基于IGA-ELM网络的滚动轴承故障诊断

为了提高航空发动机轴承故障诊断准确率，提出基于改进遗传算法优化极限学习机网络（IGA-ELM）的诊断模型。针对传统遗传算法易早熟等缺陷，对遗传算法的交叉操作和变异操作进行改进，并用改进的遗传算法优化极限学习机的输入权值矩阵和隐含层阈值，利用Moore-Penrose算法计算极限学习机的输出权值矩阵。使用IGA-ELM诊断模型对滚动轴承正常、内环故障、外环故障和滚珠故障4种工况进行诊断，并分析极限学习机隐含层神经元的数量和激活函数对轴承故障诊断的影响。为了验证改进遗传算法优化极限学习机的有效性，将传统遗传算法、自适应遗传算法和粒子群算法作为对比算法。经过分析表明：改进遗传算法收敛速度和收敛误差，均优于对比算法。

Rolling bearing fault diagnosis based on IGA-ELM network

To improve the accuracy of fault diagnosis of aero-engine bearings, a diagnosis model is proposed based on the Optimized Extreme Learning Machine by Improved Genetic Algorithm (IGA-ELM). Crossover operation and mutation operation of the genetic algorithm is improved in this paper to overcome the flaw of the traditional genetic algorithm. The input weight matrix of the connection between the input and hidden layers and biases of hidden neurons of the extreme learning machine is optimized by the improved genetic algorithm. The output matrix of the connection between the hidden and output layers was calculated by using the Moore-Penrose algorithm. Four cases including normal rolling bearing, inner ring fault, out ring fault, and ball fault are diagnosed by using the optimized extreme learning machine. The influence of the number of the hidden layer neurons and the activation function on bearing fault diagnosis of the extreme learning machine is also analyzed. To verify to, as a comparative algorithm A comparison of the traditional genetic algorithm, adaptive genetic algorithm and particle swarm optimization algorithm shows the effectiveness of the improved genetic algorithm in extreme learning machine optimization. Analysis shows that the improved genetic algorithm are better than the other algorithms in terms of convergence speed and convergence error.