计算机工程与应用2019，Vol.55Issue(6)：24-30,7.DOI:10.3778/j.issn.1002-8331.1811-0008
基于BP神经网络的故障诊断模型研究
Study on Fault Diagnosis Model Based on BP Neural Network
摘要
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 bet-ter fault diagnosis has the very good application value.关键词
BP神经网络/量子蜂群算法/故障诊断Key words
BP neural network/quantum bee colony algorithm/fault diagnosis分类
信息技术与安全科学引用本文复制引用
冯玉芳,卢厚清,殷宏,曹林..基于BP神经网络的故障诊断模型研究[J].计算机工程与应用,2019,55(6):24-30,7.基金项目
国家自然科学基金(No.61440047,No.61562079) （No.61440047,No.61562079）
新疆维吾尔自治区人文社科重点研究基地项目(No.050315C01). （No.050315C01）
