Synthetic Current Signal Generation for Broken Rotor Bars in Induction Machines Using Generative Audio Synthesis Networks

Synthetic Current Signal Generation for Broken Rotor Bars in Induction Machines Using Generative Audio Synthesis Networks | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 November 2025 V1 Latest version Share on Synthetic Current Signal Generation for Broken Rotor Bars in Induction Machines Using Generative Audio Synthesis Networks Authors : Nada El Bouharrouti 0000-0001-9637-3378 [email protected] , Masum Md Billah , Nicola Dainese , Karolina Kudelina , Muhammad U Naseer , and Anouar Belahcen Authors Info & Affiliations https://doi.org/10.22541/au.176244966.63551633/v1 206 views 133 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In data-driven condition monitoring, data scarcity poses a challenge to the development of reliable methods for diagnosing faults in induction machines. This paper proposes two approaches to stochastically synthesize current signals representing various rotor bar health conditions using generative adversarial networks, by leveraging simulated and experimental data sources. First, a conditional generative adversarial network is trained using real time-series current signals collected from experiments. Second, the same network is trained using simulated data from a magnetic equivalent circuit as the conditioning input to the generator. Learning from physics-based simulations and experimental data overcomes the limitations of each data source while leveraging their respective advantages. The results show that generative adversarial networks are able to replicate stochastically current signals with broken rotor bar fault signatures in time and frequency domains with accurate amplitude and phase. Statistical features also show that the generated data closely matches the real measurements. Finally, two supervised machine learning models are successfully trained using exclusively synthetic data generated by our models to diagnose experimental data, confirming the models' capability to produce realistic fault signatures suitable for training diagnostic algorithms or augmenting datasets in data-scarce environments. Supplementary Material File (ieeeaccess_final_compressed.pdf) Download 21.52 MB Information & Authors Information Version history V1 Version 1 06 November 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords condition monitoring data augmentation generative adversarial networks index terms broken rotor bars induction machine stator current signals Authors Affiliations Nada El Bouharrouti 0000-0001-9637-3378 [email protected] Department of Electrical Engineering and Automation, Aalto University View all articles by this author Masum Md Billah Department of Electrical Engineering and Automation, Aalto University View all articles by this author Nicola Dainese Department of Computer Science, Aalto University View all articles by this author Karolina Kudelina Mechanical Engineering and Energy Technology Processes Control Work Group, Virumaa College, Tallinn University of Technology View all articles by this author Muhammad U Naseer Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology View all articles by this author Anouar Belahcen Department of Electrical Engineering and Automation, Aalto University View all articles by this author Funding Information Research Council of Finland 346438 Metrics & Citations Metrics Article Usage 206 views 133 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Nada El Bouharrouti, Masum Md Billah, Nicola Dainese, et al. Synthetic Current Signal Generation for Broken Rotor Bars in Induction Machines Using Generative Audio Synthesis Networks. Authorea . 06 November 2025. DOI: https://doi.org/10.22541/au.176244966.63551633/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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