Risk Level Estimation for Electronics Boards in Drilling and Measurement Tools Based on the Hidden Markov Model

Jinlong Kang, C. Varnier, A. Mosallam, N. Zerhouni, Fares Ben Youssef, Na-Na Shen
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引用次数: 1

Abstract

The electronic boards in drilling and measurement (D&M) tools provide multiple functions, such as data acquisition, signal processing, operation control, and data storage. However, due to the harsh downhole operating conditions; i.e., high temperature, dynamic vibration, and extensive shocks, the boards are likely to suffer from complex failure modes and result in failed jobs. Estimating the risk level of the boards can tolerate and provide support for maintenance decision making and job planning, this paper presents a statistical method for risk assessment of the electronic boards. The method first selects relevant channels from D&M tool measurement data and extracts histogram features based on those selected channels. The histogram features are then enhanced based on a linear interpolation method and aggregated using weighted sum. Finally, hidden Markov models (HMMs) with different parameter settings are trained using the processed features. The best HMM is chosen according to the Akaike information criterion and Bayesian information criterion. The proposed HMM-based method is tested on a real-world data set of failed control processing unit boards that were assembled for a specific D&M tool. The experimental results show that this method is effective in estimating the risks as a sequence of events, which in turn, helps to achieve consistent risk estimation. The work presented in this paper is also part of a long-term project with the aim to construct a risk-based decision advisor for D&M tools used in the oil and gas industry.
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基于隐马尔可夫模型的钻测工具电子电路板风险水平估计
D&M工具中的电子板具有数据采集、信号处理、操作控制和数据存储等多种功能。然而,由于井下作业条件恶劣;例如,高温,动态振动和广泛的冲击,电路板可能遭受复杂的失效模式并导致失败的工作。评估电路板的风险水平可以为维护决策和作业计划提供支持,本文提出了一种电子电路板风险评估的统计方法。该方法首先从D&M工具测量数据中选择相关通道,并在此基础上提取直方图特征。然后基于线性插值方法增强直方图特征,并使用加权和进行聚合。最后,利用处理后的特征训练不同参数设置的隐马尔可夫模型(hmm)。根据赤池信息准则和贝叶斯信息准则选择最佳HMM。提出的基于hmm的方法在为特定D&M工具组装的故障控制处理单元板的真实数据集上进行了测试。实验结果表明,该方法可以有效地将风险作为事件序列进行估计,从而有助于实现一致性的风险估计。本文介绍的工作也是一个长期项目的一部分,该项目旨在为石油和天然气行业中使用的D&M工具构建基于风险的决策顾问。
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