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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Egile Nagusiak:	X. F. Tang, Y. B. Long
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	Croatian Society for Materials Protection 2024-01-01
Saila:	Metalurgija
Gaiak:	bearing ball fault detection mechanical vibration Bi-LSTM optimization algorithm
Sarrera elektronikoa:	https://hrcak.srce.hr/file/456150
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