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Motor Failure Diagnosis Based on ant Colony Algorithm and BP Neural Network

As the main measure to protect the normal operation of mechanical equipment, motor failure diagnosis is very important. This paper proposes failure diagnosis based on the combination of ant colony algorithm and BP neural network towards motor failure. Here, the ant colony algorithm is used for training the neural network in order to diagnosis the motor failure. By testing the algorithm it is found that the ant colony algorithm trained neural network has the characteristics of wide mapping capability, faster convergence speed, and high accuracy of failure diagnosis. The algorithm can diagnose motor failure effectively, and improve the efficiency and quality of diagnosis, to avoid the problem of slow convergence and the tendency to fall into the local minima point by just using BP neural network.