Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Machines

DOI: 10.3390/machines13111001

Authors: Geovanni Díaz-Saldaña, Luis Morales-Velazquez, Vicente Biot-Monterde, José Alfonso Antonino-Daviu

Institutions: Autonomous University of Queretaro, Instituto Tecnológico de la Energía, Universitat Politècnica de València

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896