| QIU Xiang*(仇 翔),CHEN Wei*,WU Qi*,HU Fo*,LU Kangdi**.[J].高技术通讯(英文),2025,31(1):1~11 |
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| Non-intrusive anomaly detection for carving machine systems based on CAE-GMHMM under multiple working conditions |
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| DOI：10. 3772 / j. issn. 1006-6748. 2025. 01. 001 |
| 中文关键词: |
| 英文关键词: non-intrusive detection, variant working condition, rotating machinery, motioncontrol system, hidden Markov model (HMM) |
| 基金项目: |
| Author Name | Affiliation | | QIU Xiang*(仇 翔) | (*College of Information Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China)
(**College of Information Sciences and Technology, Donghua University, Shanghai 201620, P. R. China) | | CHEN Wei* | | | WU Qi* | | | HU Fo* | | | LU Kangdi** | |
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| 中文摘要: |
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| 英文摘要: |
| 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. |
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