Title :
Fault Diagnosis of Asynchronous Induction Motor Based on BP Neural Network
Author :
Zhao Xiaodong ; Tang Xinliang ; Zhao Juan ; Zhang Yubin
Author_Institution :
Hebei Univ. of Sci. & Technol., Shijiazhuang, China
Abstract :
For asynchronous induction motor, it is necessary to carry out fault diagnosis in time. The traditional fault diagnosis methods have the shortcomings such as the diagnosis slow speed, low accuracy. In this paper, for the common fault characteristics of asynchronous induction motor, the fault diagnosis method based on improved BP algorithm, by using of the diagnosis model, is adopted to diagnose the faults of asynchronous induction motor. The simulated experimental results show that the diagnosis method, a quicker diagnosis and a higher accuracy, is feasible. It can enhance the fault recognition rate and provide an effective amelioration method for keeping equipment reliable and efficient displaying functions.
Keywords :
backpropagation; electric machine analysis computing; fault diagnosis; induction motors; neural nets; BP algorithm; BP neural network; amelioration method; asynchronous induction motor; diagnosis method; diagnosis model; fault diagnosis methods; fault recognition rate; Artificial intelligence; Fault diagnosis; Feedforward systems; Frequency; Induction motors; Neural networks; Neurons; Signal analysis; Testing; Time measurement; Asynchronous Induction Motor; BP Algorithm; Fault Diagnosis; Neural Network;
Conference_Titel :
Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on
Conference_Location :
Changsha City
Print_ISBN :
978-1-4244-5001-5
Electronic_ISBN :
978-1-4244-5739-7
DOI :
10.1109/ICMTMA.2010.417