Semi-Supervised Informer for the Compound Fault Diagnosis of Industrial Robots.
Sensors (Basel)
; 24(12)2024 Jun 08.
Article
in En
| MEDLINE
| ID: mdl-38931516
ABSTRACT
The increasing deployment of industrial robots in manufacturing requires accurate fault diagnosis. Online monitoring data typically consist of a large volume of unlabeled data and a small quantity of labeled data. Conventional intelligent diagnosis methods heavily rely on supervised learning with abundant labeled data. To address this issue, this paper presents a semi-supervised Informer algorithm for fault diagnosis modeling, leveraging the Informer model's long- and short-term memory capabilities and the benefits of semi-supervised learning to handle the diagnosis of a small amount of labeled data alongside a substantial amount of unlabeled data. An experimental study is conducted using real-world industrial robot monitoring data to assess the proposed algorithm's effectiveness, demonstrating its ability to deliver accurate fault diagnosis despite limited labeled samples.
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01-internacional
Database:
MEDLINE
Language:
En
Journal:
Sensors (Basel)
Year:
2024
Document type:
Article
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