时间序列信号的机器学习验证,以减少维护,优化和自动化数字算法中的错误

Gustavo Sánchez
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引用次数: 0

摘要

本文的目的是展示动态机器学习建模如何帮助钻井人员和操作人员验证来自传感器的信号。当涉及到数字化时,数据和信号质量是行业中的一个大问题。该方法将展示拥有验证管道的重要性,以及它如何帮助其他算法做出更好的决策。我们的方法使用统计原理、机器学习和高级分析。该方法符合ISO 8000标准,可以为公司提供数据管理和数据质量的框架。根据应用的不同,我们方法的准确性会有所不同。结果准确度在88%到99%之间。该工艺已由一家主要钻井承包商在防喷、动态定位系统和起下钻等信号方面进行了验证。该过程可以节省50%以上用于清洗、绘图和验证传感器信号的时间。最终产品允许用户理解数据收集系统中的问题,从传感器一直到企业历史记录。它还将减少在维护、优化和自动化中出现的误报和误报。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Validation of Time Series Signals to Reduce Mistakes in Digital Algorithms for Maintenance, Optimization, and Automation
The objective of the paper is to show how dynamic machine learning modeling can help drillers and operators validate signals from their sensors. Data and signal quality are a big problem in the industry when it comes to digitization. The method will show the importance of having a validation pipeline, and how it can help other algorithms make better decisions. Our approach uses statistical principles, machine learning and advanced analytics. The method is ISO 8000 compliant and can provide a framework in data management and data quality for companies to use. Depending on the application the accuracy of our method will vary. Results are anywhere in the 88% - 99% range of accuracy. The process has been validated by a major drilling contractor in signals ranging from blow out prevention, dynamic positioning systems, and tripping. The process can save upwards of 50% of time spent cleaning, mapping, and validating sensor signals. The end product allows the user to understand problems in the data collection system from the sensor all the way to the enterprise historian. It will also reduce false positives and false negative that are present in maintenance, optimization, and automation.
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