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curr2vib: Modality Embedding Translation for Broken-Rotor Bar Detection
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000−0003−3720−3015
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-1759-8593
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-7796-5201
2023 (English)In: Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II / [ed] Irena Koprinska et al., Cham: Springer Nature, 2023, Vol. 1753, p. 423-437Conference paper, Published paper (Refereed)
Abstract [en]

Recently and due to the advances in sensor technology and Internet-of-Things, the operation of machinery can be monitored, using a higher number of sources and modalities. In this study, we demonstrate that Multi-Modal Translation is capable of transferring knowledge from a modality with higher level of applicability (more usefulness to solve an specific task) but lower level of accessibility (how easy and affordable it is to collect information from this modality) to another one with higher level of accessibility but lower level of applicability. Unlike the fusion of multiple modalities which requires all of the modalities to be available during the deployment stage, our proposed method depends only on the more accessible one; which results in the reduction of the costs regarding instrumentation equipment. The presented case study demonstrates that by the employment of the proposed method we are capable of replacing five acceleration sensors with three current sensors, while the classification accuracy is also increased by more than 1%.

Place, publisher, year, edition, pages
Cham: Springer Nature, 2023. Vol. 1753, p. 423-437
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1753
Keywords [en]
Induction Motor, Broken Rotor Bar, Fault Diagnosis, Predictive Maintenance, Contrastive pre-training, Multi-Modal Latent Translation
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-49990DOI: 10.1007/978-3-031-23633-4_28ISI: 000967761200028Scopus ID: 2-s2.0-85149919657ISBN: 978-3-031-23633-4 (electronic)OAI: oai:DiVA.org:hh-49990DiVA, id: diva2:1737863
Conference
ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Grenoble, France, September 19–23, 2022
Part of project
eXplainable Predictive Maintenance, Swedish Research CouncilAvailable from: 2023-02-18 Created: 2023-02-18 Last updated: 2023-08-11Bibliographically approved

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fulltext(813 kB)98 downloads
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Publisher's full textScopushttps://link.springer.com/chapter/10.1007/978-3-031-23633-4_28

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Taghiyarrenani, ZahraNowaczyk, Sławomir

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Amirhossein, BerenjiTaghiyarrenani, ZahraNowaczyk, Sławomir
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CiteExportLink to record
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