针对高背景噪声下滚动轴承全生命周期(轻度退化、中度退化、重度退化)故障诊断需求,提出GA-OIHF (Genetic Algorithm-Output Input Hidden Feedback) Elman神经网络模型,实现退化故障的精准诊断.利用集合经验模态分解对振动信号进行有效降噪与故障特征提取.设计OIHF Elman神经网络,并在Elman神经网络结构的基础上,同时增加输出层对隐含层与输入层的反馈,进一步提高其对滚动轴承全生命周期数据的处理能力.然后,通过结合遗传算法构建一种新的GA-OIHF Elman神经网络模型,该模型综合了遗传算法的全局寻优与OIHF Elman神经网络的局部寻优能力,从而实现对滚动轴承全生命周期的精确故障诊断.实验结果表明,所提出的GA-OIHF Elman方法不仅对于滚动轴承全生命周期故障具有准确的诊断效果,而且保证了诊断模型对于不同故障(不同故障部件与不同故障时期)的诊断稳定性.

For the fault diagnosis needs of the full life cycle (light degradation, moderate degradation, and severe degradation) of rolling bearing under the environment of high background noise, a genetic algorithm-output input hidden feedback (GA-OIHF ) Elman neural network model is proposed to achieve precise diagnosis of the degradation faults of rolling bearing. Ensemble empirical mode decomposition (EEMD) is selected to effectively reduce the noise and extract fault features of the vibration signal. An OIHF Elman neural network is designed by increasing the feedbacks from the output layer to the hidden layer and the input layer based on the Elman neural network, thus further improves its ability to process full life cycle data of rolling bearing. Then, a novel GA-OIHF Elman neural network model is developed by combining the genetic algorithm (GA) and the OIHF Elman neural network. The novel GA-OIHF Elman neural network model combines the global optimization of GA and the local optimization ability of the OIHF Elman neural network to achieve an accurate fault diagnosis of the entire life cycle of rolling bearing. The experimental results show that the GA-OIHF Elman algorithm model can not only accurately diagnose the fault in the full life cycle of rolling bearing, but also ensure the stability of the diagnosis model for different faults including different fault components and stages.