Combining an optimisation-based frequency band identification method with historical data for novelty detection under time-varying operating conditions

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dc.contributor.author Schmidt, Stephan
dc.contributor.author Gryllias, Konstantinos C.
dc.date.accessioned 2022-07-15T08:47:05Z
dc.date.available 2022-07-15T08:47:05Z
dc.date.issued 2021-02
dc.description.abstract Incipient damage detection is important for critical rotating machines such as gearboxes found in the power generation, mining and aeronautical industries. However, the fault information frequently manifests in weak frequency bands in the vibration signals and the fault diagnosis process is impeded by time-varying operating conditions. Frequency band identification methods can be used to enhance the weak fault information in the vibration signal, however, this process is impeded by impulsive components unrelated to the component-of-interest and time-varying operating conditions. Hence, in this work, an optimisation-based frequency band identification method is developed to address these shortcomings. This method comprises of two steps; in the first step, a coarse informative frequency band procedure is used, whereafter a derivative-free optimisation algorithm is utilised to find the optimal frequency band for fault diagnosis. Since many rotating machines operate for long periods of time in a healthy condition, much healthy historical data are usually available when continuous monitoring is performed. Hence, this historical data are used with the proposed frequency band identification approach for automatic fault detection. The method is investigated on two experimental datasets acquired under time-varying operating conditions and compared to other existing approaches for fault diagnosis. The results indicate that the method is very capable of enhancing the fault information and can be used for automatic fault detection under time-varying operating conditions. en_US
dc.description.department Mechanical and Aeronautical Engineering en_US
dc.description.librarian hj2022 en_US
dc.description.uri http://www.elsevier.com/locate/measurement en_US
dc.identifier.citation Schmidt, S. & Gryllias, K.C. 2021, 'Combining an optimisation-based frequency band identification method with historical data for novelty detection under time-varying operating conditions', Measurement, vol. 169, art. 108517, pp. 1-19, doi : 10.1016/j.measurement.2020.108517. en_US
dc.identifier.issn 0263-2241 (print)
dc.identifier.issn 1873-412X (online)
dc.identifier.other 10.1016/j.measurement.2020.108517
dc.identifier.uri https://repository.up.ac.za/handle/2263/86233
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.rights © 2020 Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was submitted for publication in Measurement. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms are not reflected in this document. A definitive version was subsequently published in Measurement, vol. 169, art. 108517, pp. 1-19, 2021. doi : 10.1016/j.measurement.2020.108517. en_US
dc.subject Diagnostics en_US
dc.subject Frequency band identification en_US
dc.subject Optimisation en_US
dc.subject Time-varying operating conditions en_US
dc.subject Historical data en_US
dc.subject Gearboxes en_US
dc.subject.other Engineering, built environment and information technology articles SDG-04
dc.subject.other SDG-04: Quality education
dc.subject.other Engineering, built environment and information technology articles SDG-09
dc.subject.other SDG-09: Industry, innovation and infrastructure
dc.subject.other Engineering, built environment and information technology articles SDG-12
dc.subject.other SDG-12: Responsible consumption and production
dc.title Combining an optimisation-based frequency band identification method with historical data for novelty detection under time-varying operating conditions en_US
dc.type Preprint Article en_US


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