Parametric circuit fault diagnosis through oscillation based testing in analogue circuits : statistical and deep learning approaches

dc.contributor.authorCloete, Jacob B.
dc.contributor.authorStander, Tinus
dc.contributor.authorWilke, Daniel Nicolas
dc.contributor.emailu17019363@tuks.co.zaen_US
dc.date.accessioned2022-11-22T11:22:00Z
dc.date.available2022-11-22T11:22:00Z
dc.date.issued2022-02
dc.description.abstractOscillation-based testing of analogue electronic filters removes the need for test signal synthesis. Parametric faults in the presence of normal component tolerance variation are challenging to detect and diagnose. This study demonstrates the suitability of statistical learning and deep learning techniques for parametric fault diagnosis and detection by investigating several time-series classification techniques. Traditional harmonic analysis is used as a baseline for an in-depth comparison. Eight standard classification techniques are applied and compared. Deep learning approaches, which classify the time-series signals directly, are shown to benefit from the oscillator start-up region for feature extraction. Global average pooling in the convolutional neural networks (CNN) allows for Class Activation Maps (CAM). This enables interpreting the time-series signal’s discriminative regions and confirming the importance of the start-up oscillation signal. The deep learning approach outperforms the harmonic analysis approach on simulated data by an average of 11.77% in classification accuracy for the three parametric fault magnitudes considered in this work.en_US
dc.description.departmentElectrical, Electronic and Computer Engineeringen_US
dc.description.urihttps://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639en_US
dc.identifier.citationJ.B. Cloete, T. Stander and D.N. Wilke, "Parametric Circuit Fault Diagnosis Through Oscillation-Based Testing in Analogue Circuits: Statistical and Deep Learning Approaches," in IEEE Access, vol. 10, pp. 15671-15680, 2022, doi: 10.1109/ACCESS.2022.3149324.en_US
dc.identifier.issn2169-3536 (online)
dc.identifier.other10.1109/ACCESS.2022.3149324
dc.identifier.urihttps://repository.up.ac.za/handle/2263/88427
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution Non Commercial No Derivatives 4.0 License.en_US
dc.subjectCircuits faultsen_US
dc.subjectTrainingen_US
dc.subjectTestingen_US
dc.subjectOscillationsen_US
dc.subjectHarmonic analysisen_US
dc.subjectSignal classificationen_US
dc.subjectStatistical analysisen_US
dc.subjectDeep learningen_US
dc.subjectSteady stateen_US
dc.titleParametric circuit fault diagnosis through oscillation based testing in analogue circuits : statistical and deep learning approachesen_US
dc.typeArticleen_US

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