Fault detection of motor's inter-turn short circuit based on BP neural network

2012 ◽  
Vol 16 (3) ◽  
pp. 234 ◽  
Author(s):  
Zhenzhou Wang ◽  
Ming Han ◽  
Peng Liu ◽  
Jiaomin Liu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Bp Neural Network ◽  
Short Circuit

scholarly journals Motor Fault Detection Using Wavelet Transform and Improved PSO-BP Neural Network

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1322 ◽  
Author(s):  
Chun-Yao Lee ◽  
Yi-Hsin Cheng
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Bp Neural Network ◽  
Short Circuit ◽  
Back Propagation ◽  
Operating Environment ◽  
Cause Of Failure ◽  
Bearing Damage ◽  
Improved Pso

This paper proposes a motor fault detection method based on wavelet transform (WT) and improved PSO-BP neural network which is combined with improved particle swarm optimization (PSO) and a back propagation (BP) neural network with linearly increasing inertia weight. First, this research used WT to analyze the current signals of the healthy motor, bearing damage motor, stator winding inter-turn short circuit motor, and broken rotor bar motor. Second, features after completing the signal analysis were extracted, and three types of classifiers were used to classify. The results show that the improved PSO-BP neural network can effectively detect the cause of failure. In addition, in order to simulate the actual operating environment of the motor, this study added white noise with signal noise ratios of 30 dB, 25 dB, and 20 dB to verify that this model has a better anti-noise ability.

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.

Fault Detection of Carbide Anvil Based on Hurst Exponent and BP Neural Network

2013 ◽  
Vol 805-806 ◽  
pp. 1881-1886 ◽  
Author(s):  
Li Han ◽  
Bin Chen ◽  
Bao Cheng Gao ◽  
Zhao Li Yan ◽  
Xiao Bin Cheng
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Bp Neural Network ◽  
Hurst Exponent ◽  
Recognition Rate ◽  
Pulse Signal ◽  
Characteristic Parameters ◽  
Threshold Method ◽  
Fault Recognition ◽  
Bp Neural Network Model

This paper proposed a novel diagnosis algorithm based on Hurst exponent and BP neural network to detect carbide anvil fault in synthetic diamond industry. Firstly, a sort of preprocessing algorithm is proposed, which uses the sliding window and energy threshold method to separate the pulse from initial continuous signal. Then, some characteristic parameters which are based on Hurst exponent are extracted from the separated pulse signal. These characteristic parameters are used to construct fault characteristic vectors. Finally, the BP neural network model was established for fault recognition. Experimental results show that the proposed fault detection method has high recognition rate of 96.7%.

The Application of BP Neural Network in Diagnosing the Short Circuit Fault of Winding

2014 ◽  
Vol 543-547 ◽  
pp. 1064-1067
Author(s):  
Jian Qun Zhang ◽  
De Jian Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Short Circuit ◽  
Evidence Theory ◽  
Accurate Diagnosis ◽  
Bp Neural Networks ◽  
Rotor Winding ◽  
Short Circuit Fault

As a common fault of motor, the short circuit of rotor winding is important for the accurate diagnosis. In this article, the author collected every status parameter of motor by different sensors, using two BP neural networks to partly diagnose the motor and fusing the results of partly diagnosis by D-S evidence theory. The author increases the creditability of diagnosis results by practices and decreases uncertainty, showing the efficiency of this method.

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