Classifying induction motor faults using spectrogram images with deep transfer learning - Ertargin, Merve; Yildirim, Ozal; Orhan, Ahmet (Avestia Publishing, 2024)
For industrial systems to be reliable and efficient, motor faults detection is important. Conventional methods of fault detection are typically expensive and time-consuming. The ability to detection motor faults has advanced significantly in recent years thanks to the application of deep learning techniques. Deep learning algorithms have the ability to automatically extract complicated features from big data sets, which can speed up and improve the accuracy of motor faults detection. Since collecting motor acoustic data is cost-effective and easy, it is advantageous to use it in fault detection. In this study, motor acoustic signals were converted into spectrograms and faults in induction motors were detected using a transfer learning approach with pre-trained models. Totally 8-class fault detection was performed with an accuracy rate of 91.52% using VGG16 and 92.11% using VGG19. © 2024, Avestia Publishing. All rights reserved.
10th World Congress on Electrical Engineering and Computer Systems and Sciences, EECSS 2024 -- 19 August 2024 through 21 August 2024 -- Barcelona -- 319779
Keywords: Acoustic data; Electric motor fault detection; VGG16; VGG19