A knowledge-informed warm-start autoencoder that enables efficient adaptation of a pre-trained model to a new bearing with improved accuracy and fair computational effort is proposed, focusing on early fault detection and remaining useful life (RUL) estimation.
Results show that using statistical vibration features with ensemble classifiers is a good way to diagnose multi-class bearing faults and establishes a comprehensive benchmark for ML- and DL-based rolling bearing FDD.
M. I. Quamar, Abdulrazaq Nafiu Abubakar, Ali Nasir· Journal of Vibration Enginee...· 0 citations
TitanDiag, a recently proposed architecture for long-context language modelling, tackles a similar challenge of maintaining performance across varying contexts by adapting this mechanism to fault diagnosis for rolling bearings.
Bingcong Li· Advances in Engineering Inno...· 0 citations
This study presents a healthy-only bearing fault detection framework in which a stacked long short-term memory (LSTM) predictor learns normal vibration dynamics through multi-step forecasting, and deviations between predicted and observed vibration sequences are used for residual-based anomaly detection. The methodolog...
Syed Sajjad Haider Zaidi, Alex Shenfield, Hongwei Zhang et al.· Electronics· 0 citations
A transferred SISA (Sharded, Isolated, Sliced, and Aggregated) fault diagnosis framework is developed and applied to rolling bearing data, demonstrating a 84.32% decrease in retraining time compared to non-SISA full-retraining while restoring accuracy to the pre-poisoning SISA level.
Emily Yin, Jing-Yi Yan, Nanhong Liu et al.· 0 citations
Remaining useful life (RUL) prediction of rolling bearings is essential for ensuring the safe operation and condition-based maintenance of rotating machinery. To address unreliable degradation-onset identification and insufficient joint modeling of trend and detail components in non-stationary degradation signals, this...
The proposed framework improves RUL prediction accuracy and provides competitive cross-bearing generalization and comprehensive experiments show that the proposed framework improves RUL prediction accuracy and provides competitive cross-bearing generalization.
Zhuo-Heng Dai, Lei Jiang, Liang Peng et al.· Frontiers of Computer Scienc...· 0 citations
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