scholarly journals State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA-BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
QingHui Song ◽  
QingJun Song ◽  
Linjing Xiao ◽  
HaiYan Jiang ◽  
LiNa Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Nondestructive Technique ◽  
Operation Conditions ◽  
Mode Decomposition ◽  
Before And After

Vibration analysis is considered as an effective and reliable nondestructive technique for monitoring the operation conditions of elevator control transformer. In the paper, a novel model using the Empirical Mode Decomposition (EMD), the empirical wavelet packet transform, the mind evolutionary algorithm (MEA), and the backpropagation (BP) neural network is proposed for elevator control transformer fault diagnosis. Firstly, the collected signal is smoothed by EMD, the intrinsic mode function (IMF) components with large noise are determined according to the correlation coefficient, the wavelet adaptive threshold denoising algorithm is used to process the noisy IMF components, and the IMF components before and after processing and its residual component are reconstructed to obtain the denoised signal. Then, the denoised signal is transformed by empirical wavelet packet transform to extract the energy ratio and energy entropy features in the wavelet packet coefficients. Finally, a fault diagnosis model composed of MEA and BP neural network is developed, which avoids the problems of premature convergence and poor diagnosis effect. The experimental results show that the proposed model has a remarkable performance with an average root mean square error of 0.00672 and the average diagnosis accuracy of 90.8%, which is better than classic BP neural network.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

Fault Diagnosis of Large Rotors Based on Wavelet Packet Neural Network

2014 ◽  
Vol 722 ◽  
pp. 363-366
Author(s):  
You Juan Zheng ◽  
Ping Liao ◽  
Cai Long Qin ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Test Bench ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Network Method ◽  
Spectrum Characteristic ◽  
Fault Information

Using wavelet packet neural network method which is consist of wavelet packet and BP neural network to diagnose large rotors by vibration signal .Firstly , according to the spectrum characteristic of large rotors’ common vibration fault ,using the improved wavelet packet method to compute the energy of the spectrum that can reflect the fault information .And then make the feature vector as the input to establish a model of improved wavelet packet neural network for fault diagnosis . Collect the data of five working conditions from the test bench , establish a improved wavelet packet neural network model, and then use the model to diagnose fault. The experimental results show that this method improves the accuracy obviously and calculate fast.

Application of Wavelet Packet Transform for Detection of Ball Bearing Race Fault

2009 ◽  
Vol 626-627 ◽  
pp. 511-516
Author(s):  
Dong Yun Wang ◽  
Wen Zhi Zhang ◽  
Wei Ping Lu ◽  
J.W. Du
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Ball Bearing ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Rolling Ball ◽  
Bearing Race ◽  
Artificial Neural

In this study, a fault diagnosis system is proposed for rolling ball bearing race using wavelet packet transform(WPT) and artificial neural network(ANN)technique. Vibration signal from ball bearings having defects on inner race and outer race is considered and the extraction method of feature vector based on wavelet packet transform with frequency band energy is used. The vibration signal is decomposed into the individual frequency bands. The variations of the signal energy in these bands reflect the different fault locations. Further, the artificial neural network is proposed to develop the diagnostic rules of the data base in the present fault identification system. The experimental work is performed to evaluate the effect of fault diagnosis in a rolling ball bearing platform under different fault conditions. The experimental results indicate the effectiveness of the proposed method in fault bearing identification.

scholarly journals Applying Energy-Equal Entropy of Wavelet Packet to Diagnose Circuit Breaker Faults

2014 ◽  
Vol 8 (1) ◽  
pp. 445-452
Author(s):  
Liu Mingliang ◽  
Wang Keqi ◽  
Sun Laijun ◽  
Zhang Jianfeng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Circuit Breaker ◽  
Back Propagation ◽  
Vibration Signal ◽  
Characteristic Parameter ◽  
Vibration Signals

Aiming to better reflect features of machinery vibration signals of high-voltage (HV) circuit breaker (CB), a new method is proposed on the basis of energy-equal entropy of wavelet packet(WP). First of all, three-layer wavelet packet decomposes vibration signal, reconstructing 8 nodes of signals in the 3rd layer. Then, the vector is extracted with energy-equal entropy of reconstructed signals. At last, the simple back-propagation (BP) neural network for fault diagnosis contributes to classification of the characteristic parameter. This technology is the basis of a number of patents and patents pending, which is experimentally demonstrated by the significant improvement of diagnose faults.

Fault Diagnosis of Railway Axlebox Bearing Based on Wavelet Packet and Neural Network

2012 ◽  
Vol 226-228 ◽  
pp. 749-755 ◽  
Author(s):  
Xiao Feng Li ◽  
Yun Xiao Fu ◽  
Li Min Jia
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Diagnostic System ◽  
Vibration Signal ◽  
Fault Classification ◽  
Wavelet Packet Decomposition ◽  
Bearing Fault ◽  
Fault Diagnostic

A real time and effective axlebox bearing fault diagnostic method is significant in the condition-based maintenance. In the axlebox bearing fault diagnostic system, fault features extraction and fault patterns classification are two important aspects to identify whether a axlebox bearing is failure or not. This paper presents a method of axlebox bearing fault diagnosis based on wavelet packet decomposition and BP neural network. First decompose the vibration signal into a finite number of coefficients by wavelet packet decomposition. Then calculate energy moment of each coefficient and take the energy moment as an eigenvector to effectively express the failure feature. Finally BP neural network is used for fault classification. The experimental results show that combining wavelet packet decomposition with BP neural network could identify the axlebox bearing fault effectively. The average diagnosis accuracy rate is 96.67%.

Gear Fault Diagnosis Based on Harmonic Wavelet Packet and BP Neural Network

2012 ◽  
Vol 217-219 ◽  
pp. 2683-2687 ◽  
Author(s):  
Chen Jiang ◽  
Xue Tao Weng ◽  
Jing Jun Lou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Fault Detection And Identification ◽  
Gear Fault ◽  
Harmonic Wavelet ◽  
Gear Fault Diagnosis

The gear fault diagnosis system is proposed based on harmonic wavelet packet transform (WPT) and BP neural network techniques. The WPT is a well-known signal processing technique for fault detection and identification in mechanical system，In the preprocessing of vibration signals, WPT coefficients are used for evaluating their energy and treated as the features to distinguish the fault conditions.In the experimental work, the harmonic wavelets are used as mother wavelets to build and perform the proposed WPT technique. The experimental results showed that the proposed system achieved an average classification accuracy of over 95% for various gear working conditions.

Fan Fault Feature Extraction Based on Wavelet Packet Transform

2014 ◽  
Vol 668-669 ◽  
pp. 999-1002
Author(s):  
Xin Li ◽  
Pan Feng Guo
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Research Topic ◽  
Signal Characteristic ◽  
Manufacturing Enterprises ◽  
Important Position ◽  
Fault Feature Extraction

Fan occupies the important position in many industry, it give rise to that fault diagnosis become the new hot research topic, also is the urgent demand of many manufacturing enterprises. This paper based on the theory of wavelet packet transform, selecting wavelet packet transform and energy spectrum to wavelet de-noising and fault feature extraction the fan vibration signal. And use the MATLAB get the fan vibration signal characteristic vector, lay the foundation for the fan fault diagnosis.

scholarly journals Intelligent Model-based Integrity Assessment of Nonstationary Mechanical System

2021 ◽  
Author(s):  
Hanxin Chen ◽  
Yuzhuo Miao ◽  
Yongting Chen ◽  
Lu Fang ◽  
Li Zeng ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Particle Filter ◽  
Mechanical System ◽  
Centrifugal Pump ◽  
Bp Neural Network ◽  
Vibration Signal ◽  
Fault Identification ◽  
Integrity Assessment ◽  
Diagnosis Method

The fault diagnosis model for nonstationary mechanical system is proposed in the condition monitoring. The algorithm with an improved particle filter and Back Propagation for intelligent fault identification is developed, which is used to reduce the noise of the experimental vibration signals to delete the negative effect of the noise on the feature extraction of the original vibration signal. The proposed integrated method is applied for the trouble shoot of the impellers inside the centrifugal pump. The principal component analysis (PCA) method optimizes the clean vibration signal to choose the optimal eigenvalue features.The constructed BP neural network is trained to get the condition models for fault identification. The proposed novel model is compared with the BP neural network based on traditional PF and particle swarm optimization particle filter (PSO-PF) algorithm. The BP neural network diagnosis method based on the improved PF algorithm is much better for the integrity assessment of the centrifugal pump impeller. This method is much significant for big data mining in the fault diagnosis method of the complex mechanical system.

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