Physics-driven feature alignment combined with dynamic distribution adaptation for three-cylinder drilling pump cross-speed fault diagnosis

Menghang Dai, Zhiliang Liu, Jinrui Wang and Mingjian Zuo

Reliability Engineering and System Safety, 2024, vol. 251, issue C

Abstract: Monitoring the health of drilling pumps and diagnosing faults is crucial for the smooth operation of oil drilling activities. However, existing deep learning algorithms struggle with varying speed conditions. To address this, we propose a method that combines physics-driven feature alignment with Dynamic Distribution Adaptation (DDA) for diagnosing drilling pump faults across varying speeds. Our physics-driven feature alignment method reduces discrepancies between samples by aligning amplitude values, angular sampling frequencies, and impulse phases of signals. DDA further improves cross-domain feature matching by dynamically adjusting marginal and conditional distributions during domain adaptation. Experiments on a three-cylinder drilling pump platform under varying speed conditions demonstrate the superior performance of our method compared to state-of-the-art approaches.

Keywords: Drilling pump; Physics-driven feature alignment; Transfer learning; Dynamic distribution adaptation; Fault diagnosis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:251:y:2024:i:c:s0951832024004411

DOI: 10.1016/j.ress.2024.110369

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