递阶结构进化神经网络在故障诊断中的应用

Hierarchical genetic neural network for fault diagnosis

主要研究进化神经网络在旋转机械故障诊断中的应用 ,提出了一种基于递阶结构的遗传算法与进化规划相结合的神经网络学习新算法 ,利用该算法可以同时对网络进行结构优化和权重求解。通过旋转机械故障分类应用实例 ,与传统的 BP训练算法作了比较 ,证明基于递阶结构的进化神经网络算法不仅在权重训练方面比传统 BP训练算法更加快速稳定 ,避免陷入局部极小点 ,而且同时对网络结构进行了优化 。

A genetic neural network was developed for fault diagnosis in rotating machinery. A new hierarchical structure is presented using a neural network learning algorithm which combines a genetic algorithm and evolutionary programming. The shooting method is used to optimize the network structure and train the connection weights. Rotating-machinely fault-classification data was used to compare the shooting method and the traditional backwards propagation (BP) algorithm. The result proves that the hierarchical genetic neural network converges faster than the BP training algorithm, that it avoids falling into local minima, and that it provides a much simpler classification neural network for the faults.