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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.
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.
This paper proposes a knowledge-based input configuration to inform deep learning models for both electrical and mechanical fault diagnosis, rather than increasing model complexity, and confirms that, while conventional feature processing techniques perform well for electrical fault diagnosis, only the proposed FFT-inf...
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