Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises

Shen Yan, Haidong Shao, Yimin Xiao, Bin Liu, Jiafu Wan

Research output: Contribution to journalArticlepeer-review

137 Citations (Scopus)
60 Downloads (Pure)

Abstract

Anomaly detection of machine tools plays a vital role in the machinery industry to sustain efficient operation and avoid catastrophic failures. Compared to traditional machine learning and signal processing methods, deep learning has greater adaptive capability and end-to-end convenience. However, challenges still exist in recent research in anomaly detection of machine tools based on deep learning despite the marvelous endeavors so far, such as the necessity of labeled data for model training and insufficient consideration of noise effects. During machine operation, labeled data is often difficult to obtain; the collected data contains varying degrees of noise disturbances. To address the above challenges, this paper develops a hybrid robust convolutional autoencoder (HRCAE) for unsupervised anomaly detection of machine tools under noises. A parallel convolutional distribution fitting (PCDF) module is constructed, which can effectively fuse multi-sensor information and enhance network robustness by training in parallel to better fit the data distribution with unsupervised learning. A fused directional distance (FDD) loss function is designed to comprehensively consider the distance and angle differences among the data, which can effectively suppress the influence of noises and further improve the model robustness. The proposed method is validated by real computer numerical control (CNC) machine tool data, obtaining better performance of unsupervised anomaly detection under different noises compared to other popular unsupervised improved autoencoder methods.
Original languageEnglish
Article number102441
Number of pages12
JournalRobotics and Computer-Integrated Manufacturing
Volume79
Early online date19 Aug 2022
DOIs
Publication statusPublished - 1 Feb 2023

Keywords

  • machine tools
  • deep learning
  • unsupervised anomaly detection
  • hybrid robust convolutional autoencoder
  • noises

Fingerprint

Dive into the research topics of 'Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises'. Together they form a unique fingerprint.

Cite this