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Weak Fault Feature Extraction of the Rotating Machinery Using Flexible Analytic Wavelet Transform and Nonlinear Quantum Permutation Entropy

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Computers, Materials & Continua 2024年第6期79卷 4513-4531页

作者： Lili Bai Wenhui Li He Ren Feng Li TaoYan Lirong Chen College of Aeronautics and Astronautics Taiyuan University of TechnologyTaiyuan030024China Commercial Aircraft corporation of China Ltd.Shanghai200126China College of Physics and Electronic Engineering State Key Laboratory of Quantum Optics and Quantum Optics DevicesShanxi UniversityTaiyuan030006China

Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery,where weak fault characteristic signals hinder accurate fault state representation,we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform(FAWT)with Nonlinear Quantum Permutation Entropy.FAWT,leveraging fractional orders and arbitrary scaling and translation factors,exhibits superior translational invariance and adjustable fundamental oscillatory characteristics.This flexibility enables FAWT to provide well-suited wavelet shapes,effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults.In our approach,gearbox vibration signals undergo FAWT to obtain sub-bands.Quantum theory is then introduced into permutation entropy to propose Nonlinear Quantum Permutation Entropy,a feature that more accurately characterizes the operational state of vibration simulation signals.The nonlinear quantum permutation entropy extracted from sub-bands is utilized to characterize the operating state of rotating machinery.A comprehensive analysis of vibration signals from rolling bearings and gearboxes validates the feasibility of the proposed method.Comparative assessments with parameters derived from traditional permutation entropy,sample entropy,wavelet transform(WT),and empirical mode decomposition(emd)underscore the superior effectiveness of this approach in fault detection and classification for rotating machinery.

关键词： Rotating machinery quantum theory nonlinear quantum permutation entropy Flexible Analytic Wavelet Transform(FAWT) feature extraction

Research on motor rotation anomaly detection based on improved VMD algorithm

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Railway Sciences 2024年第1期3卷 18-31页

作者： Fuzhao Chen Zhilei Chen Qian Chen Tianyang Gao Mingyan Dai Xiang Zhang Lin Sun China Academy of Railway Sciences corporation Limited Locomotive and Car Research InstituteBeijingChina Brake Development Department Beijing Zongheng Electro-Mechanical Technology Co.Ltd.BeijingChina Process Management Department Beijing Zongheng Electro-Mechanical Technology Co.Ltd.BeijingChina

Purpose–The electromechanical brake system is leading the latest development trend in railway braking technology.The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder.The tolerance leads to imprecise brake control,so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system.This paper aims to present improved variational mode decomposition(VMD)algorithm,which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.Design/methodology/approach–The VMD algorithm plays a pivotal role in the preliminary phase,employing mode decomposition techniques to decompose the motor speed signals.Afterward,the error energy algorithm precision is utilized to extract abnormal features,leveraging the practical intrinsic mode functions,eliminating extraneous noise and enhancing the signal’s fidelity.This refined signal then becomes the basis for fault analysis.In the analytical step,the cepstrum is employed to calculate the formant and envelope of the reconstructed signal.By scrutinizing the formant and envelope,the fault point within the electromechanical brake system is precisely identified,contributing to a sophisticated and accurate fault diagnosis.Findings–This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake(EMB)motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction.The signal is reconstructed according to the effective intrinsic mode functions(IMFS)component of removing noise,and the formant and envelope are calculated by cepstrum to locate the fault point.Experiments show that the empirical mode decomposition(emd)algorithm can effectively decompose the original speed signal.After feature extraction,signal enhancement and fault identification,the mo

关键词： Electromechanical brake system Railway brake system Motor fault diagnosis Variational mode decomposition Error energy Feature extraction

Unsupervised Video Object Segmentation via Weak User Interaction and Temporal Modulation

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Chinese Journal of Electronics 2023年第3期32卷 507-518页

作者： FAN Jiaqing ZHANG Kaihua ZHAO Yaqian LIU Qingshan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics College of Computer and Software Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Inspur Suzhou Intelligent Technology corporation

In unsupervised video object segmentation(UVOS), the whole video might segment the wrong target due to the lack of initial prior information. Also, in semi-supervised video object segmentation(SVOS), the initial video frame with a fine-grained pixel-level mask is essential to good segmentation accuracy. It is expensive and laborious to provide the accurate pixel-level masks for each training sequence. To address this issue, We present a weak user interactive UVOS approach guided by a simple human-made rectangle annotation in the initial frame. We first interactively draw the region of interest by a rectangle, and then we leverage the mask RCNN(region-based convolutional neural networks) method to generate a set of coarse reference labels for subsequent mask propagations. To establish the temporal correspondence between the coherent frames, we further design two novel temporal modulation modules to enhance the target representations. We compute the earth mover’s distance(emd)-based similarity between coherent frames to mine the co-occurrent objects in the two images, which is used to modulate the target representation to highlight the foreground target. We design a cross-squeeze temporal modulation module to emphasize the co-occurrent features across frames, which further helps to enhance the foreground target representation. We augment the temporally modulated representations with the original representation and obtain the compositive spatio-temporal information, producing a more accurate video object segmentation(VOS) model. The experimental results on both UVOS and SVOS datasets including Davis2016,FBMS, Youtube-VOS, and Davis2017, show that our method yields favorable accuracy and complexity. The related code is available.

关键词： Unsupervised video object segmentation The earth mover’s distance (emd)-based modulation Cross-squeeze modulation Weak interaction Region-based convolutional neural networks (RCNN)

Study of Denoising in the Electricity Anti-Stealing Detection Based on VMD-WTD Combination

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Energy Engineering 2022年第4期119卷 1453-1466页

作者： Huakun Que Guolong Lin Wenchong Guo Xiaofeng Feng Zetao Jiang Yunfei Cao Jinmin Fan Zhixian Ni Metrology Center of Guangdong Power Grid corporation Shenzhen518049China School of Automation Guangdong University of TechnologyGuangzhou510006China China-EU School of Clean and Renewable Energy Huazhong University of Science and TechnologyWuhan430074China

In order to solve the failure of electricity anti-stealing detection device triggered by the noise mixed in high-frequency electricity stealing signals,a denoising method based on variational mode decomposition(VMD)and wavelet threshold denoising(WTD)was applied to extract the effective high-frequency electricity stealing signals.First,the signal polluted by noise was pre-decomposed using the VMD algorithm,the instantaneous frequency means of each pre-decomposed components was analyzed,so as to determine the optimal K value.The optimal K value was used to decompose the polluted signal into K intrinsic mode components,and the sensitive mode components were determined through the cross-correlation function.Next,each sensitive mode was reconstructed.Finally,the reconstructed signal denoised using the wavelet threshold to obtain the denoised signal.The simulation analysis and experimental results show that the proposed method is superior to the traditional VMD method,FFT method and emd method,as it can effectively eliminate the noise and enhance the reliability of high-frequency electricity stealing signal detection.

关键词： Electricity stealing electromagnetic interference variable mode wavelet threshold instantaneous frequency mean

Non-overshooting and Non-undershooting Cubic Spline Interpolation for Empirical Mode Decomposition

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Journal of Beijing Institute of Technology 2008年第3期17卷 316-321页

作者：袁晔梅文博吴嗣亮袁起 School of Information Science and Technology Beijing Institute of Technology The Scienceand Technology Committee of China Aerospace Mechanical and Electric corporation

To suppress the overshoots and undershoots in the envelope fitting for empirical mode decomposition （emd）, an alternative cubic spline interpolation method without overshooting and undershooting is proposed. On the basis of the derived slope constraints of knots of a non-overshooting and non-undershooting cubic interpolant, together with ＂not-a-knot＂ conditions the cubic spline interpolants are constructed by replacing the requirement for equal second order derivatives at every knot with Brodlie＇ s derivative formula. Analysis and simulation experiments show that this approach can effectively avoid generating new extrema, shifting or exaggerating the existing ones in a signal, and thus significantly improve the decomposition performance of emd.

关键词： overshooting and undershooting cubic spline interpolation empirical mode decomposition

Performances of lithium manganese oxide prepared by hydrothermal process

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Journal of Central South University 2014年第4期21卷 1279-1284页

作者：孔龙李运姣张鹏黄海花叶万奇李春霞 School of Metallurgy and Environment Central South University Citic Dameng Mining Industries Limited Changsha Research Institute of Mining and Metallurgy corporation Limited

A simple hydrothermal process followed by heat treatment was applied to the preparation of spinel Li1.05Mn1.95O4. In this process, electrolytic manganese dioxide(emd) and LiOH·H2O were used as starting materials. The physiochemical properties of the synthesized samples were investigated by thermogravimetry-differential scanning calorimetry(TG-DSC), X-ray diffractometry(XRD), and scanning electronic microscopy(SEM). The results show that the hydrothermally synthesized precursor is an essential amorphous. The precursor can be easily transferred to spinel powders with a homogeneous structure and a regularly-shaped morphology by heat treatment. Li1.05Mn1.95O4 powder obtained by heat treating the precursor at 430 °C for 12 h and then calcining at 800 °C for 12 h shows an excellent cycling performance with an initial charge capacity of 118.2 mA·h·g-1 obtained at 0.5C rate and 93.8% of its original value retained after 100 cycles.

关键词：水热法锰酸锂差示扫描量热法扫描电子显微镜表演制备电解二氧化锰 TG-DSC

Identifying the Correlation Between Ambient Temperature and Gas Consumption in a Local Energy System

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CSEE Journal of Power and Energy Systems 2018年第4期4卷 479-486页

作者： Hantao Wang Chenghong Gu Xin Zhang Furong Li Lihong Gu Department of Electronic and Electrical Eng. University of BathBathUK National Grid UK East Branch of State Grid corporation Shanghai China

In order to understand energy consumption and ensure precise load prediction,it is essential to identify the variation of gas consumption in response to ambient temperature change outdoor.In this paper,the relationship is identified by using Empirical Mode Decomposition(emd)and linear regression analysis together with outlier detection.emd is a data processing tool that can divide original data into several Intrinsic Mode Functions(IMFs)with a lower frequency residue.By applying the data mining technique-Mahalanobis distance measurement,some outliers from real-time gas consumption and temperature data points are detected,which are excluded from the data sets to ensure accuracy.Correlation coefficients between the gas load and ambient temperature are calculated and denoted as an important index to quantify their relationship through regression analysis.By comparing such indices on realtime data and emd processed data,the weather-sensitive part of gas demand is identified.The methods are implemented on a local energy system and the results reveal that the outcome after emd presents a higher level of correlation between the gas load and ambient temperature,compared to the results from directly using the real-time gas load and temperature data.

关键词： Correlation coefficient emd linear regression load consumption outlier detection weather effect

Model predictive control and improved low-pass filtering strategies based on wind power fluctuation mitigation

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Journal of Modern Power Systems and Clean Energy 2019年第3期7卷 512-524页

作者： Yushu SUN Xisheng TANG Xiaozhe SUN Dongqiang JIA Zhihuang CAO Jing PAN Bin XU Institute of Electrical Engineering Chinese Academy of SciencesBeijing 100190China Sinopec Oilfield Service Shengli corporation Tarim Branch Company Korla 841000China State Grid Beijing Electric Power Company Electric Power Research Institute Beijing 100075China State Grid Anhui Electric Power Company Hefei 230000China

The rapid development of renewable energy sources such as wind power has brought great challenges to the power grid. Wind power penetration can be improved by using hybrid energy storage(ES) to mitigate wind power fluctuation. We studied the strategy of smoothing wind power fluctuation and the strategy of hybrid ES power distribution. Firstly, an effective control strategy can be extracted by comparing constant-time low-pass filtering(CLF), variable-time low-pass filtering(VLF), wavelet packet decomposition(WPD), empirical mode decomposition(emd) and model predictive control algorithms with fluctuation rate constraints of the identical grid-connected wind power. Moreover, the mean frequency of ES as the cutoff frequency can be acquired by the Hilbert Huang transform(HHT), and the time constant of filtering algorithm can be obtained. Then, an improved low-pass filtering algorithm(ILFA) is proposed to achieve the power allocation between lithium battery(LB) and supercapacitor(SC), which can overcome the over-charge and over-discharge of ES in the traditional low-pass filtering algorithm(TLFA). In addition, the optimized LB and SC power are further obtained based on the SC priority control strategy combined with the fuzzy control(FC) method. Finally, simulation results show that wind power fluctuation can be effectively suppressed by LB and SC based on the proposed control strategies, which is beneficial to the development of wind and storage system.

关键词： Wind power fluctuation mitigation Control strategies Model predictive control algorithm Hybrid energy storage(ES) Improved low-pass filtering algorithm(ILFA) Fuzzy control(FC) Strategy

Empirical mode decomposition using variable filtering with time scale calibrating

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Journal of Systems Engineering and Electronics 2008年第6期19卷 1076-1081页

作者： Yuan Ye Mei Wenbo Wu Siliang Yuan Qi School of Information Science and Technology Beijing Inst. of Technology Beijing 100081 P. R. China School of Industrial Design and Information Engineering Beijing Inst. of Clothing Technology Beijing 100029 P. R. China The Science and Technology Committee of China Aerospace Science and Industry corporation Beijing 100854 P. R. China

A novel and efficient method for decomposing a signal into a set of intrinsic mode functions （IMFs） and a trend is proposed. Unlike the original empirical mode decomposition （emd）, which uses spline fits to extract variations from the signal by separating the local mean from the fluctuations in the decomposing process, this new method being proposed takes advantage of the theory of variable finite impulse response （FIR） filtering where filter coefficients and breakpoint frequencies can be adjusted to track any peak-to-peak time scale changes. The IMFs are results of a multiple variable frequency response FIR filtering when signals pass through the filters. Numerical examples validate that in contrast with the original emd, the proposed method can fine-tune the frequency resolution and suppress the aliasing effectively.

关键词： empirical mode decomposition variable FIR filtering time scale calibrating.