Valve Health Identification Using Sensors and Machine Learning Methods

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Title: Valve Health Identification Using Sensors and Machine Learning Methods
Authors: Qureshi, M. AtifMiralles-Pechuán, LuisPayne, JasonO'Malley, RonanMacNamee, Brian
Permanent link: http://hdl.handle.net/10197/12573
Date: 18-Sep-2020
Online since: 2021-10-26T11:07:52Z
Abstract: Predictive maintenance models attempt to identify developing issues with industrial equipment before they become critical. In this paper, we describe both supervised and unsupervised approaches to predictive maintenance for subsea valves in the oil and gas industry. The supervised approach is appropriate for valves for which a long history of operation along with manual assessments of the state of the valves exists, while the unsupervised approach is suitable to address the cold start problem when new valves, for which we do not have an operational history, come online. For the supervised prediction problem, we attempt to distinguish between healthy and unhealthy valve actuators using sensor data measuring hydraulic pressures and flows during valve opening and closing events. Unlike previous approaches that solely rely on raw sensor data, we derive frequency and time domain features, and experiment with a range of classification algorithms and different feature subsets. The performing models for the supervised approach were discovered to be Adaboost and Random Forest ensembles. In the unsupervised approach, the goal is to detect sudden abrupt changes in valve behaviour by comparing the sensor readings from consecutive opening or closing events. Our novel methodology doing this essentially works by comparing the sequences of sensor readings captured during these events using both raw sensor readings, as well as normalised and first derivative versions of the sequences. We evaluate the effectiveness of a number of well-known time series similarity measures and find that using discrete Frechet distance or dynamic time warping leads to the best results, with the Bray-Curtis similarity measure leading to only marginally poorer change detection but requiring considerably less computational effort.
Funding Details: Enterprise Ireland
Type of material: Book Chapter
Publisher: Springer
Series/Report no.: Communications in Computer and Information Science; 1325
Copyright (published version): 2020 Springer
Keywords: Time-seriesClassificationAnomaly detectionPredictive maintenance modelsSensor data
DOI: 10.1007/978-3-030-66770-2_4
Other versions: https://abifet.wixsite.com/iotstream2020
Language: en
Status of Item: Peer reviewed
Is part of: Gama, J, Pashami, S., Bifet, A., Sayed-Mouchawe, M., Fröning, H., Pernkopf, F., Schiele, G. and Blott, M. (eds.). IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers
Conference Details: The Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020, Ghent, Belgium (held online due to coronavirus outbreak), 14-18 September 2020
ISBN: 9783030667696
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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