Wenyi Liu

Wenyi Liu
Jiangsu Normal University · School of Mechatronic Engineering

Ph.D

About

44
Publications
7,558
Reads
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1,410
Citations
Introduction
Wenyi Liu received the Ph.D. degree in Mechatronic Engineering in 2010 from Chongqing University. He joined the School of Mechatronic Engineering, Jiangsu Normal University as an Associate professor. He is a visiting researcher in Case Western Reserve University between Jan 2013 and Jan 2014. He is also a Member of CSVE, Senior Member of CMES. His research interests are in the areas of condition monitoring and fault detection of wind turbines.
Additional affiliations
January 2013 - January 2014
Case Western Reserve University
Position
  • Visiting Researcher
January 2011 - present
Jiangsu Normal University
Position
  • Researcher
January 2011 - August 2016
Jiangsu Normal University
Position
  • Researcher

Publications

Publications (44)
Article
A typical 1.5 MW wind turbine suitable for Xuzhou City is designed and simulated in this paper. The wind turbine blade-hub-tower coupling system and most of the parameters are designed and calculated in the design process. In the kinetic analysis process, the force analysis under 4 different situations are taken to verify the structure design, whic...
Article
This paper proposed a novel wind turbine ball bearing fault diagnosis method based on Integral Extension Local Mean Decomposition (IELMD). Wind turbine vibration signal has the characteristic of non-Gaussian and non-stationary. Some typical time-frequency analysis methods cannot achieve ideal effects. A new method named LMD can deal with non-statio...
Article
Full-text available
With the rise of artificial intelligence, deep learning methods are more and more widely used in the field of intelligent fault diagnosis. However, the actual deep model used in fault diagnosis often exhibits over-fitting or under-fitting. In addition, the training process of these models requires configuration of a large number of hyper-parameters...
Article
Full-text available
This paper describes the development of a fault diagnosis method for identifying different fault conditions in the rolling bearings and gears of wind turbines. For the fault signal, the compressed sensing (CS) technology is used to perform noise reduction and feature extraction. The noise reduction process consists of sparse compression and reconst...
Article
Aimed at the difficulty in fault diagnosis of wind turbine transmission system under variable working conditions, the paper proposes a novel health condition monitoring method based on correlative features domain adaptation. Firstly, the envelope analysis of the collected signals is carried out, and the time–frequency features of the signals are ex...
Article
Aimed at the problem that the complicated working condition of wind turbine and the lack of sufficient target samples, which makes it difficult to conduct effective health condition monitoring (HCM), a novel method based on composite variational mode entropy (CVME) and weighted distribution adaptation (WDA) is proposed in this paper. A series of mo...
Article
Aimed at the difficulty of diagnosing the transmission system of wind turbine under variable working conditions, a novel health condition monitoring method based on common features distribution adaptation is proposed in this article. In the method, envelope analysis is first performed on the collected signals, and then the time‐frequency features a...
Article
Aimed at the problem that the signal data of wind turbine faulty gearbox is difficult to obtain and the health condition is difficult to diagnose under variable working conditions, a fault diagnosis method based on variational mode decomposition (VMD) multi-scale permutation entropy (MPE) and feature-based transfer learning (FTL) is proposed. Accor...
Article
Aimed at identifying the health state of wind turbines accurately by comprehensively using the change information in spatial and temporal scale of the supervisory control and data acquisition (SCADA) data, a novel condition monitoring method of wind turbines based on spatio-temporal features fusion of SCADA data by convolutional neural networks (CN...
Article
Purpose In this paper, a 1.5-MW wind turbine design process is proposed. Method A hybrid transmission type with single planetary gear connected to two-stage parallel shaft cylindrical gear is designed and some main relevant parameters are calculated. The contact and bending fatigue strength of the sun gear and planetary gear are checked separately...
Article
As a clean non-pollution and renewable energy producer, the healthy running of wind turbines can directly influence the electric power output of the wind farm. In order to maintain the safety running of the wind turbines, intelligent fault diagnosis and healthy condition monitoring techniques have been introduced to decrease the running costs and t...
Article
Aimed at the non-stationary and nonlinear characteristics of wind turbine vibration signals, a novel fault diagnosis method based on integral extension load mean decomposition multiscale entropy and least squares support vector machine was proposed in this paper. At first, the raw vibration signals monitored from the wind turbine were divided into...
Article
A novel wind turbine weak feature extraction method based on Cross Genetic Algorithm (CGA) optimal Mexican-Hat Wavelet (MHW) is proposed in this paper. At first, the optimal MHW is improved by adding 2 parameters to change the mother wavelet waveform. Then the CGA is introduced to optimize the suitable parameters in mother waveform establishment an...
Article
Aiming at reducing the end effect of Local Mean Decomposition(LMD) in feature extraction and overcoming the disadvantages of slow convergence and over learning of Artificial Neural Network(ANN) in pattern recognition, a bearing fault diagnosis method was proposed based on the integral waveform extension LMD and the Support Vector Machine(SVM). The...
Article
This paper proposed a novel integral extension local mean decomposition (IELMD) method, which can be widely applied in wind turbine fault diagnosis. Firstly, the characteristic waveform and its corresponding integral values are calculated. Then, the similar waveform is established according to signal extreme points and their integral values. The si...
Article
In this paper, a new Chinese character recognition (CCR) approach is proposed based on the fuzzy clustering analysis theory. Chinese characters (CCs) have various similar radicals and stroke components, which make it difficult to recognize features in the CCR process. At the same time, the recognition accuracy and the efficiency are lower when the...
Article
A new bearing fault diagnosis method based on auto term window (ATW) method is proposed in this paper. Ball bearing is the foremost important and also much easier to be damaged component in the rotation machinery. Vibration signature analysis of machine components is a commonly fault detect technique employed in ball bearing systems. The new fault...
Article
In this paper, the parameter selection in Auto term window (ATW) method is discussed and analyzed in the theoretical and experimental analysis. ATW method can suppress cross terms in Wigner-Ville distribution (WVD) effectively and applied in rotary machinery feature extraction and fault diagnosis. According to the characteristic that the auto terms...
Article
Gear is one of the popular and important components in the rotary machinery transmission. Vibration monitoring is the common way to take gear feature extraction and fault diagnosis. The gear vibration signal collected in the running time often reflects the characteristics such as non-Gaussian and nonlinear, which is difficult in time domain or freq...
Article
A new de-noising method based on parameter optimized Mexican hat wavelet was put forward in this paper. For the similar shape to the mechanical shock vibration signal, the Mexican hat wavelet is chosen as the mother wavelet and improved by the shape parameters optimization. The noise jamming in the raw vibration signals can be filtered by the conti...
Article
A rolling element bearing fault recognition approach is proposed in this paper. This method combines the basic Higher-order spectrum (HOS) theory and fuzzy clustering method in data mining area. In the first step, all the bispectrum estimation results of the training samples and test samples are turned into binary feature images. Secondly, the bina...
Article
A fuzzy clustering bispectrum estimation approach is proposed in this paper and applied on the rolling element bearing fault recognition. The method combines the basic higher order spectrum theory and fuzzy clustering technique in data mining. At first, all the bispectrum estimation results of the training samples and test samples are taken binariz...
Article
Aimed at the analysis of wind turbine development status in 2010, this paper discussed the wind turbine capacity status and manufacturers in China. Through the detail analysis in the wind turbine development status, some problems have been discussed such as planning imbalance between country and local governments, coordinate between wind energy and...
Article
Analyzing the vibration signals of wind turbine usually requires feature extraction. However, in many cases, to extract feature components becomes challenging and the applicability of information drops down due to the large amount of noise. In this paper, a new denoising method based on adaptive Morlet wavelet and singular value decomposition (SVD)...
Article
In this paper, a hybrid time-frequency method (HTM) based on the improved Morlet wavelet and auto terms window (ATW) is presented. The Morlet wavelet, for its shape is similar to the mechanical shock signals, is added two parameters which decide the shape of the mother wavelet. The added parameters and the appropriate scale parameter for continuous...
Article
Based on the Morlet wavelet transformation and Wigner-Ville distribution (WVD), we present a wind turbine fault diagnosis method in this paper. Wind turbine can be damaged by moisture absorption, fatigue, wind gusts or lightening strikes. Due to this reason, there is an increasing need to monitor the health of these structures. Vibration analysis i...
Article
A new fault diagnosis model is proposed based on Multi-Class Least Square Support Vector Machine optimized hierarchically by Genetic Algorithm(GA). Original vibration signals are decomposed into several stationary IMFs. Then the instantaneous amplitude energy of the IMFs with fault modulation characteristics is computed and regarded as the input ch...
Article
A new hybrid time-frequency method was put forward based on the optimal Morlet wavelet and auto terms window. The Morlet wavelet, with a shape similar to the mechanical shock signals, was improved by adding two parameters to determine the shape of the mother wavelet. The added parameters and the appropriate scale parameter for the continuous wavele...
Article
Wind energy is an important renewable energy source because of its reliability due to the maturity of the technology, good infrastructure and relative cost competitiveness. Rich wind resources and strong support in regulations by the Chinese government have enabled the wind power industry to grow at a fast speed and the primary market scale has bee...
Article
According to the fact that the defects of mechanical components such as rolling bearing or gearbox can excites vibration with specific impact component, a feature extraction method based on parameter optimized Morlet wavelet is proposed. Firstly, minimum Shannon entropy is used to optimize the Morlet wavelet shape factor in order to match with the...
Article
A new de-noising method based on parameter optimized Morlet wavelet is put forward. The Morlet wavelet is chosen as the mother wavelet because its shape is similar to the mechanical shock signals. The mother Morlet wavelet is improved by adding two parameters which decide the shape of the mother wavelet in time domain. The added parameters and the...
Article
Based on the relationship among the auto terms and the cross terms of WVD, a new method for time-frequency representation(TFR) of a signal was proposed, which used the SPWVD spectrum as the auto terms to suppress the cross terms. Firstly, the SPWVD of the signals to be analyzed was calculated. Secondly, the SPWVD spectrum was selected to replace th...

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Projects

Projects (3)
Project
Research on the wind farm healthy condition monitoring methods under critical conditions
Project
Research on the wind turbine transmission system healthy condition monitoring methods under the variable condition
Project
Micro-feature Healthy Condition Recognition Method of Wind turbine Transmission Chain System based on Multi-dimension Kernel Domain Spectrum