Investigation on the Use of 2D-DOST on Time–Frequency Representations of Stray Flux Signals for Induction Motor Fault Classification Using a Lightweight CNN Model

Condition monitoring and fault detection in induction motors (IMs) are priorities in the industrial environment to secure safe conditions for the processes and production. Convolutional Neural Networks (CNNs) are gaining interest in these tasks as they allow automatic extraction of features from the inputs, sometimes Time–Frequency Distributions (TFDs) obtained with various transforms, directly into large models for data classification. This work presents a proposal for the application of a widely used texture analysis tool in the medical field, the 2D Discrete Orthonormal Stockwell Transform (2D-DOST), to improve the accuracy of a lightweight CNN when using different TFDs and comparing the results to the use of the TFDs in RGB and grayscale. The results show that the use of the 2D-DOST improves the classification accuracy in a two to five percent range for all motor conditions under study, while having minimal variations to the training times when compared to RGB or grayscale images, opening the possibility for the use of image processing tools on TFDs to improve automatic feature extraction while using small CNN models.