Reach on Micro-Motor Acoustics Fault Diagnosis Based on Loose Wavelet Neural Network

2013 ◽  
Vol 579-580 ◽  
pp. 775-780
Author(s):  
Ying Ge Li ◽  
Gui Tang Wang ◽  
Zheng Li ◽  
Xin Liang Yin
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Reconstruction Algorithm ◽  
Characteristic Parameter ◽  
Wavelet Neural Network ◽  
Voice Signal ◽  
Network Fault ◽  
Frequency Band Energy

Nicro-motor voice signal contains abundant running status information as well as vibration signal, aiming at the problem that it is difficult to obtain vibration signal in the production line of micro-motor, this paper proposes a micro-motor acoustic fault diagnosis methods based on loose wavelet neural network. Wavelet packet decomposition and reconstruction algorithm is utilized to extract micro-motor voice signals in each frequency band energy as the characteristic parameters of fault characteristic parameter samples will input to improve the BP neural network for training, build up the fault type of classifier, the realization of fault intelligent diagnosis. Application results show that a reasonable design of neural network has strong ability of fault identification; use loose micro-motor acoustic wavelet neural network fault diagnosis is feasible.

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.

Motor Broken-Bar Fault Diagnosis Based on Park Vector and Wavelet Neural Network

2011 ◽  
Vol 382 ◽  
pp. 163-166
Author(s):  
Qing Xin Zhang ◽  
Jin Li ◽  
Hai Bin Li ◽  
Chong Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fundamental Frequency ◽  
Wavelet Packet ◽  
Frequency Component ◽  
Wavelet Neural Network ◽  
Simulation Software ◽  
Monitoring Methods ◽  
Stator Current ◽  
Fundamental Frequency Component

In the technology of motor fault diagnosis, current monitoring methods have become a new trend in motor fault diagnosis. This paper presents a motor fault diagnosis method based on Park vector and wavelet neural network. This method uses the stator current as the object of study. Firstly, it uses Park vector to deal with the stator current and filter out fundamental frequency component, thus the characteristics component of motor broken-bar will be separated from fundamental frequency component; Secondly, it uses five layers wavelet packet decomposition to pick up fault characteristic signal; Finally, we distinguish the fault by BP neural network, and use the simulation software of MATLAB to realize it. The test results show that: This method can detect the existence of motor broken-bar fault, and has a good value in engineering.

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.

scholarly journals Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network

2013 ◽  
Vol 20 (2) ◽  
pp. 247-262 ◽  
Author(s):  
Hanxin Chen ◽  
Yanjun Lu ◽  
Ling Tu
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Experimental System ◽  
Vibration Signal ◽  
Wavelet Neural Network ◽  
Acceleration Sensors ◽  
Gear Fault ◽  
Gear Crack

A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.

Based on Information Fusion Integrated Wavelet Neural Network Fault Diagnosis

2011 ◽  
Vol 219-220 ◽  
pp. 1077-1080
Author(s):  
Dong Yan Cui ◽  
Zai Xing Xie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Wavelet Neural Network ◽  
Wavelet Neural Networks ◽  
Effective Combination ◽  
Fault Diagnosis System ◽  
Network Fault ◽  
Fault Characteristic

In this paper, the integration of wavelet neural network fault diagnosis system is established based on information fusion technology. the effective combination of fault characteristic information proves that integration of wavelet neural networks make better use of a variety of characteristic information than the list of wavelet neural networks to solve difficulties and problems which are difficult to resolve by a single network.

Application of Wavelet Packet Energy Spectrum in Micro Motor Fault Diagnosis

2013 ◽  
Vol 726-731 ◽  
pp. 3159-3162
Author(s):  
Sheng Yi Chen ◽  
Gui Tang Wang ◽  
Shou Lei Sun ◽  
Qiang Zhou
Keyword(s):  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Reconstruction Algorithm ◽  
Mechanical Equipment ◽  
Vibration Signals ◽  
Band Energy ◽  
Status Information ◽  
Frequency Band Energy

To diagnosis vibration signals of micro motor in several different fault types a method based on wavelet packet energy spectrum is presented, the energy on each Sub-frequency band, which are Calculated by Wavelet packet decomposition and reconstruction algorithm, are used to normalization process.Under both circumstances of normal working and unmoral working of mechanical equipment,there exist evident differences among the Sub-frequency band energy after the decomposition of wavelet packet, which energy contains a wealth of micro motor running status information and the eigenvectors is structured by the Sub-frequency band energy spectrum can establish energy and Fault mapping relationship.The preliminary experimental results show that it is effective to use the wavelet packet-energy spectrum in micro motor fault diagnosis .

scholarly journals Fault diagnosis of wind turbine gearbox based on wavelet neural network

2018 ◽  
Vol 37 (4) ◽  
pp. 977-986 ◽  
Author(s):  
Chen Huitao ◽  
Jing Shuangxi ◽  
Wang Xianhui ◽  
Wang Zhiyang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Network Model ◽  
Vibration Signal ◽  
Wavelet Neural Network ◽  
Wind Turbine Gearbox ◽  
Diagnosis Method ◽  
Fault Mechanism ◽  
The Relationship

In order to monitor the wind turbine gearbox running state effectively, a fault diagnosis method of wind turbine gearbox is put forward based on wavelet neural network. Taking a 1.5 MW wind turbine gearbox as the target of study, the frequency spectrum of vibration signal and the fault mechanism of driving part are analyzed, and the eigenvalues of the frequency domain are extracted. A wavelet neural network model for fault diagnosis of wind turbine gearbox is established, and wavelet neural network is trained by using different feature vectors of fault types. The relationship between fault component and vibration signal is identified, and the vibration fault of wind turbine gearbox is predicted and diagnosed by network model. The analysis results show that the method can diagnose fault and fault pattern recognition of wind turbine gearbox very well.

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.

Study of the Fault Diagnosis Method Based on Wavelet Time and Frequency Analysis

2012 ◽  
Vol 472-475 ◽  
pp. 2166-2170
Author(s):  
Qun Qi ◽  
Xue Zhang Zhao
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Asynchronous Motor ◽  
Wavelet Neural Network ◽  
Experiment Data ◽  
Complex Fault ◽  
Diagnosis Method ◽  
Neural Network Algorithm ◽  
Network Fault

In order to better solve asynchronous motor complex fault characteristics, improve the reliability of the diagnosis and accuracy, combined with wavelet transform technique, construct a wavelet neural network, wavelet transform technology feature extraction asynchronous motor as a signal wavelet neural network's input vector, and the wavelet neural network algorithm was used to optimize, realize the motor identify types of fault, through the simulation experiment data diagnosis results show that this method is effective and feasible. Based on the wavelet analysis and neural network fault diagnosis method of research.

Fault Diagnosis Method of Time Domain and Time-Frequency Domain Based on Information Fusion

2013 ◽  
Vol 300-301 ◽  
pp. 635-639 ◽  
Author(s):  
Jiang Zhao ◽  
Jiao Wang ◽  
Meng Shang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Time Domain ◽  
Wavelet Packet ◽  
Network Information ◽  
Time Frequency ◽  
Index Data ◽  
Diagnosis Method ◽  
Frequency Band Energy

On account of the problem that traditional pipe leakage diagnosis method is not highly accuracy .this paper come up with a method that based on pipe leakage diagnosis method of neural network information fusion. Giving the stress wave time domain feature extraction index data algorithm and wavelet packet extraction each the frequency band energy algorithm, by comparing with these results of the pressure wave time domain feature index data, time-frequency extraction energy values and fault diagnosis of both information fusion ,which show the neural network information fusion method that is used for pipe leakage diagnosis that is feasible and effective.

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