A fine-tuning deep learning framework to palliate data distribution shift effects in rotary machine fault detection

Abstract

In the condition monitoring of rotating systems, overfitting is a common challenge due to limited data history, which reduces the effectiveness of fault detection frameworks; this limitation often leads to unreliable diagnostics, resulting in unexpected machine failures and increased operational costs in industrial applications. Advances in deep learning suggest using simulated data to address this issue, but operational variabilities still cause significant data distribution shifts, affecting model accuracy. This article presents a new vibration-based monitoring framework that improves fault detection in rotary machines by effectively managing these shifts. It features a novel fine-tuning approach within sequential domain adaptation, requiring only a limited number of observations from the target domain for accurate model adjustment. The domain adaptation process is elucidated through a novel visualization of internal activation patterns within the sequential network. This method is further enhanced by a hybrid algorithm that combines wavelet transformation, a multi-layer perceptron, and a transformer encoder, followed by domain-specific fine-tuning. The framework’s effectiveness is demonstrated through experimental data from two different rotor systems, validated by sensitivity and comparative analyses, highlighting its robustness, generalizability, and practical applicability as a baseline in industrial fault detection scenarios.