|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | PARAMETERS OPTIMIZATION OF CONTINUOUS WAVELET TRANSFORM AND ITS APPLICATION IN ACOUSTIC EMISSION SIGNAL ANALYSIS OF ROLLING BEARING显示文摘Morlet wavelet is suitable to extract the impulse components of mechanical fault signals. And thus its continuous wavelet transform (CWT) has been successfully used in the field of fault diagnosis. The principle of scale selection in CWT is discussed. Based on genetic algorithm,an opti-mization strategy for the waveform parameters of the mother wavelet is proposed with wavelet en-tropy as the optimization target. Based on the optimized waveform parameters,the wavelet scalogram is used to analyze the simulated acoustic emission (AE) signal and real AE signal of rolling bearing. The results indicate that the proposed method is useful and efficient to improve the quality of CWT. | ZHANG Xinming HE Yongyong HAO Rujiang CHU Fulei | 2007 | Chinese Journal of Mechanical Engineering2007，20，2： | 7 |
| 2 | Adaptive TQWT filter based feature extraction method and its application to detection of repetitive transients显示文摘The local defect in rotating machine always gives rise to repetitive transients in the collected vibration signal. However, the transient signature is prone to be contaminated by strong background noises, thus it is a challenging task to detect the weak transients for machine fault diagnosis. In this paper, a novel adaptive tunable Q-factor wavelet transform(TQWT) filter based feature extraction method is proposed to detect repetitive transients. The emerging TQWT possesses distinct advantages over the classical constant-Q wavelet transforms, whose Q-factor can be tuned to match the oscillatory behavior of different signals, but the parameter selection for TQWT heavily relies on prior knowledge. Within our adaptive TQWT filter algorithm, the automatic optimization techniques for three TQWT parameters are implemented to achieve an optimal TQWT basis that matches the transient components. Specifically, the decomposition level is selected according to a center frequency ratio based stopping criterion, and the Q-factor and redundancy are optimized based on the minimum energy-weighted normalized wavelet entropy.Then, the adaptive TQWT decomposition can be achieved in a sparse way and result in subband signals at various wavelet scales.Further, the optimum subband signal which carries transient feature information, is identified using a normalized energy to bandwidth ratio index. Finally, the single branch reconstruction signal from the optimum subband is obtained with transient signatures via inverse TQWT, and the frequency of repetitive transients is detected using Hilbert envelope demodulation. It has been verified via numerical simulation that the proposed adaptive TQWT filter based feature extraction method can adaptively select TQWT parameters and the optimum subband for repetitive transient detection without prior knowledge. The proposed method is also applied to faulty bearing vibration signals and its effectiveness is validated. | KONG Yun WANG TianYang CHU FuLei | 2018 | Science China(Technological Sciences)2018，61，10： | 5 |
| 3 | Deep Spatiotemporal Convolutional-Neural-Network-Based Remaining Useful Life Estimation of Bearings显示文摘The remaining useful life(RUL)estimation of bearings is critical for ensuring the reliability of mechanical systems.Owing to the rapid development of deep learning methods,a multitude of data-driven RUL estimation approaches have been proposed recently.However,the following problems remain in existing methods:1)Most network models use raw data or statistical features as input,which renders it difficult to extract complex fault-related information hidden in signals;2)for current observations,the dependence between current states is emphasized,but their complex dependence on previous states is often disregarded;3)the output of neural networks is directly used as the estimated RUL in most studies,resulting in extremely volatile prediction results that lack robustness.Hence,a novel prognostics approach is proposed based on a time-frequency representation(TFR)subsequence,three-dimensional convolutional neural network(3DCNN),and Gaussian process regression(GPR).The approach primarily comprises two aspects:construction of a health indicator(HI)using the TFR-subsequence-3DCNN model,and RUL estimation based on the GPR model.The raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping strategy.Subsequently,the 3DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct HIs.Finally,the RUL of the bearings is estimated using the GPR model,which can also define the probability distribution of the potential function and prediction confidence.Experiments on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence-3DCNN-GPR approach.The use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification. | Xu Wang Tianyang Wang Anbo Ming Qinkai Han Fulei Chu Wei Zhang Aihua Li | 2021 | Chinese Journal of Mechanical Engineering2021，34，3： | 5 |
| 4 | Nonlinear dynamic responses of sandwich functionally graded porous cylindrical shells embedded in elastic media under 1:1 internal resonance显示文摘In this article, the nonlinear dynamic responses of sandwich functionally graded(FG) porous cylindrical shell embedded in elastic media are investigated. The shell studied here consists of three layers, of which the outer and inner skins are made of solid metal, while the core is FG porous metal foam. Partial differential equations are derived by utilizing the improved Donnell's nonlinear shell theory and Hamilton's principle. Afterwards, the Galerkin method is used to transform the governing equations into nonlinear ordinary differential equations, and an approximate analytical solution is obtained by using the multiple scales method. The effects of various system parameters,specifically, the radial load, core thickness, foam type, foam coefficient, structure damping,and Winkler-Pasternak foundation parameters on nonlinear internal resonance of the sandwich FG porous thin shells are evaluated. | Yunfei LIU Zhaoye QINT Fulei CHU | 2021 | Applied Mathematics and Mechanics(English Edition)2021，42，6： | 4 |
| 5 | Power fluctuation and power loss of wind turbines due to wind shear and tower shadow显示文摘 | Binrong WEN Sha WEI Kexiang WEI Wenxian YANG Zhike PENG Fulei CHU | 2017 | Frontiers of Mechanical Engineering2017，12，3： | 4 |
| 6 | Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum显示文摘 | Yun KONG Tianyang WANG Zheng LI Fulei CHU | 2017 | Frontiers of Mechanical Engineering2017，12，3： | 3 |
| 7 | Frequency Demodulation Analysis Method for Fault Diagnosis of Planetary Gearboxes显示文摘 | FENG Zhipeng CHU Fulei | 2013 | 中国电机工程学报2013，33，11： | 3 |
| 8 | Morphological undecimated wavelet decomposition for fault diagnostics of rolling element bearings显示文摘 | Rujiang Hao Fulei Chu | 2008 | Journal of Sound and Vibration2008，，4： | 2 |
| 9 | DIAGNOSTICS OF FATIGUE CRACK IN ULTERIOR PLACES OF LARGER-SCALE OVERLOADED SUPPORTING SHAFT BASED ON TIME SERIES AND NEURAL NETWORKS显示文摘To improve the diagnosis accuracy and self-adaptability of fatigue crack in ulterior place of the supporting shaft, time series and neural network are attempted to be applied in research on diag-nosing the fatigue crack’s degree based on analyzing the vibration characteristics of the supporting shaft. By analyzing the characteristic parameter which is easy to be detected from the supporting shaft’s exterior, the time series model parameter which is hypersensitive to the situation of fatigue crack in ulterior place of the supporting shaft is the target input of neural network, and the fatigue crack’s degree value of supporting shaft is the output. The BP network model can be built and net-work can be trained after the structural parameters of network are selected. Furthermore, choosing the other two different group data can test the network. The test result will verify the validity of the BP network model. The result of experiment shows that the method of time series and neural network are effective to diagnose the occurrence and the development of the fatigue crack’s degree in ulterior place of the supporting shaft. | LI Xueiun BIN Guangfu CHU Fulei | 2007 | Chinese Journal of Mechanical Engineering2007，20，3： | 2 |
| 10 | Fault recognition method for speed - up and speed - down process of rotating machinery based an independent component analysis and Factorial Hidden Markov Model显示文摘 | Zhinong Li Yongyong He Fulei Chu Jie Han Wei Ham | 2006 | Journal of Sound and Vibration2006，291，： | 1 |
| 11 | Fault Diagnosis Based on Support Vector Machines with Parameter Optimization by Artificial Immunization Algorithm显示文摘 | Shengfa Yuan Fulei Chu | 2007 | Mechanical Systems and Signal Processing2007，21，： | 1 |
| 12 | The mechanism theory and application of deployable structures based on SLE显示文摘 | ZHAO Jingshan CHU Fulei FENG Zhijing | 2009 | Mechanism and Machine Theory2009，44，2： | 1 |
| 13 | The dynamic behavior of a rotor system with a slant crack on the shaft显示文摘 | Yanli Lin Fulei Chu | 2009 | Mechanical Systems and Signal Processing2009，，2： | 1 |
| 14 | The dynamic behavior of a rotor system with a slant crack on the shaft显示文摘 | Lin Yanli Chu Fulei | 2010 | Mechanical Systems and Signal Processing2010，，24： | 1 |
| 15 | Hidden Markov model-based fault diagnostics method in speed-up and speed-down process for rotating machinery显示文摘 | Zhinong Li Zhaotong Wu Yongyong He Fulei Chu | 2005 | Mechanical Systems and Signal Processing2005，19，2： | 1 |
| 16 | Support vector machines based fault diagnosis for turbo-pump rotor显示文摘 | Yuan Shengfa Chu Fulei | 2006 | Mechanical Systems and Signal Processing2006，20，： | 1 |
| 17 | Morphological undecimated wavelet decomposition for fault diagnostics of rolling element bearings 显示文摘 | Hao Rujiang Chu Fulei | 2009 | Journal of Sound and Vibration2009，320，45： | 1 |
| 18 | Parametric instability of a ro-tor-hearing system with two breathing transversecracks 显示文摘 | Han Qinkai Chu Fulei | 2012 | European Journal of Mechanics-A Solids2012，36，： | 1 |
| 19 | Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography显示文摘 | PENG Zhike CHU Fulei | 2004 | Mechanical Systems and Signal Processing2004，18，2： | 1 |
| 20 | Simulation of rotor's ax- ial rub-impact in full degrees of freedom 显示文摘 | Yuan Zhenwei Chu Fulei Hao Rujiang | 2006 | Mechanism and Machine Theory2006，42，： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员