arXiv subjects

Xiuli Zhu

Publications and source records attributed to Xiuli Zhu.

4 recordsLinked to original sources

arXiv2026

Contrastive Siamese Representation Learning for Predictive Maintenance of Electrical Submersible Pumps

Electrical submersible pumps (ESPs) are essential in offshore oil production, where unexpected failures can result in significant operational and financial losses. Accurate predictive maintenance for ESP systems remains challenging due to nonlinear operating conditions, class imbalance, and variability among pump units. To address these issues, this study presents a fault diagnosis framework that incorporates class imbalance awareness by employing Siamese contrastive representation learning and prior-corrected k-nearest neighbor (KNN) classification. The method first extracts discriminative features relevant to fault detection from vibration-domain indicators and engineered harmonic relationships. A Siamese neural network is trained with class-balanced contrastive pairs to construct an embedding space that clusters samples of the same fault type and separates different fault classes. To further mitigate class imbalance during classification, a prior-corrected distance-weighted KNN is applied. The framework is validated using a Leave-One-ESP-Out (LOEO) strategy to evaluate generalization to previously unseen ESP units. Experimental results indicate that the proposed framework delivers robust and consistent fault classification performance under realistic industrial conditions, supporting its potential for reliable predictive maintenance and intelligent ESP system monitoring.

cs.LG↗

arXiv2026

Retrieval-based and Fine-tuned LLM Approaches for Industrial Asset Health Monitoring and Decision Support

Industrial plants run many important machines such as pumps, turbines, and compressors. Although engineers can use their experience to identify and diagnose machine problems, transferring this reasoning ability to computer systems remains difficult. This work studies how well a retrieval-only method and an open-source large language model (LLM) perform failure-sensor diagnostic reasoning using the FailureSensorIQ benchmark, a multiple-choice question-answering task introduced by IBM Research. In the retrieval-only approach, each answer option is converted into an option-level query and scored using similar correct and incorrect records from the training data. TF-IDF, BM25, semantic search, and hybrid search are tested and compared. In the LLM-based approach, the Qwen2.5-7B-Instruct model is evaluated using zero-shot prompting, few-shot prompting, and QLoRA fine-tuning. The results show that semantic search and hybrid search perform better than pure keyword-matching techniques, indicating that meaning-based similarity is more important for industrial failure-sensor reasoning. Among the LLM-based methods, the fine-tuned model achieves the best performance and substantially improves over zero-shot and few-shot prompting. Error analysis shows that performance decreases as the number of answer options increases. Robustness analysis also shows that all methods are sensitive to option shuffling, changed labels, paraphrasing, and additional distractors.

cs.IR↗

arXiv2025

Explainable Probabilistic Machine Learning for Predicting Drilling Fluid Loss of Circulation in Marun Oil Field

Lost circulation remains a major and costly challenge in drilling operations, often resulting in wellbore instability, stuck pipe, and extended non-productive time. Accurate prediction of fluid loss is therefore essential for improving drilling safety and efficiency. This study presents a probabilistic machine learning framework based on Gaussian Process Regression (GPR) for predicting drilling fluid loss in complex formations. The GPR model captures nonlinear dependencies among drilling parameters while quantifying predictive uncertainty, offering enhanced reliability for high-risk decision-making. Model hyperparameters are optimized using the Limited memory Broyden Fletcher Goldfarb Shanno (LBFGS) algorithm to ensure numerical stability and robust generalization. To improve interpretability, Local Interpretable Model agnostic Explanations (LIME) are employed to elucidate how individual features influence model predictions. The results highlight the potential of explainable probabilistic learning for proactive identification of lost-circulation risks, optimized design of lost circulation materials (LCM), and reduction of operational uncertainties in drilling applications.

cs.LG↗

arXiv2023

Estimation of minimum miscibility pressure (MMP) in impure/pure N2 based enhanced oil recovery process: A comparative study of statistical and machine learning algorithms

Minimum miscibility pressure (MMP) prediction plays an important role in design and operation of nitrogen based enhanced oil recovery processes. In this work, a comparative study of statistical and machine learning methods used for MMP estimation is carried out. Most of the predictive models developed in this study exhibited superior performance over correlation and predictive models reported in literature.

cs.LG↗