The new fault diagnosis method of wavelet packet neural network on pump valves of reciprocating pumps

2009 ◽  
Author(s):  
Duan Yu-bo ◽  
Wang Xing-zhu ◽  
Han Xue-song
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Diagnosis Method

Fault Diagnosis of Cascaded Converter Based on Wavelet Packet RBF Neural Network

2013 ◽  
Vol 273 ◽  
pp. 300-304
Author(s):  
Xin Wang ◽  
Juan Xu ◽  
Guo Dong Zhang ◽  
Rui Min Qi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Open Circuit ◽  
Learning Performance ◽  
Cascaded Converter ◽  
Diagnosis Method ◽  
Faults Diagnosis

To study the power component open circuit faults diagnosis method of the cascaded converter. Aiming at the insufficiency of the BP learning algorithm in the machinery fault diagnosis, such as the low learning convergence speed, the easily appearing local minimum, the instability learning performance caused by the initial value, to proposed a new method applied to the cascaded converter based on radial basis function (RBF) neural network. Experiments show that the method based on wavelet packet analysis and RBF neural network has better learning and fault identification capability, and it can meet the online real-time fault diagnosis of the cascaded converter.

The Fault Diagnosis Method of Diesel Based on Wavelet Pocket and BP Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 910-913 ◽  
Author(s):  
Hong Hui Sun ◽  
Jun Xu ◽  
Qing Hua Zhang ◽  
Hong Xia Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Vibration Signals ◽  
Signal Energy ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Neural Network Algorithm

Because of the well time-frequency spectrum disposal capability of wavelet packet, the wavelet packet algorithm is used to analyze the time - frequency characteristics of diesel vibration signals. The signal energy distributing characteristics based on wavelet packet transform. are extracted and taken as diagnostic characteristic vector, then improved BP neural network algorithm that connects additional momentum with self-adaptive learning rate was used to classify and recognize faults of diesel valves. The experimental results show the fault diagnosis method of diesel based on wavelet pocket and BP neural network is effective and feasible.

scholarly journals Optimization of a Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Wavelet Packet Transform by Simulated Annealing

2022 ◽  
Author(s):  
Feng He ◽  
Qing Ye
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Optimal Solution ◽  
Fault Classification ◽  
Parameter Adjustment ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

Bearings are widely used in various types of electrical machinery and equipment. As their core components, failures will often cause serious consequences . At present, most methods of parameter adjustment are still manual adjustment of parameters. This adjustment method is susceptible to prior knowledge and easy to fall into the local optimal solution, failing to obtain the global optimal solution and requires a lot of resources.Therefore, this paper proposes a new method of bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by simulated annealing algorithm.The experimental results show that the method proposed in this paper has a more accurate effect in feature extraction and fault classification compared with traditional bearing fault diagnosis methods. At the same time, compared with the traditional artificial neural network parameter adjustment, this paper introduces the simulated annealing algorithm to automatically adjust the parameters of the neural network, thereby obtaining an adaptive bearing fault diagnosis method. To verify the effectiveness of the method, the Case Western Reserve University bearing database was used for testing, and the traditional intelligent bearing fault diagnosis method was compared. The results show that the method proposed in this paper has good results in bearing fault diagnosis. Provides a new way of thinking in the field of bearing fault diagnosis in parameter adjustment and fault classification algorithms

Fault Diagnosis Technology for Power Electronics Rectifier Based on Wavelet Packet

2011 ◽  
Vol 317-319 ◽  
pp. 1215-1218 ◽  
Author(s):  
Qing Hu ◽  
Rong Jie Wang ◽  
Lian Shi Lin
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Electronics ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Fault Pattern ◽  
Multi Scale ◽  
Wavelet Packet Transformation ◽  
Diagnosis Method

The paper presents on power electronics controlled rectifier faults diagnose technology based on the combination wavelet packet transformation and neural network mainly. By using the character of wavelet packet multiresolution, fault signal is decomposed at multi-scale, orthogonalation and normalization, extract feature vector, which as training input of neural network, and design classifier of fault pattern. The validity and feasibility of the fault diagnosis method is demonstrated by simulation. this method can quite accurately diagnosis fault and define fault element for power electronics controlled rectifier, and the diagnosis precision is high, the method has very good practical value and apply future on solving power electronics controlled rectifier fault diagnosis.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet- Neural Network Characteristic Entropy

2010 ◽  
Vol 108-111 ◽  
pp. 1075-1079 ◽  
Author(s):  
Li Ying Wang ◽  
Wei Guo Zhao ◽  
Ying Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Decomposition ◽  
Intelligent Fault Diagnosis ◽  
Network Characteristic ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

On the basis of neural network based on wavelet packet-characteristic entropy(WP-CE) the author proposes a new fault diagnosis method of vibrating of hearings, in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted, the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample the three layers BP neural network is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

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