Metallurgy, Vol. 63 No. 3-4, 2024.
Original scientific paper
Integration of gradient least mean squares in bidirectional long short-term (LSTM) memory networks for metallurgical bearing ball fault diagnosis
X. F. Tang
; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China
*
Y. B. Long
; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China
* Corresponding author.
Abstract
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.
Keywords
bearing; ball fault detection; mechanical vibration; Bi-LSTM; optimization algorithm
Hrčak ID:
315686
URI
Publication date:
1.7.2024.
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