基于深度学习特征提取和WOA-SVM状态识别的轴承故障诊断

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振动与冲击 ›› 2019, Vol. 38 ›› Issue (10) : 31-37.

论文

赵春华1, 2 胡恒星2 陈保家1, 2 张毅娜2 肖嘉伟2

作者信息 +

Bearing fault diagnosis based on the deep learning feature extraction and WOA SVM state recognition

ZHAO Chunhua HU Hengxing CHEN Baojia ZHANG Yina XIAO Jiawei

Author information +

文章历史 +

摘要

针对滚动轴承故障诊断问题，利用深度学习神经网络、鲸鱼优化算法（Whale optimization algorithm，WOA）和支持向量机(Support vector machine,SVM)等技术，提出了一种基于深度学习特征提取和WOA-SVM状态识别相结合的故障诊断模型。先通过深度学习自适应提取故障频谱特征，并将其与数理统计方法提取的时域特征相融合，再通过WOA-SVM对融合后的联合特征进行故障诊断。该模型在对滚动轴承试验台的故障诊断中实现了不同工况下多种故障类型的可靠识别，并且在一定程度上提高了故障分类的准确性。为了验证WOA-SVM在深度学习提取特征的轴承故障识别中的可行性和有效性，对比了粒子群支持向量机和遗传支持向量机，结果表明WOA-SVM具有较高的收敛精度和收敛速度。

Abstract

For the fault diagnosis of rolling bearings, a fault diagnosis model based on the deep learning feature extraction and WOA-SVM state recognition was proposed.The fault frequency domain feature was extracted by the depth learning adaptive method, and then it was fused with the time domain feature extracted by the mathematical statistics method.The fused joint features were used in diagnosis through the processing of WOA-SVM.By the model, it has realized the reliable identification of various fault types of rolling bearings under different working conditions on a test bench and improves the accuracy of fault classification to a certain extent.In order to verify the feasibility and effectiveness of bearing fault identification based on WOA-SVM, the diagnosis results were compared with those by the PSO-SVM and GA-SVM.The results show that WOA-SVM has higher convergence accuracy and convergence speed.

导出引用

赵春华1, 2 胡恒星2 陈保家1, 2 张毅娜2 肖嘉伟2. 基于深度学习特征提取和WOA-SVM状态识别的轴承故障诊断[J]. 振动与冲击, 2019, 38(10): 31-37

ZHAO Chunhua HU Hengxing CHEN Baojia ZHANG Yina XIAO Jiawei. Bearing fault diagnosis based on the deep learning feature extraction and WOA SVM state recognition[J]. Journal of Vibration and Shock, 2019, 38(10): 31-37

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