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

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