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المؤلفNashed, Mohamad S
المؤلفRenno, Jamil
المؤلفMohamed, M Shadi
تاريخ الإتاحة2024-06-02T06:20:08Z
تاريخ النشر2023
اسم المنشورJVC/Journal of Vibration and Control
المصدرScopus
المعرّفhttp://dx.doi.org/10.1177/10775463231177101
الرقم المعياري الدولي للكتاب10775463
معرّف المصادر الموحدhttp://hdl.handle.net/10576/55702
الملخصWe present a novel method for real-time fault classification using the time history of acoustic emissions (AEs) recorded from a lab-scale gas turbine operating under normal and faulty conditions across multiple turbine speeds. Time-frequency features are extracted using the continuous wavelet transform, and for each signal, the root mean square (RMS) and kurtosis are calculated. We employ a color mapping technique to combine the time-frequency and statistical features into a single red-green-blue (RGB) image. The red channel is mapped to the time-frequency data, whereas the green and blue channels are mapped to the RMS and kurtosis, respectively. Subsequently, a deep convolutional neural network is trained on the generated images to classify the gas turbine condition. We show that the proposed model can form an online monitoring system using AEs to classify multiple running conditions at various turbine speeds. The methodology not only achieves real-time classification of faults but also minimizes the human intervention in identifying these faults. The datasets and codes used in this paper will be openly available.
راعي المشروعThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this research was graciously provided by Qatar National Research Fund (a member of Qatar Foundation) via the National Priorities Research Program under Grant no. NPRP-11S-1220-170112. The authors would also like to thank Professor Robert L. Reuben (of Heriot Watt University) for the fruitful discussions.
اللغةen
الناشرSAGE Publications Inc.
الموضوعacoustic emissions
Color mapping
continuous wavelet transform
convolutional neural networks
gas turbine blade fault
sensor fault classification
العنوانFault classification using convolutional neural networks and color channels for time-frequency analysis of acoustic emissions
النوعArticle
الصفحات-
dc.accessType Abstract Only


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