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

Autor:	X. F. Tang, Y. B. Long
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	bearing ball fault detection mechanical vibration Bi-LSTM optimization algorithm Mining engineering. Metallurgy TN1-997
Zdroj:	Metalurgija, Vol 63, Iss 3-4, Pp 403-406 (2024)
Druh dokumentu:	article
ISSN:	0543-5846 1334-2576
Popis:	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 identification of incipient bearing ball failures at early stages, thus markedly improving prediction accuracy. Our empirical results underscore the superior performance of this composite methodology in accurately detecting a spectrum of five mechanical bearing ball failure types, achieving a substantial enhancement in diagnostic precision.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/8ed99ed9b1ea41cdb7d493f5b318da8b Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF