scholarly journals Gear Fault Detection Analysis Method Based on Fractional Wavelet Transform and Back Propagation Neural Network

2019 ◽  
Vol 121 (3) ◽  
pp. 1011-1028
Author(s):  
Yanqiang Sun ◽  
Hongfang Chen ◽  
Liang Tang ◽  
Shuang Zhang
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Analysis Method ◽  
Detection Analysis ◽  
Gear Fault ◽  
Fractional Wavelet Transform

scholarly journals Motor Fault Detection Using Wavelet Transform and Improved PSO-BP Neural Network

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1322 ◽  
Author(s):  
Chun-Yao Lee ◽  
Yi-Hsin Cheng
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Bp Neural Network ◽  
Short Circuit ◽  
Back Propagation ◽  
Operating Environment ◽  
Cause Of Failure ◽  
Bearing Damage ◽  
Improved Pso

This paper proposes a motor fault detection method based on wavelet transform (WT) and improved PSO-BP neural network which is combined with improved particle swarm optimization (PSO) and a back propagation (BP) neural network with linearly increasing inertia weight. First, this research used WT to analyze the current signals of the healthy motor, bearing damage motor, stator winding inter-turn short circuit motor, and broken rotor bar motor. Second, features after completing the signal analysis were extracted, and three types of classifiers were used to classify. The results show that the improved PSO-BP neural network can effectively detect the cause of failure. In addition, in order to simulate the actual operating environment of the motor, this study added white noise with signal noise ratios of 30 dB, 25 dB, and 20 dB to verify that this model has a better anti-noise ability.

Integrated Navigation of Mobile Robot Based on Neural Network and Fault Detection

2012 ◽  
Vol 433-440 ◽  
pp. 3175-3180
Author(s):  
Hong Mei Wang ◽  
Ming Lu Zhang ◽  
Guang Zhu Meng
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Global Positioning System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Integrated Navigation ◽  
Global Positioning ◽  
Navigation Accuracy

When global positioning system (GPS) signal outages, the integrated navigation accuracy of GPS and strap-down inertial navigation system (SINS) will decline with time, and even navigation system cannot work. To avoid this, a new design is introduced. When GPS works normally, square root filter estimates the errors of position, velocity and attitude and compensates the outputs of SINS. When GPS is out of order, back propagation neural network (BPNN) will take the place of GPS to calculate the error parameters, thus the accuracy of navigation will enhance. And in this paper, the unit of fault detection is added to detect whether GPS signal outages or not. The simulation results show the effectiveness of this method

Research of Defect Detection Method Noise for Bevel Gear

2014 ◽  
Vol 889-890 ◽  
pp. 722-725 ◽  
Author(s):  
Feng Yan Dai ◽  
Zhao Yao Shi ◽  
Jia Chun Lin
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Background Noise ◽  
Back Propagation ◽  
Noise Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Bevel Gear ◽  
Noise Characteristics ◽  
Useful Signal ◽  
Gear Fault

Noise signal analysis method is widely available for gearbox bevel gear fault detection. However, the noise from the gearbox is usually concealed by background noise, which leads to poor efficiency analysis. This paper reports an ensemble empirical mode decomposition (EEMD) and neural network method for bevel gear fault detection. To extract useful signal, EEMD algorithm was firstly applied to get rid of the background noise. Characteristics from a group of discriminating defect status were then chosen to build the eigenvector. Finally, the eigenvector was imported into a back propagation (BP) neural network classifier for defect diagnosis automatically. Experimental results show that the proposed approach is capable for signal denoising and providing distinguishing characteristics of founded fault. The developed method is an accurate approach to detect fault for tested bevel gear.

Transmission Line Using Discrete Wavelet Transform and Back-Propagation Neural Network Based on Clarke’s Transformation

2016 ◽  
Vol 818 ◽  
pp. 156-165 ◽  
Author(s):  
Makmur Saini ◽  
Abdullah Asuhaimi bin Mohd Zin ◽  
Mohd Wazir Bin Mustafa ◽  
Ahmad Rizal Sultan ◽  
Rahimuddin
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Comparison Of The Results

This paper proposes a new technique of using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation for fault classification and detection on a single circuit transmission line. Simulation and training process for the neural network are done by using PSCAD / EMTDC and MATLAB. Daubechies4 mother wavelet (DB4) is used to decompose the high frequency components of these signals. The wavelet transform coefficients (WTC) and wavelet energy coefficients (WEC) for classification fault and detect patterns used as input for neural network training back-propagation (BPNN). This information is then fed into a neural network to classify the fault condition. A DWT with quasi optimal performance for preprocessing stage are presented. This study also includes a comparison of the results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using Clarke transformation in training will give in a smaller mean square error (MSE) and mean absolute error (MAE). The simulation also shows that the new algorithm is more reliable and accurate.

Sign in / Sign up

Export Citation Format

Share Document