首页|期刊导航|Petroleum|A clustering-guided feature enhancement and Bayesian-optimized LSTM approach for LC temporal anomaly detection

A clustering-guided feature enhancement and Bayesian-optimized LSTM approach for LC temporal anomaly detection

Yating Lei He Zhang Haibo Liang Yihui Han Jialing Zou

Petroleum2026，Vol.12Issue(3)：P.509-522,14.

Petroleum2026，Vol.12Issue(3)：P.509-522,14.DOI:10.1016/j.petlm.2026.04.004

A clustering-guided feature enhancement and Bayesian-optimized LSTM approach for LC temporal anomaly detection

Yating Lei ¹He Zhang ¹Haibo Liang ¹Yihui Han ²Jialing Zou¹

作者信息

1. Southwest Petroleum University,Chengdu 610500,China
2. The 10th Research Institute of China Electronics Technology Group Corporation,Chengdu,610036,China Agile and Intelligent Computing Key Laboratory of Sichuan Province,Chengdu,610036,China
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摘要

关键词

LC early warning/Long short-term memory network/Bayesian optimization/Clustering/Feature enhancement

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Yating Lei,He Zhang,Haibo Liang,Yihui Han,Jialing Zou..A clustering-guided feature enhancement and Bayesian-optimized LSTM approach for LC temporal anomaly detection[J].Petroleum,2026,12(3):P.509-522,14.

基金项目

supported by the National Natural Science Foundation of China(Grant No.52404005) （Grant No.52404005）

the Innovation Research Community Project of the Natural Science Foundation of Sichuan Province(Grant No.2024NSFTD0053). （Grant No.2024NSFTD0053）

Petroleum

ISSN：2405-6561

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