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(...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.展开更多
The boundedness on Triebel-Lizorkin and Lebesgue spaces of the multilinear operators associated to some singular integral operators satisfying a variant of Hörmander’s condition are obtained.
基金Supported by the National Natural Science Foundation of China(No.62203390).
文摘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.
基金supported by the National Natural Science Foundation of China(Grant No.11901126)the Natural Science Foundation of Hunan Province(No.2021JJ30630)the Scientific Research Funds of Hunan Provincial Education Department(Grant No.19B509).
文摘The boundedness on Triebel-Lizorkin and Lebesgue spaces of the multilinear operators associated to some singular integral operators satisfying a variant of Hörmander’s condition are obtained.
