管理用于移动设备预测的流式传感器数据

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
T. Griffiths, Débora C. Corrêa, M. Hodkiewicz, A. Polpo
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引用次数: 4

摘要

摘要从重型移动设备上的传感器无线传输数据的能力为主动评估资产状况提供了机会。然而,由于数据的大小和结构,数据分析方法的应用具有挑战性,其中包含不一致和异步的条目,以及大量的数据丢失。目前的方法通常需要现场工程师的专业知识来为变量选择提供信息。在这项工作中,我们开发了一种数据准备方法来清理和排列这些流数据进行分析,包括数据驱动的变量选择。数据来自采矿业案例研究,传感器数据来自一台初级生产挖掘机,历时9个月。变量包括58个数字传感器和40个二进制指示器,它们被捕获在4500万行数据中,描述机器不同子系统的条件和状态。总共57%的时间戳包含至少一个传感器的缺失值。响应变量来自操作员选择的故障代码,并存储在车队管理系统中。应用于液压系统,针对操作员识别的21个故障事件,表明数据驱动的选择包含与主题专家预期一致的变量,以及挖掘机上其他系统上的一些传感器,这些传感器从工程角度来看不太容易解释。我们的贡献是展示了一种压缩数据表示,使用开-高-低-关和变量选择来可视化数据,并支持从多变量流数据中识别故障事件的潜在指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Managing streamed sensor data for mobile equipment prognostics
Abstract The ability to wirelessly stream data from sensors on heavy mobile equipment provides opportunities to proactively assess asset condition. However, data analysis methods are challenging to apply due to the size and structure of the data, which contain inconsistent and asynchronous entries, and large periods of missing data. Current methods usually require expertise from site engineers to inform variable selection. In this work, we develop a data preparation method to clean and arrange this streaming data for analysis, including a data-driven variable selection. Data are drawn from a mining industry case study, with sensor data from a primary production excavator over a period of 9 months. Variables include 58 numerical sensors and 40 binary indicators captured in 45-million rows of data describing the conditions and status of different subsystems of the machine. A total of 57% of time stamps contain missing values for at least one sensor. The response variable is drawn from fault codes selected by the operator and stored in the fleet management system. Application to the hydraulic system, for 21 failure events identified by the operator, shows that the data-driven selection contains variables consistent with subject matter expert expectations, as well as some sensors on other systems on the excavator that are less easy to explain from an engineering perspective. Our contribution is to demonstrate a compressed data representation using open-high-low-close and variable selection to visualize data and support identification of potential indicators of failure events from multivariate streamed data.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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