TY - GEN
T1 - Valve Health Identification Using Sensors and Machine Learning Methods
AU - Atif Qureshi, M.
AU - Miralles-Pechuán, Luis
AU - Payne, Jason
AU - O’Malley, Ronan
AU - Namee, Brian Mac
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Anomaly detection
KW - Classification
KW - Predictive maintenance models
KW - Sensor data
KW - Time-series
UR - http://www.scopus.com/inward/record.url?scp=85101593131&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-66770-2_4
DO - 10.1007/978-3-030-66770-2_4
M3 - Conference contribution
AN - SCOPUS:85101593131
SN - 9783030667696
T3 - Communications in Computer and Information Science
SP - 45
EP - 60
BT - 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, Revised Selected Papers
A2 - Gama, Joao
A2 - Pashami, Sepideh
A2 - Bifet, Albert
A2 - Sayed-Mouchawe, Moamar
A2 - Fröning, Holger
A2 - Pernkopf, Franz
A2 - Schiele, Gregor
A2 - Blott, Michaela
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and 1st International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020
Y2 - 14 September 2020 through 18 September 2020
ER -