Fault diagnosis of induction motor rotor based on BP neural network and D-S evidence theory

2012 ◽  
Author(s):  
Lieping Zhang ◽  
Shoufeng Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Bp Neural Network ◽  
Evidence Theory

scholarly journals A novel nonlinear observer for fault diagnosis of induction motor

2020 ◽  
Vol 14 ◽  
pp. 174830262092272
Author(s):  
Lingzhi Yi ◽  
Yue Liu ◽  
Wenxin Yu ◽  
Jian Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Bp Neural Network ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Adaptive Observer ◽  
Nonlinear Observer ◽  
Feedback Gain

In order to accurately diagnose the fault of induction motor, a fault diagnosis of nonlinear observer method based on BP neural network and Cuckoo Search algorithm is proposed. It is a new method which mixes analytical model and artificial neural network; firstly, the induction motor model is divided into linear and nonlinear parts, and BP neural network is used to approximate the nonlinear part. Then an adaptive observer is established, in which a simple and effective method for selecting the feedback gain matrix is offered. Cuckoo Search algorithm is utilized to improve the convergence speed and approximation accuracy in BP Neural Network. Compared with some other algorithms, the simulation results show that the proposed method has higher prediction accuracy. The designed nonlinear observer can estimate the current and speed accurately. Finally, the experiment of winding fault is implemented, and the online fault detection of induction motor is realized by analyzing the current residual errors.

Vibration Fault Diagnosis for Hydroelectric Generating Units Based on the Modified Cuckoo Search Algorithm and Evidence Theory

2019 ◽  
Vol 13 (3) ◽  
pp. 281-288
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Yan Xiong
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Search Algorithm ◽  
Evidence Theory ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
System Structure ◽  
Hybrid Scheme ◽  
Independent Evidence

Background: In view of the complex system structure and uncertain factors in the fault diagnosis of hydroelectric generating units (HGU), it is a difficult problem to design the diagnosis method rationally. Objective: An attempt is made to employ multi-source feature information to improve the accuracy of fault diagnosis, and the effectiveness of the proposed scheme is verified by using a diagnostic example. Methods: Through the research on recent papers and patents related to fault diagnosis of the HGU, a hybrid scheme based on the modified cuckoo search algorithm, back-propagation (BP) neural network and evidence theory are proposed. For this modified version named cuckoo search with fitness information (CSF), the step factor is adaptively tuned using the fitness value. Next, three diagnostic models based on BP neural network trained by CSF are used for primary diagnosis. These diagnostic results are then used as the independent evidence, and the fusion decision is made by using evidence theory. Results: Experimental results show that CSF algorithm is better than the original cuckoo search (CS) and its three variants, and the hybrid method has the highest diagnostic accuracy. Conclusion: The proposed hybrid scheme has strong robustness and fault tolerance, and can effectively classify the vibration faults of hydroelectric generating units

Fault Diagnosis of Analog Circuits Based on Evidence Theory

2014 ◽  
Vol 556-562 ◽  
pp. 2149-2152
Author(s):  
Cheng Cheng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Analog Circuits ◽  
Bp Neural Network ◽  
Analog Circuit ◽  
Evidence Theory ◽  
Fusion Technology ◽  
Diagnosis Method ◽  
Simulation Results

BP neural network and evidence theory data fusion technology can be used in troubleshooting electronic equipment, from the simulation results show that the fault diagnosis method based on evidence theory and BP neural network can effectively diagnose faults in analog circuit, and it has automated intelligent characteristics.

Improved PSO-BP neural network for power transformer fault diagnosis

2010 ◽  
Vol 30 (3) ◽  
pp. 783-785 ◽  
Author(s):  
Zhong-yang XIONG ◽  
Qing-bo YANG ◽  
Yu-fang ZHANG
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Transformer Fault ◽  
Improved Pso

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

2020 ◽  
Vol 14 (2) ◽  
pp. 205-220
Author(s):  
Yuxiu Jiang ◽  
Xiaohuan Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lower Limit ◽  
Bp Neural Network ◽  
Accelerator Pedal ◽  
Electronic Throttle ◽  
Measured Voltage ◽  
Upper Limit ◽  
Network Fault

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

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.

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