Integration of gradient least mean squares in bidirectional long short-term (LSTM) memory networks for metallurgical bearing ball fault diagnosis

Integration of gradient least mean squares in bidirectional long short-term (LSTM) memory networks for metallurgical bearing ball fault diagnosis

This paper introduces a novel diagnostic approach for bearing ball failures: a synergistic implementation of a bidirectional Long Short-Term Memory (LSTM) network, empowered by Gradient Minimum Mean Square. This method leverages deep analysis of operational data from bearings, enabling the precise i...

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Bibliographic Details
Main Authors: X. F. Tang, Y. B. Long
Format: Article
Language:English
Published: Croatian Metallurgical Society 2024-01-01
Series:Metalurgija
Subjects:
bearing
ball fault detection
mechanical vibration
Bi-LSTM
optimization algorithm
Online Access:https://hrcak.srce.hr/file/456150

Internet

https://hrcak.srce.hr/file/456150

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