详细信息

简述了基于波物理特性人工智能超材料的研究背景,总结了人工智能超材料的核心思想在于"学习感知",指出了实现人工智能超材料的关键在于如何构建超材料动力学模型,并分析其与人工智能模型的关联.基于波操纵的人工智能超材料将会为突破信息感知与处理的传统技术难题开辟新的思路,具有广阔的应用前景.

关键词：超材料人工智能学习感知波物理动力学机理

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New Sensing Technologies for Monitoring Machinery,Structures,and Manufacturing Processes

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Journal of Dynamics, Monitoring and Diagnostics 2023年第2期2卷 69-88页

作者： JDMD Editorial Office Zhaoyan Fan Robert X.Gao qingbo he Yi Huang Tianxi Jiang Zhike Peng Luc Thévenaz Yuyong Xiong Shuncong Zhong Editorial Office of JDMD Chongqing University of TechnologyChongqingPeople’s Republic of China Department of Mechanical Industrialand Manufacturing EngineeringOregon State UniversityCorvallisORUSA Department of Mechanical and Aerospace Engineering Case Western Reserve UniversityClevelandOHUSA State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing School of Mechanical Engineering and AutomationFuzhou UniversityFuzhouPeople’s Republic of China Institute of Precision Instrument and Intelligent Measurement&Control Fuzhou UniversityFuzhouPeople’s Republic of China School of Mechanical Engineering Ningxia UniversityYinchuanPeople’s Republic of China EPFL Ecole Polytechnique Fédérale de Lausanne Group for Fibre OpticsSCI-STI-LT Station 111015 LausanneSwitzerland

Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing *** this paper,we briefly discuss the general idea and advances of various new sensing technologies,including multiphysics sensing,smart materials and metamaterials sensing,microwave sensing,fiber optic sensors,and terahertz sensing,for measuring vibration,deformation,strain,acoustics,temperature,spectroscopic,*** on the observations from the state of the art,we provide comprehensive discussions on the possible opportunities and challenges of these new sensing technologies so as to steer future development.

关键词： fiber optic sensor metamaterials sensing microwave sensing multiphysics sensing terahertz sensing

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Adaptive-basis decomposition-based low-rank network for efficient non-uniform motion deblurring

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Optoelectronics Letters 2025年第1期21卷 43-50页

作者： CheN Lei XIONG qingbo ZHANG Wei LI Runde School of Software Henan University School of Applied Technology China University of Labor Relations

In this study, we present a unified sparsity-driven framework that significantly enhances motion deblurring performance by integrating two key components: a custom-designed dataset and a low-rank module(LRM). This framework leverages the inherent sparsity of per-pixel blur kernels to bolster both deblurring accuracy and model interpretability. Firstly, we propose an adaptive-basis decomposition-based deblurring(ADD) approach, which constructs a tailored training dataset to enhance the generalization capacity of the deblurring network. The ADD framework adaptively decomposes motion blur into sparse basis elements, effectively addressing the intricacies associated with non-uniform blurs. Secondly, an LRM is proposed to improve the interpretability of deblurring models as a plug-and-play module, primarily designed to identify and harness the intrinsic sparse features in sharp images. A series of ablation studies have been conducted to substantiate the synergistic advantages of combining the proposed ADD with the LRM for overall improvement in deblurring efficacy. Subsequently, we empirically demonstrate through rigorous experimentation that incorporating the LRM into an existing Uformer network leads to substantial enhancement in reconstruction performance. This integration yields a sparsity-guided low-rank network(SGLRN). Operating under the overarching principle of sparsity, SGLRN consistently outperforms state-of-the-art methods across multiple standard deblurring benchmarks. Comprehensive experimental results, assessed through quantitative metrics and qualitative visual evaluations, provide compelling evidence of its effectiveness. The overall deblurring results are available at Google Drive.

关键词： Image enhancement

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一种新型液压导管应变测量装置设计与研究

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仪器仪表学报 2024年第1期45卷 180-188页

作者：李泽函廖昕昕黄浩何清波上海交通大学机械与动力工程学院上海200240

针对飞行器液压导管应变测量环境复杂,传统有线测量设备传感器布置困难、引线多等问题,提出了一种新型液压导管应变测量方法。该方法基于频响函数法建立测量装置与导管的动力学模型,研究了两者之间的应变映射关系,设计了可拆卸式测量装... 详细信息

针对飞行器液压导管应变测量环境复杂,传统有线测量设备传感器布置困难、引线多等问题,提出了一种新型液压导管应变测量方法。该方法基于频响函数法建立测量装置与导管的动力学模型,研究了两者之间的应变映射关系,设计了可拆卸式测量装置,实现传感器非接触测量导管应变;结合理论计算和有限元仿真,研究了测量装置对导管模态及应变的影响,优化了装置设计及相关尺寸参数;通过试验验证有限元仿真结果,检验了装置非接触测量液压导管应变方法的有效性。结果表明,测量装置对导管应变影响微弱,300 Hz以下应变重构相对平均偏差为6.2%,证明测量装置能够有效测得导管应变,该方法和装置有望解决复杂测量环境下飞行器液压导管应变的快速准确测量问题。

关键词：液压导管应变测量频响函数可拆卸式

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基层治理数字增负及其破解

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理论探索 2024年第3期 84-91页

作者：盛明科贺清波湖南人文科技学院湖南娄底417000 湖南大学长沙410006

数字赋能增效是基层减负的重要着力点。然而,旨在赋能增效的数字技术在现实中反倒增加了基层负担,导致数字技术的效益悖反。在基层数字治理交互场域,来自上级组织事务下沉、同级组织协同不足及社会公众诉求泛化的三重压力,构成了基层组... 详细信息

数字赋能增效是基层减负的重要着力点。然而,旨在赋能增效的数字技术在现实中反倒增加了基层负担,导致数字技术的效益悖反。在基层数字治理交互场域,来自上级组织事务下沉、同级组织协同不足及社会公众诉求泛化的三重压力,构成了基层组织面对多重主体施压被动承接的数字负担。从主动加码角度来看,基层组织面对上级组织的“向上”表现、同级组织的创新内卷以及对于社会公众的迭代过频,此三重压力构成了基层组织主动制造的数字负担。破解基层治理的数字增负,需要明确上下权责,强化基层治权;推动同级合作,缓解创新内卷;促进政社沟通,提升数治素养。

关键词：被动加压主动加码数字赋能基层负担治理转型

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