基于多尺度量子熵的中介轴承故障诊断方法

Inter-shaft bearing fault diagnosis method based on multi-scale quantum entropy

针对中介轴承故障信号传递路径复杂,故障信号特征微弱诊断困难的问题,提出一种基于多尺度量子熵(MQE)、局部线性嵌入算法(LLE)与概率神经网络(PNN)的故障诊断方法。该方法采用空域相关降噪对振动信号进行滤波降噪,提高信号的信噪比;利用MQE提取中介轴承故障特征信息;采用LLE方法对高维特征进行降维处理;将低维故障特征输入PNN中进行故障识别。搭建了中介轴承故障模拟试验台,模拟中介轴承正常、内圈故障、外圈故障和滚动体故障,并采集数据对本文建立的中介轴承故障诊断算法进行验证。试验结果表明:提出的中介轴承故障诊断方法能够有效识别中介轴承故障类型,且没有出现过拟合现象,并表现出良好的泛化能力。

Targeting at the problem of complex transmission path of inter-shaft bearing fault signal,weak fault signal characteristics and difficulty in fault feature extraction,a fault diagnosis method based on Multi-scale Quantum Entropy(MQE),Locally Linear Embedding(LLE)algorithm and Probabilistic Neural Network(PNN)is proposed in this paper.Firstly,the inter-shaft bearing fault signals are denoised through the spatial correlation noise reduction method to improve the signal to noise ratio.Secondly,MQE is utilized to extract the features of inter-shaft bearings.Then,LLE is utilized to reduce and fuse high-dimensional fault features of multi-sensor to construct fault samples.Finally,the low-dimensional fault features are input into PNN multi-fault classifier for fault identification.The fault simulation test bench of the inter-shaft bearing is built to simulate the normal bearing,inner ring fault,outer ring fault and rolling element fault,and the data were collected to verify the MQE-LLE-PNN inter-shaft bearing fault diagnosis algorithm established in this paper.The experimental results validate that the proposed method can effectively identify the inter-shaft bearing fault,and shows good generalization ability without any over-fitting phenomenon.