Scholarlink

<p>This study proposes a machine learning-based framework for predictive maintenance and quality control of drilling mud pumps, targeting critical operational challenges such as equipment failure, downtime, and high maintenance costs. The methodology involves using a dataset of 820 time-indexed operational records comprising historical maintenance logs, sensor readings, and operational parameters. Four supervised learning models were applied: Random Forest (RF), Multiclass Logistic Regression (MCLR), Feedforward Neural Network (FNN), and Long Short-Term Memory (LSTM). Although each record contains static measurements, the dataset is chronologically ordered by cumulative operating time and failure events, enabling the construction of temporal input sequences for sequence-based learning models. Data preprocessing included imputing missing values, normalization, and categorical encoding. Performance evaluation was conducted using stratified k-fold cross-validation across training, validation, and test sets to mitigate overfitting and preserve failure-class distributions. The results show that among the models, LSTM achieved the highest predictive accuracy (RMSE = 0.7273; R² = 0.9153), demonstrating superior capability in capturing temporal degradation patterns associated with drilling mud pump failures. These findings contribute to the development of intelligent maintenance systems in oilfield operations, offering a scalable, data-driven approach to improve pump performance and operational sustainability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning insight in quality control and maintenance management of drilling mud pumps

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月28日

查看全文
  • Xiaoming Wu,
  • Jingxuan Chang,
  • Chengcheng Long,
  • Hamzeh Ghorbani

摘要

This study proposes a machine learning-based framework for predictive maintenance and quality control of drilling mud pumps, targeting critical operational challenges such as equipment failure, downtime, and high maintenance costs. The methodology involves using a dataset of 820 time-indexed operational records comprising historical maintenance logs, sensor readings, and operational parameters. Four supervised learning models were applied: Random Forest (RF), Multiclass Logistic Regression (MCLR), Feedforward Neural Network (FNN), and Long Short-Term Memory (LSTM). Although each record contains static measurements, the dataset is chronologically ordered by cumulative operating time and failure events, enabling the construction of temporal input sequences for sequence-based learning models. Data preprocessing included imputing missing values, normalization, and categorical encoding. Performance evaluation was conducted using stratified k-fold cross-validation across training, validation, and test sets to mitigate overfitting and preserve failure-class distributions. The results show that among the models, LSTM achieved the highest predictive accuracy (RMSE = 0.7273; R² = 0.9153), demonstrating superior capability in capturing temporal degradation patterns associated with drilling mud pump failures. These findings contribute to the development of intelligent maintenance systems in oilfield operations, offering a scalable, data-driven approach to improve pump performance and operational sustainability.