基于连续小波卷积神经网络的轴承智能故障诊断方法

Bearing intelligent fault diagnosis method based on continuous wavelet convolutional neural network

传统故障诊断方法存在特征提取有限和故障检测不准确的问题,为此提出新的轴承智能故障诊断方法.构建连续小波卷积层取代卷积神经网络(CNN)中的初始卷积层,用于提取轴承数据的初级特征;使用增强ACON激活函数处理提取的振动信号;设计新的计算空间,提高CNN的整体自适应性.在凯斯西储大学轴承数据集上开展滚动轴承故障诊断方法对比实验.结果表明,与传统基于CNN、快速傅里叶变换-CNN、长短时记忆CNN故障诊断方法相比,所提方法的故障诊断精度分别提高了7.45、4.46和1.53个百分点,CNN的收敛速度更快.在不同工况的泛化任务中,所提方法的平均准确率为99.64%,准确性和泛化能力良好.

A new bearing intelligent fault diagnosis method was proposed,aiming at the problems of limited feature extraction and inaccurate fault detection in traditional fault diagnosis methods.A continuous wavelet convolutional layer was constructed to replace the initial convolutional layer in the convolutional neural network(CNN)for extracting the primary features of the bearing data.The enhanced ACON activation function was used to process the extracted vibration signals.A new computational space was designed to improve the overall adaptivity of CNN.Comparative experiments of rolling bearing fault diagnosis methods based on the Case Western Reserve University bearing dataset were carried out.Experimental results showed that the fault diagnosis accuracy of the proposed method was improved by 7.45,4.46 and 1.53 percentage points,respectively,and the convergence speed of CNN was faster compared with the traditional fault diagnosis methods based on CNN,the fast Fourier transform with CNN,the long short-term memory with CNN.In the generalization task for different working conditions,the proposed method had an average accuracy of 99.64%,demonstrating superior accuracy and generalisability.