Research On Fault Diagnosis Of Induction Motor Based On Artificial Neural Network Optimized By Bee Colony Algorithm

With the development of social economy,the normal operation of Asynchronous Motors has a great impact on production efficiency and production rhythm.Because the asynchronous motor is in a variety of complex environment,cause the cause of the failure is full of uncertainty,so it is necessary to study the type of fault into the type of fault,so that it can be found early and solved in time.Therefore,the asynchronous motor fault diagnosis technology provides a guarantee for the normal operation of the motor,and has very important practical significance and theoretical significance.On the one hand,the problem where the fault is known early is where the accident can be avoided immediately so that the loss of the property can be retrieved.On the other hand,the related data involved in the type of fault detection can provide very valuable data experience for the designer,and also to improve the performance of the motor.Contribution.On the basis of summarizing the research status of motor fault diagnosis at home and abroad,this paper introduces the basic principle and basic theory of BP neural network and artificial bee colony algorithm(Artificial Bee Colony ABC).By analyzing the basic principle of the artificial bee colony algorithm,it is found that although the algorithm has a good effect on the global search ability,the algorithm has a good effect,but it has a good effect on the global search ability,but it is found that the algorithm has a good effect on the global search ability.There is no strong local search mechanism for the optimal solution,and there is imbalance between exploitation and development.In combination with the improved bee search formula,this paper further improves the selection of interference factors in the formula.Secondly,some shortcomings and defects of BP neural network in the fault diagnosis process are analyzed,and the artificial bee colony algorithm has a good effect on solving some global optimization problems.The improved artificial bee colony algorithm is applied to optimize the BP neural network,and the improved network is used for asynchronous motor at the most later.Fault diagnosis.In this way,the advantages of the artificial bee colony algorithm and the BP neural network are integrated each other,making it better able to detect the performance of the motor fault.