Mechanical Fault Prediction Based on Event Knowledge Graph

Li He, Liang Zhang, Wei Yan

2023

Abstract

Currently, the majority of diagnoses in the field of mechanical faults are performed by experts or expert systems, which require domain experts to guide the completion while having subpar and limited portability. Consequently, we analyzed the current situation of rolling bearing fault, event knowledge graph and convolutional neural network, explored the intelligent fault diagnosis and prediction technology of rolling bearing, introduced event knowledge graph and convolutional neural network in rolling bearing fault diagnosis, provided support for fault diagnosis and prediction, enhanced the accuracy of fault diagnosis and prediction, and enabled the predictive of complex mechanical equations.

Paper Citation

in Harvard Style

He L., Zhang L. and Yan W. (2023). Mechanical Fault Prediction Based on Event Knowledge Graph. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-671-2, SciTePress, pages 164-173. DOI: 10.5220/0012178200003598

in Bibtex Style

@conference{keod23,
author={Li He and Liang Zhang and Wei Yan},
title={Mechanical Fault Prediction Based on Event Knowledge Graph},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2023},
pages={164-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012178200003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - Mechanical Fault Prediction Based on Event Knowledge Graph
SN - 978-989-758-671-2
AU - He L.
AU - Zhang L.
AU - Yan W.
PY - 2023
SP - 164
EP - 173
DO - 10.5220/0012178200003598
PB - SciTePress