Application and comparison of feature-based classification models for multistable impact motions of percussive drilling
dc.contributor.author | Afebu, KO | |
dc.contributor.author | Liu, Y | |
dc.contributor.author | Papatheou, E | |
dc.date.accessioned | 2021-05-11T08:50:27Z | |
dc.date.issued | 2021-05-08 | |
dc.description.abstract | Dynamics of the bit-rock interaction under percussive drilling often encounter multistability that produces coexisting impact motions for a wide range of drilling conditions. Some of them may be detrimental to its performance as it cuts through the inhomogeneous rock layers. A necessary mitigation is the ability to distinguish between coexisting impact motions in order to maintain a high-performance drilling. For this purpose, dynamical responses of a vibro-impact system mimicking the bit-rock interaction of percussive drilling were explored in this study by using machine learning techniques. As a fundamental approach of improving machine learning, hand-crafted and automatic feature extractions were carried out. Simulation results show that extracting appropriate features and using a suitable network are essential for characterising the vibro-impact motions. Extracting statistical, histogram of gradient, continuous wavelet transform and pre-trained convolutional network features are effective and less computationally intensive. With their high accuracies, they become the first point of consideration when designing the classification model for multistable vibro-impact motions of percussive drilling. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.description.sponsorship | Petroleum Technology Development Fund (PTDF) of Nigeria | en_GB |
dc.identifier.citation | Article 116205 | en_GB |
dc.identifier.doi | 10.1016/j.jsv.2021.116205 | |
dc.identifier.grantnumber | EP/P023983/1 | en_GB |
dc.identifier.grantnumber | PTDF/ED/PHD/AKO/1080/17 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/125632 | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier | en_GB |
dc.rights.embargoreason | Under embargo until 8 May 2022 in compliance with publisher policy | en_GB |
dc.rights | © 2021 Published by Elsevier Ltd. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_GB |
dc.subject | Percussive drilling | en_GB |
dc.subject | Bit-rock interaction | en_GB |
dc.subject | Vibro-impact dynamics | en_GB |
dc.subject | Multistability | en_GB |
dc.subject | Machine learning | en_GB |
dc.title | Application and comparison of feature-based classification models for multistable impact motions of percussive drilling | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2021-05-11T08:50:27Z | |
dc.identifier.issn | 0022-460X | |
exeter.article-number | 116205 | en_GB |
dc.description | This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record | en_GB |
dc.description | Data accessibility: The data sets generated and analysed during the current study are available from the corresponding author on reasonable request. | en_GB |
dc.identifier.journal | Journal of Sound and Vibration | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_GB |
dcterms.dateAccepted | 2021-05-08 | |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2021-05-08 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2021-05-11T08:48:02Z | |
refterms.versionFCD | AM | |
refterms.panel | B | en_GB |
Files in this item
This item appears in the following Collection(s)
Except where otherwise noted, this item's licence is described as © 2021 Published by Elsevier Ltd. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/