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• W4281386648 abstract "Considering the characteristics of rolling bearing such as variable working conditions, unbalanced fault sample size, and multiple coupling fault types, it is a great challenge to achieve general accurate fault diagnosis model. In this paper, deep meta-learning and variational autoencoder (DML-VAE) is applied for coupling fault diagnosis of rolling bearing under variable working conditions. The collected vibration signals of rolling bearing are divided into long time series samples, including normal samples, single fault samples, and coupling fault samples. Then, variational autoencoder (VAE) is utilized for data augmentation of time series samples, and the generated samples are brought into one-dimensional deep convolutional neural network (1-DCNN) for further classification of multiple coupling faults. Subsequently, the trained 1-DCNN is regarded as embedding model. Training samples and other working condition samples are defined as support set and query set. Based on metric-based meta-learning method, sample pairs composed of support set and query set are constructed and brought into the embedding model to get the category with shortest metric distance as the classification result. In addition, the embedding model can be optimized by minimizing the contrastive loss among these sample pairs. The case study shows that the DML-VAE can achieve accurate classification results under the coupling of two faults and three faults, and maintain high diagnostic accuracy under variable working conditions. Compared with other models, the proposed model can also get the most accurate fault diagnosis results for all categories under unbalanced samples." @default.
• W4281386648 created "2022-05-25" @default.
• W4281386648 creator A5014150866 @default.
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• W4281386648 date "2022-05-24" @default.
• W4281386648 modified "2023-10-02" @default.
• W4281386648 title "Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions" @default.
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• W4281386648 doi "https://doi.org/10.1177/09544062221101834" @default.
• W4281386648 hasPublicationYear "2022" @default.
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