Lijun Wang
North China University of Water Resources and Electric Power

Published : 3 Documents
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Motor Fault Diagnosis Based on Wavelet Transform Wang, Lijun; Guo, Huijuan; Zhang, Shenfeng
TELKOMNIKA Indonesian Journal of Electrical Engineering Vol 11, No 9: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/telkomnika.v11i9.3288

Abstract

The wavelet transform theory is used to motor fault diagnosis in this paper, considering its characteristics of multi-resolution and stronger feature extraction ability than Fourier. The paper emphasizes de-noising and eliminating the singular value point of the wavelet transform in the non-stationary signal. And it makes a detailed and in-depth analysis about how to detect the frequency components of weak signal by using equivalent power spectrum of reconstruction signal, which is acquired by using the wavelet transform. Through the comparison analysis of the simulation signal and motor vibration signal’s experimental data, the corresponding energy of original signal’s equivalent power spectrum and reconstructing signal’s equivalent power spectrum are compared to determine the fault frequency, so as to accurately find out the motor fault.  
Model Construction and Simulation of Weighted Industrial Cluster Complex Network Wang, Lijun; Wen, Lei
TELKOMNIKA Indonesian Journal of Electrical Engineering Vol 12, No 10: October 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/telkomnika.v12i10.5185

Abstract

In this paper, we construct a weighted industrial cluster complex network model according to preferential attachment and local world mechanism, in which the logistics amount of business contact between one enterprise and others is defined as edge weight. We simulate and analyze statistic characteristics of the industrial cluster model such as degree distribution, point strength, point strength-point strength correlation, and relationship with degree and point strength. The simulation results show that the degree and point strength distribution are accord with power law distribution, the point strength-point strength correlation is negative, and the relationship with degree and point strength is strongly linear.
Optimization of Hydrogen-fueled Engine Ignition Timing Based on L-M Neural Network Algorithm Wang, Lijun; Liu, Yuan; Liu, Yahui; Wang, Wei; Zhao, Yanan; Yang, Zhenzhong
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (414.746 KB) | DOI: 10.12928/telkomnika.v14i3.2756

Abstract

In view of the improvement measures of the optimization control algorithm for the ignition system of the hydrogen-fueled engine, the L-M neural network algorithm, Powell neural network algorithm and the traditional BP neural network algorithm are used to optimize the ignition system. The results showed that L-M algorithm not only can accurately predict the hydrogen-fueled engine ignition timing, but also has high precision, high convergence speed, a simple model and other outstanding advantages in the training process, which can greatly reduce the workload of human engine bench tests. Only a small amount of engine bench test is carried out, and the obtained sample data can be used to predict the ignition timing under the whole working conditions. The mean square error of the optimization results based on L-M algorithm arrives at 0.0028 after 100 times of calculation, the maximum value of absolute error arrives at 0.2454, and the minimum value of absolute error arrives at 0.00426.