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first_page settings Order Article Reprints Open AccessArticle Classifying Multivariate Signals in Rolling Bearing Fault Detection Using Adaptive Wide-Kernel CNNs

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https://doi.org/10.48693/239
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dc.creatorvan den Hoogen, Jurgen-
dc.creatorBloemheuvel, Stefan-
dc.creatorAtzmueller, Martin-
dc.date.accessioned2023-01-31T08:58:48Z-
dc.date.available2023-01-31T08:58:48Z-
dc.date.issued2021-11-13-
dc.identifier.citationvan den Hoogen J, Bloemheuvel S, Atzmueller M: Classifying Multivariate Signals in Rolling Bearing Fault Detection Using Adaptive Wide-Kernel CNNs. Applied Sciences. 2021; 11(23):11429.ger
dc.identifier.urihttps://doi.org/10.48693/239-
dc.identifier.urihttps://osnadocs.ub.uni-osnabrueck.de/handle/ds-202301318142-
dc.description.abstractWith the developments in improved computation power and the vast amount of (automatic) data collection, industry has become more data-driven. These data-driven approaches for monitoring processes and machinery require different modeling methods focusing on automated learning and deployment. In this context, deep learning provides possibilities for industrial diagnostics to achieve improved performance and efficiency. These deep learning applications can be used to automatically extract features during training, eliminating time-consuming feature engineering and prior understanding of sophisticated (signal) processing techniques. This paper extends on previous work, introducing one-dimensional (1D) CNN architectures that utilize an adaptive wide-kernel layer to improve classification of multivariate signals, e.g., time series classification in fault detection and condition monitoring context. We used multiple prominent benchmark datasets for rolling bearing fault detection to determine the performance of the proposed wide-kernel CNN architectures in different settings. For example, distinctive experimental conditions were tested with deviating amounts of training data. We shed light on the performance of these models compared to traditional machine learning applications and explain different approaches to handle multivariate signals with deep learning. Our proposed models show promising results for classifying different fault conditions of rolling bearing elements and their respective machine condition, while using a fairly straightforward 1D CNN architecture with minimal data preprocessing. Thus, using a 1D CNN with an adaptive wide-kernel layer seems well-suited for fault detection and condition monitoring. In addition, this paper clearly indicates the high potential performance of deep learning compared to traditional machine learning, particularly in complex multivariate and multi-class classification tasks.eng
dc.relationhttps://doi.org/10.3390/app112311429ger
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectfault detectioneng
dc.subjectcondition monitoringeng
dc.subjectmultivariate signalseng
dc.subjecttime series analysiseng
dc.subjectdeep learningeng
dc.subjectindustrial applicationeng
dc.subject.ddc004 - Informatikger
dc.titlefirst_page settings Order Article Reprints Open AccessArticle Classifying Multivariate Signals in Rolling Bearing Fault Detection Using Adaptive Wide-Kernel CNNsger
dc.typeEinzelbeitrag in einer wissenschaftlichen Zeitschrift [Article]ger
orcid.creatorhttps://orcid.org/0000-0001-5723-6629-
orcid.creatorhttps://orcid.org/0000-0002-2480-6901-
ddb.annotationThis paper is a substantially adapted and extended revision of our paper “An Improved Wide-Kernel CNN for Classifying Multivariate Signals in Fault Diagnosis” published in the 2020 IEEE International Conference on Data Mining Workshops Proceedings (ICDMW), Sorrento, Italy, 17–20 November 2020.eng
dc.identifier.doi10.3390/app112311429-
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