关键词: BP神经网络, 量子蜂群算法, 故障诊断

Abstract: Rolling bearing is one of the most commonly used components in rotating machinery. It’s easy to be damaged. But its working condition usually is very complex, which makes it difficult to diagnose rolling bearing faults accurately. In order to improve the effectiveness of rolling bearing fault?diagnosis, the improved quantum bee colony algorithm is introduced into BP neural network and the BP neural network diagnosis model based on Improved Quantum Artificial Bee Colony algorithm（IQABC-BP） is proposed. Firstly, an improved quantum bee colony algorithm is proposed to solve the problem of quantum bee colony algorithm. Then the improved quantum bee colony algorithm is applied to optimize the initial weight, threshold and the number of hidden layer of BP neural network. Finally the model of BP neural network based on the improved quantum bee colony algorithm with super parallel and ultra-high speed is proposed and is applied to?fault diagnosis of rolling bearing. The experimental results show that IQABC-BP with convergence speed faster and better fault diagnosis has the very good application value.

Key words: BP neural network, quantum bee colony algorithm, fault diagnosis