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Equipment Health Assessment Based on Node Embedding

EasyChair Preprint no. 9841

13 pagesDate: March 7, 2023


Equipment health assessment is a fundamental task in predictive equipment maintenance practice, which aims to predict the health of equipment based on information about the equipment and its operation, thus avoiding unexpected equipment failures. In the current context, equipment health assessment based on sequential deep learning methods is becoming more and more popular, however, such methods ignore the inter-device correlations, leading to their lack of readiness for health assessment of a large number of devices. To address this problem, this paper proposes a node-embedding-based device health assessment method, which creatively introduces a graph model for device health assessment and effectively improves the performance of health assessment. Firstly, this paper proposes a way to define equipment association graphs. Secondly, we introduce the node embedding technique to extract graph information. Finally, an equipment health assessment method based on the equipment association graph is proposed. Experiments show that the proposed method outperforms the existing prevailing methods.

Keyphrases: Association graph, health assessment, node embedding

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Jian Li and Xiao Chen and Chao Zhang and Hao Wu and Xin Yu and Shiqi Liu and Haolei Wang},
  title = {Equipment Health Assessment Based on Node Embedding},
  howpublished = {EasyChair Preprint no. 9841},

  year = {EasyChair, 2023}}
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