Managing streamed sensor data for mobile equipment prognostics

• Main Authors: Corrêa, D. (Author), Griffiths, T. (Author), Hodkiewicz, M. (Author), Polpo, A. (Author)
• Format: Article
• Language: English
• Published: Cambridge University Press 2022
• Subjects: Asset health; Condition; Construction equipment; Excavators; Fleet operations; Hydraulic equipment; LASSO logistic regression; Logistics regressions; Mobile equipments; Mobile fleet prognostic; mobile fleet prognostics; Regression analysis; sensor data; Sensors data; Site selection; Streaming data; Systems engineering; time-series data; Time-series data; Variables selections
• Online Access: View Fulltext in Publisher

 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. Copyright © The Author(s), 2022.