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基于IPSOElman神经网络的航空发动机故障诊断

皮骏 黄江博

皮骏, 黄江博. 基于IPSOElman神经网络的航空发动机故障诊断[J]. 航空动力学报, 2017, 32(12): 3031-3038. doi: 10.13224/j.cnki.jasp.2017.12.028
引用本文: 皮骏, 黄江博. 基于IPSOElman神经网络的航空发动机故障诊断[J]. 航空动力学报, 2017, 32(12): 3031-3038. doi: 10.13224/j.cnki.jasp.2017.12.028
Aeroengine fault diagnosis based on IPSOElman neural network[J]. Journal of Aerospace Power, 2017, 32(12): 3031-3038. doi: 10.13224/j.cnki.jasp.2017.12.028
Citation: Aeroengine fault diagnosis based on IPSOElman neural network[J]. Journal of Aerospace Power, 2017, 32(12): 3031-3038. doi: 10.13224/j.cnki.jasp.2017.12.028

基于IPSOElman神经网络的航空发动机故障诊断

doi: 10.13224/j.cnki.jasp.2017.12.028
基金项目: 中央高校基本科研业务费中国民航大学专项资金(3122013H001)

Aeroengine fault diagnosis based on IPSOElman neural network

  • 摘要: 为提高航空发动机故障诊断的精度,提出改进粒子群优化的Elman神经网络对航空发动机故障诊断的方法。利用MIV(平均影响值)对神经网络的输入端自变量进行筛选,降低输入维度;采用改进粒子群优化算法对Elman神经网络的权值和阀值进行优化,并对优化的神经网络进行训练;用训练好的神经网络对航空发动机故障进行诊断并与常规的BP(back propagation)、Elman神经网络、GM(1,n)、SVM (support vector machines)进行对比。仿真结果表明:IPSOElman(improved particle swarm optimization Elman neural network)神经网络的诊断误差在不同数量训练样本时都小于其他方法,并且在参选故障诊断的性能参数不同时,其诊断误差相近,展现出较强的适应能力。

     

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出版历程
  • 收稿日期:  2017-03-03
  • 刊出日期:  2017-12-28

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