An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis

An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis

To address the challenge of extracting fault features and accurately identifying bearing fault conditions under strong noisy environments, a rolling bearing failure diagnostic technique is presented that utilizes parameter-optimized maximum second-order cyclostationary blind deconvolution (CYCBD) an...

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Bibliographic Details
Main Authors: Jixin Liu, Liwei Deng, Yue Cao, Chenglin Wen, Zhihuan Song, Mei Liu, Xiaowei Cui
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Sensors
Subjects:
fault diagnosis
maximum second-order cyclostationarity blind deconvolution (CYCBD)
bidirectional long short-term memory (BiLSTM)
golden jackal optimization (GJO)
Online Access:https://www.mdpi.com/1424-8220/25/5/1495
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