Non-intrusive anomaly detection for carving machine systems based on CAE-GMHMM under multiple working conditions  

作　　者：QIU Xiang CHEN Wei WU Qi HU Fo LU Kangdi 仇翔

机构地区：[1]College of Information Engineering,Zhejiang University of Technology,Hangzhou 310014,P.R.China [2]College of Information Sciences and Technology,Donghua University,Shanghai 201620,P.R.China

出　　处：《High Technology Letters》2025年第1期1-11,共11页高技术通讯(英文版)

基　　金：Supported by the National Natural Science Foundation of China(No.62203390).

摘　　要：This paper is concerned with a non-intrusive anomaly detection method for carving machine systems with variant working conditions,and a novel unsupervised detection framework that integrates convolutional autoencoder(CAE)and Gaussian mixture hidden Markov model(GMHMM)is proposed.Firstly,the built-in sensor information under normal conditions is recorded,and a 1D convolutional autoencoder is employed to compress high-dimensional time series,thereby transforming the anomaly detection problem in high-dimensional space into a density estimation problem in a latent low-dimensional space.Then,two separate estimation networks are utilized to predict the mixture memberships and state transition probabilities for each sample,enabling GMHMM to handle low-dimensional representations and multi-condition information.Furthermore,a cost function comprising CAE reconstruction and GMHMM probability assessment is constructed for the low-dimensional representation generation and subsequent density estimation in an end-to-end fashion,and the joint optimization effectively enhances the anomaly detection performance.Finally,experiments are carried out on a self-developed multi-axis carving machine platform to validate the effectiveness and superiority of the proposed method.

关 键 词：non-intrusive detection variant working condition rotating machinery motion control system hidden Markov model(HMM) 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]