Name: VINÍCIUS ANDRADE NUNES DE MORAES
Publication date: 24/03/2025
Examining board:
Name |
Role |
|---|---|
| ANDRÉ ABEL AUGUSTO | Examinador Externo |
| HELDER ROBERTO DE OLIVEIRA ROCHA | Presidente |
| JAIR ADRIANO LIMA SILVA | Coorientador |
| LUCAS FRIZERA ENCARNACAO | Examinador Interno |
Summary: This work presents a hybrid approach using Convolutional Neural Networks (CNN) and Transformers for fault diagnosis in three-phase induction motors, focusing on the detection and classification of the severity of broken bar faults based on current and
voltage signals. Electrical Signature Analysis (ESA), widely used in motor monitoring, offers several advantages. However, ESA-based techniques traditionally rely on spectral transformations, which can result in high computational cost and reduced generalization capability. CNNs can extract discriminative features directly from raw data, eliminating the need for preprocessing steps. The proposed study integrates CNNs with the attention mechanism of Transformers, which captures spatiotemporal dependencies in the data. The Convolutional Transformer Neural Network (CTNN) achieved approximately 97% accuracy when using the entire dataset, significantly outperforming classical machine learning algorithms such as Random Forest and k-Nearest Neighbors (KNN), which obtained 90% and 86% accuracy, respectively. The 1D CNN, tested under similar conditions, achieved
96% accuracy. Compared to other methodologies involving multiple preprocessing steps and transformations to the frequency domain, the proposed approach achieves similar results, close to 100% accuracy, while being simpler, more efficient, and with greater generalization capability. Additionally, the methodology employs a reduced sampling rate, approximately six times lower, contributing to computational cost reduction without compromising performance.
