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.

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

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.

A two-stage classifier switchable aluminum electrolysis fault diagnosis method

2021 ◽  
pp. 014233122110596
Author(s):  
Tianhao Gao ◽  
Ke Zhang ◽  
Huaitao Shi ◽  
Jinbao Zhao ◽  
Jiejia Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Simple Structure ◽  
Aluminum Electrolysis ◽  
Electrolytic Cell ◽  
Training Method ◽  
Random Forest Algorithm ◽  
Electrolytic Cells ◽  
Diagnosis Method ◽  
Neural Network Algorithm

Traditional aluminum electrolysis fault diagnosis methods have problems such as low accuracy, small forecast advance, and high CPU usage, which make their popularity low in enterprises. Aiming at the above problems, a fault diagnosis method with switchable two-level classifiers is designed. The input data are first judged by the first-level algorithm. If it is determined that there is no fault, the result will be output directly. If it is determined that there is a fault in the electrolytic cell, the data will be transferred to the second-level network for specific fault diagnosis. The first level is based on the Random Forest algorithm with simple structure and good two-class classification effect and is optimized by the improved cuckoo algorithm. The second level is based on an improved DBN-DNN (Deep Belief Neural Network–Deep Neural Network) algorithm, and the training method is given. Experimental results show that this method can switch between different algorithms according to different situations, save computing resources, realize that a computer can monitor multiple electrolytic cells, and reduce investment costs. In addition, the accuracy and forecast advance have been significantly improved, which has promoted the popularization of fault diagnosis systems in aluminum electrolysis enterprises.

scholarly journals Research on Fault Diagnosis Method Based on Rule Base Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zheng Ni ◽  
Zhang Lin ◽  
Wang Wenfeng ◽  
Zhang Bo ◽  
Liu Yongjin ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Location ◽  
Fuzzy Theory ◽  
Learning Rule ◽  
Rule Base ◽  
Fuzzy Neural ◽  
Diagnosis Method ◽  
Construction Model ◽  
Network Fault

The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

Fault Diagnosis Method Based on KPCA and Selective Neural Network Ensemble

2014 ◽  
Vol 915-916 ◽  
pp. 1272-1276 ◽  
Author(s):  
Yan Yan Pang ◽  
Hai Ping Zhu ◽  
Fan Mao Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Binary Particle Swarm Optimization ◽  
Large Network ◽  
Neural Network Ensemble ◽  
Tennessee Eastman Process ◽  
Training Time ◽  
Diagnosis Method ◽  
Neural Network Algorithm ◽  
Bagging Method

Aiming at the problems of less study sample, large network scale and long training time existing in current fault diagnosis field, we develop a new method based on KPCA and selective neural network ensemble. First, reducing the data size by using KPCA to extract the sample features. Then achieving a selective neural network ensemble method based on improved binary particle swarm optimization algorithm (IBPSOSEN), and combining the two methods for fault diagnosis. In selective neural network algorithm, bagging method is used to take a number of different training sets of fault samples to solve the problem of less fault samples. Finally, simulation experiments and comparisons over Tennessee Eastman Process (TE) demonstrate the effectiveness and feasibility of the proposed method.

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.

Fault Diagnosis of Tolerance Analog Circuit Based on Wavelet Neural Network with PSO Algorithm

2013 ◽  
Vol 307 ◽  
pp. 312-315 ◽  
Author(s):  
Wei Cong ◽  
Bo Jing ◽  
Hong Kun Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Parameters Optimization ◽  
Bp Algorithm ◽  
Algorithm Performance ◽  
Fast Training ◽  
Diagnosis Method

For the Difficulties in fault diagnosis of tolerance analog circuit, a Wavelet Neural Network (WNN) diagnosis method based on Particle Swarm Optimization (PSO) algorithm is proposed. To overcome the deficiencies of the traditional BP algorithm using in WNN, PSO algorithm is introduced into the parameters optimization in WNN, and the velocity disturbance operator is embedded to ensure the particle out of the premature position for PSO algorithm performance. The simulation results show that the proposed method has the fast training rate, accurate diagnosis, without local convergence.

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