Valve Health Identification Using Sensors and Machine Learning Methods

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dc.contributor.authorQureshi, M. Atif-
dc.contributor.authorMiralles-Pechuán, Luis-
dc.contributor.authorPayne, Jason-
dc.contributor.authorO'Malley, Ronan-
dc.contributor.authorMacNamee, Brian- Springeren_US
dc.descriptionThe 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 2020en_US
dc.description.abstractPredictive 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.en_US
dc.description.sponsorshipEnterprise Irelanden_US
dc.relation.ispartofGama, 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 Papersen_US
dc.relation.ispartofseriesCommunications in Computer and Information Scienceen_US
dc.rightsThe final publication is available at
dc.subjectAnomaly detectionen_US
dc.subjectPredictive maintenance modelsen_US
dc.subjectSensor dataen_US
dc.titleValve Health Identification Using Sensors and Machine Learning Methodsen_US
dc.typeBook Chapteren_US
dc.statusPeer revieweden_US
dc.neeo.contributorQureshi|M. Atif|aut|-
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