关键词: 异常监测, 卷积变分自编码器, 核密度估计, 滚珠丝杠副, 退化阶段识别

Abstract: To solve the fault data restriction problem in the ball screw condition monitoring,a ball screw anomaly monitoring method was proposed based on the Convolutional Variational Autoencoder (CNVAE) and the dynamic kernel density estimation model.The one-dimensional convolutional neural network was used to construct the variational autoencoder model,and it was trained by the normal vibration data of the ball screw collected in the early stage.Hence,a CNVAE model capable of reconstructing the normal samples could be learned.Then,the real-time signals were input into the CNVAE model,and the reconstruction error could be obtained,which was used as a health indicator to represent the degradation state of the ball screw.With a sliding window on the time scale,the reconstruction errors in different time periods were input into the kernel density estimation model,and the ball screw anomaly could be automatically detected through observing the probability distribution variation of the reconstruction errors in the sliding window.Experimental results showed that the proposed method could recognize the ball screw degradation stage effectively,characterize the deterioration evolution process and detect the abnormality earlier than traditional methods.

Key words: anomaly monitoring, convolutional variational autoencoder, kernel density estimation, ball screw, degradation stage identification