基于FSC-MPE与BP神经网络的滚动轴承故障诊断方法

Rolling bearing fault diagnosis method based on FSC-MPE and BP neural network

[目的]提出一种从强背景噪声、非平稳、非线性的复杂设备滚动轴承早期冲击故障振动信号中有效提取故障特征并进行故障模式识别的方法。[方法]首先,利用快速谱相关(FSC)分析提取原始振动信号的故障特征,并利用多尺度排列熵(MPE)对故障特征进行量化;然后,将故障特征数据输入BP神经网络进行故障诊断模型训练与测试;最后,对变速情况下的滚动轴承故障模拟实验数据和美国凯斯西储大学公开的轴承故障试验数据集进行故障识别研究。[结果]结果显示:所提方法对不同类型的故障具有较高的辨识精度,可达97%以上。[结论]研究验证了基于FSC-MPE与BP神经网络的滚动轴承故障诊断方法的可行性和优越性,可为滚动轴承健康状态评估提供技术支持。

[Objectives]This paper proposes a method for effectively extracting fault features and identifying fault patterns from the early impact vibration signals of the rolling bearings of complex equipment which is non-stationary,nonlinear and has strong background noise.[Methods]First,the fault features of the original vibration signals are extracted via fast spectral correlation analysis and quantified via multi-scale permutation entropy(FSC-MPE).The fault feature data is then input into a BP neural network for fault diagnosis model training and testing.Finally,fault identification research is carried out on the rolling bearing fault simulation experimental data under variable speed and the public bearing fault test dataset of Case Western Reserve University.[Results]The results show that the proposed method has high identification accuracy for different types of faults,reaching more than 97%.[Conclusions]The feasibility and superiority of the proposed rolling bearing fault diagnosis method based on FSC-MPE and BP neural network are verified,and it can provide technical support for rolling bearing health evaluation.