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
Description
Summary: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.
ISSN:0543-5846
1334-2576

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