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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) 2025年第1期68卷 335-351页

作者： Peng ZHOU Xiaoluo YU Yang YANG Qingbo HE Zhike PENG State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China School of Mechanical Engineering Ningxia UniversityYinchuan 750021China Science and Technology on Vehicle Transmission Laboratory China North Vehicle InstituteBeijing 100072China

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 ***,due to harsh and non-stationary working conditions,gear surfaces often deteriorate,leading to faults such as wear,pitting,and ***,it is vital to monitor the working status of gearboxes and diagnose gear faults as early as *** faults can induce characteristic modulation effects near the gear meshing frequency(GMF),resulting in the appearance of faultinduced sidebands in a vibration *** 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 *** address these challenging issues,this paper proposes a novel fault-induced gear meshing modulation sideband extraction *** 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)***,a fault-related gear meshing modulation component(GMMC)can be accurately reconstructed by summing up the extracted fault-induced *** the GMMC,the gear fault severity can be assessed by evaluating its amplitude modulation(AM)*** superior performance of the proposed method is finally demonstrated by experimental data.

关键词： planetary gearbox gear meshing frequency modulation sideband signal decomposition fault diagnosis

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Health monitoring of time-varying stochastic structures by latent components and fuzzy expert system

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Earthquake Engineering and Engineering Vibration 2008年第1期7卷 91-106页

作者： M. M. Ettefagh M. H. Sadeghi Vibration and Modal Analysis Laboratory Department of Mechanical Engineering University of Tabriz Tabriz 51666 Iran

In this paper, a novel parametric model-based decomposition method is proposed for structural health monitoring of time-varying structures. For this purpose, the advanced Functional-Series Time-dependent Auto Regressive Moving Average （FS-TARMA） technique is used to estimate the parameters and innovation variance used in the parametric signal decomposition scheme. Additionally, a unique feature extraction and reduction method based on the decomposed signals, known as Latent Components （LCs）, is proposed. To evaluate the efficiency of the proposed method, numerical simulation and an experimental study in the laboratory were conducted on a time-varying structure, where various types of damage were introduced. The Fuzzy Expert System （FES） was used as a classification toot to demonstrate that the proposed method successfully identifies different structural conditions when compared with other methods based on non-reduced and ordinary feature extraction.

关键词： parametric modeling signal decomposition feature extraction/reduction fuzzy classification

Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation

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Global Energy Interconnection 2020年第3期3卷 247-258页

作者： Peizhe Xin Ying Liu Nan Yang Xuankun Song Yu Huang State Grid Economic and Technological Research Institute Co.Ltd. State Grid Office DistrictNorth District of Future Science and Technology CityNorth Seven Changping DistrictBeijing 102209P.R.China Department of Hubei Provincial Collaborative Innovation Center for New Energy Microgrid China Three Gorges UniversityYichangHubei Province 443000P.R.China Stevens Institute of Technology HobokenNJ 07030USA

In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm *** this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)***,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the ***,the model is improved adaptively,and is then solved by using constraint-order *** simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.

关键词： Moving average method signal decomposition Wind power fluctuation characteristics Kernel density estimation Constrained order optimization

SRMD:Sparse Random Mode decomposition

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Communications on Applied Mathematics and Computation 2024年第2期6卷 879-906页

作者： Nicholas Richardson Hayden Schaeffer Giang Tran University of Waterloo WaterlooONCanada University of British Columbia VancouverBCCanada University of California Los AngelesCAUSA

signal decomposition and multiscale signal analysis provide many useful tools for timefrequency *** proposed a random feature method for analyzing time-series data by constructing a sparse approximation to the *** randomization is both in the time window locations and the frequency sampling,which lowers the overall sampling and computational *** sparsification of the spectrogram leads to a sharp separation between time-frequency clusters which makes it easier to identify intrinsic modes,and thus leads to a new data-driven mode *** applications include signal representation,outlier removal,and mode *** benchmark tests,we show that our approach outperforms other state-of-the-art decomposition methods.

关键词： Sparse random features signal decomposition Short-time Fourier transform

Forecasting wind speed using a reinforcement learning hybrid ensemble model:a high-speed railways strong wind signal prediction study in Xinjiang,China

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Transportation Safety and Environment 2023年第4期5卷 17-28页

作者： Bin Liu Xinmin Pan Rui Yang Zhu Duan Ye Li Shi Yin Nikolaos Nikitas Hui Liu Research Institute of Science and Technology of China Railway Urumqi Group Co. Ltd.Urumqi 830011XinjiangChina Xinjiang Uygur Autonomous Region Meteorological Service Urumqi 830002XinjiangChina School of Traffic and Transportation Engineering Central South UniversityChangsha 410075HunanChina School of Civil Engineering University of LeedsLS29JTLeedsUK

Considering the application of wind-forecasting technology along the railway,it becomes an effective means to reduce the risk of tain more reliable wind-speed prediction results,this study proposes an intelligent ensemble forecasting method for strong winds train derailment and *** prediction of crosswinds can provide scientific guidance for safe train *** obalong the high-speed *** method consists of three parts:the data preprocessing module,the hybrid prediction module and original wind speed ***,Broyden-Fletcher-Goldfarb-Shanno(BFGS)method,non-linear autoregressive network with exoge-the reinforcement learing ensemble ***,fast ensemble empirical model decomposition(FEEMD)is used to process the prediction models for all the sublayers of ***,Q-learning is utilized to iteratively calculate the combined weights nous inputs(NARX)and deep belief network(DBN),three benchmark predictors with different characteristics are employed to build of the three models,and the prediction results of each sublayer are superimposed to obtain the model *** real wind speed data of two railway stations in Xinjiang are used for experimental *** show that compared with the single benchmark model,the hybrid ensemble model has better accumacy and robustness for wind speed prediction along the *** 1-step forecasting results mean absolute error(MAE),mean absolute percentage error(MAPE)and root mean square error(RMSE)of Q-leaming-FEEMD-BFGS-NARX-DBN in site #1 and site #2 are 0.0894 m/s,0.6509%,0.1146 m/s,and 0.0458 m/s.0.2709%,0.0616 m/*** proposed ensemble model is a promising method for railway wind speed prediction.

关键词： wind speed forecasting high-speed railways signal decomposition reinforcement learning ensemble model

A hybrid decomposition-boosting model for short-term multi-step solar radiation forecasting with NARX neural network

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Journal of Central South University 2021年第2期28卷 507-526页

作者： HUANG Jia-hao LIU Hui Institute of Artificial Intelligence&Robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic and Transportation EngineeringCentral South UniversityChangsha 410075China

Due to global energy depletion,solar energy technology has been widely used in the *** output power of the solar energy systems is affected by solar *** short-term forecasting of solar radiation can ensure the safety of photovoltaic grids and improve the utilization efficiency of the solar energy *** the study,a new decomposition-boosting model using artificial intelligence is proposed to realize the solar radiation multi-step *** proposed model includes four parts:signal decomposition(EWT),neural network(NARX),Adaboost and *** real solar radiation datasets from Changde,China were used to validate the efficiency of the proposed *** verify the robustness of the multi-step prediction model,this experiment compared nine models and made 1,3,and 5 steps ahead predictions for the time *** is verified that the proposed model has the best performance among all models.

关键词： solar radiation forecasting multi-step forecasting smart hybrid model signal decomposition

Empirical Wavelet Transform;Stationary and Nonstationary signals

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Journal of Electronic & Information Systems 2019年第2期1卷 1-5页

作者： Hesam Akbari Sedigheh Ghofrani Biomedical Engineering Department South Tehran BranchIslamic Azad UniversityTehranIran Electrical Engineering Department South Tehran BranchIslamic Azad UniversityTehranIran

signal decomposition into the frequency components is one of the oldest challenges in the digital signal *** early nineteenth century,Fourier transform(FT)showed that any applicable signal can be decomposed by unlimited ***,the relationship between time and frequency is lost under using *** to many researches for appropriate time-frequency representation,in early twentieth century,wavelet transform(WT)was *** is a well-known method which developed in order to decompose a signal into frequency *** contrast with original WT which is not adaptive according to the input signal,empirical wavelet transform(EWT)was *** this paper,the performance of discrete WT(DWT)and EWT in terms of signal decomposing into basic components are *** this purpose,a stationary signal including five sinusoids and ECG as biomedical and nonstationary signal are *** to being non-adaptive,DWT may remove signal components but EWT because of being adaptive is *** can also extract the baseline of ECG signal easier than DWT.

关键词： Empirical wavelet transform Discrete wavelet transform signal decomposition

Wavelet Neural Network Applied to Power Disturbance signal in Distributed Power System

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Wavelet Neural Network Applied to Power Disturbance Signal i...