| 基于ALNAFSA优化BP神经网络的行星齿轮箱故障诊断 |
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| 引用本文： | 程海吉,魏秀业,张宁,贺妍,徐晋宏. 基于ALNAFSA优化BP神经网络的行星齿轮箱故障诊断[J]. 煤矿机械, 2021, 42(1): 143-146 |
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| 作者姓名： | 程海吉 魏秀业 张宁 贺妍 徐晋宏 |
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| 作者单位： | 中北大学机械工程学院,太原030051;中北大学机械工程学院,太原030051;先进制造技术山西省重点实验室,太原030051 |
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| 基金项目： | 山西省应用基础研究计划青年基金项目(201901D211201);中北大学先进制造技术山西省重点实验室开放课题研究基金(XJZZ202002)。 |
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| 摘 要： | 以行星齿轮箱为研究对象,提出了一种新型故障诊断方法。介绍了一种改进的自适应局部邻域人工鱼群算法(ALNAFSA),用该算法优化BP神经网络,避免了BP出现局部极值。对行星齿轮箱进行了实验,提取出5种工况的样本熵并构成特征向量,将其输入到ALNAFSA-BP模型中进行分类识别。与BP神经网络模型进行比较,结果表明,ALNAFSA-BP神经网络故障诊断准确率显著提高,达到95%。
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| 关 键 词： | 人工鱼群算法 BP神经网络 故障诊断 行星齿轮箱 |
Planetary Gearbox Fault Diagnosis Based on BP Neural Network Optimized by ALNAFSA |
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| Cheng Haiji,Wei Xiuye,Zhang Ning,He Yan,Xu Jinhong. Planetary Gearbox Fault Diagnosis Based on BP Neural Network Optimized by ALNAFSA[J]. Coal Mine Machinery, 2021, 42(1): 143-146 |
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| Authors: | Cheng Haiji Wei Xiuye Zhang Ning He Yan Xu Jinhong |
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| Affiliation: | (School of Mechanical Engineering,North University of China,Taiyuan 030051,China;Advanced Manufacturing Technology Key Laboratory of Shanxi Province,Taiyuan 030051,China) |
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| Abstract: | Taking the planetary gearbox as research object,a new method of planetary gearbox fault diagnosis was proposed. First, an improved adaptive local neighborhood artificial fish swarm algorithm(ALNAFSA) was proposed. By this algorithm optimized the BP neural network,avoided BP local extremums. The planetary gearbox was tested and the sample entropy of five working conditions was extracted to form a feature vector, which was input into the ALNAFSA-BP model for classification and recognition. Compared with BP neural network model,the results show that the optimized BP neural network greatly improves the fault diagnosis efficiency of ALNAFSA which reaches 95%. |
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| Keywords: | artificial fish swarm algorithm BP neural network fault diagnosis planetary gearbox |
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