An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks DOI Creative Commons

Xianglong Luo,

Fengrong Yu, Jing Qian

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4338 - 4338

Опубликована: Апрель 14, 2025

To address the issue of rolling bearing fault diagnosis, this paper proposes a novel model combining Improved Gorilla Troop Optimization (IGTO) algorithm, Variational Mode Decomposition (VMD), Permutation Entropy (PE), and Long Short-Term Memory (LSTM) networks. The IGTO algorithm is used to optimize parameters VMD LSTM, enhancing signal decomposition feature extraction. proposed achieves classification accuracies 96.67% 98.96% in testing training phases, respectively, on Case Western Reserve University dataset, with minimal accuracy fluctuations. Furthermore, Jiangnan reaches an average 98.85%, highest reaching 99.48%. results also demonstrate high stability, as indicated by low standard deviations (1.2148 1.3217) narrow 95% confidence intervals ([95.75%, 97.58%] [96.73%, 97.49%]). Despite longer runtime 13.88 s per sample, model’s superior justifies computational cost. These excellent diagnostic performance, adaptability different datasets, practical applicability for diagnosis. This approach provides valuable reference predictive maintenance detection systems industrial applications.

Язык: Английский

An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks DOI Creative Commons

Xianglong Luo,

Fengrong Yu, Jing Qian

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4338 - 4338

Опубликована: Апрель 14, 2025

To address the issue of rolling bearing fault diagnosis, this paper proposes a novel model combining Improved Gorilla Troop Optimization (IGTO) algorithm, Variational Mode Decomposition (VMD), Permutation Entropy (PE), and Long Short-Term Memory (LSTM) networks. The IGTO algorithm is used to optimize parameters VMD LSTM, enhancing signal decomposition feature extraction. proposed achieves classification accuracies 96.67% 98.96% in testing training phases, respectively, on Case Western Reserve University dataset, with minimal accuracy fluctuations. Furthermore, Jiangnan reaches an average 98.85%, highest reaching 99.48%. results also demonstrate high stability, as indicated by low standard deviations (1.2148 1.3217) narrow 95% confidence intervals ([95.75%, 97.58%] [96.73%, 97.49%]). Despite longer runtime 13.88 s per sample, model’s superior justifies computational cost. These excellent diagnostic performance, adaptability different datasets, practical applicability for diagnosis. This approach provides valuable reference predictive maintenance detection systems industrial applications.

Язык: Английский

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