Contrast Feature-Based Approach for Fault Detection in Wound-Rotor Induction Machines

Ferrucho-Alvarez, Edna R.; Taherzadeh, Mehdi; Henao, H.; Capolino, Gérard‐André; Cabal-Yépez, Eduardo

doi:10.1109/icem51905.2022.9910754

Cited by 2 publications

(1 citation statement)

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“…resultant casualties, and economic losses [6], [7]. Therefore, monitoring and diagnosing of bearing is necessary to establish a model with good practicality and stability in the field of fault diagnosis, which has been explored by experts worldwide [8]- [11].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Using Imbalanced Data of Rolling Bearings Based on a Deep Migration Model

Wang,

Zhang

2024

IEEE Access

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To address the problem that uneven sample distribution can affect the accuracy and stability of fault diagnosis outcomes, we propose a deep transfer learning-Res2Net-convolutional block attention mechanism model. Firstly, the deep migration technique is used to transfer weights of the imbalanced source domain samples to the balanced target domain, expanding the data samples. Secondly, in the feature extraction and detection phase, All eight residual blocks are embedded in convolution block attention to ensure that interference signals are suppressed and that the key fault features are retained. Multilayer feature fusion extracts faulty sample features from multiple residual blocks of the network, which are combined into the feature fusion layer by parallel splicing. Finally, this model is experimentally validated using two different bearing datasets, and the combined evaluation indexes of the new model under the severe imbalance condition are 95.80% and 95.85%, respectively, demonstrating the feasibility and excellent performance of the model.

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