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• W4311496177 abstract "Capsule networks, which have achieved many achievements in machine vision, have also gotten wide attention in machinery fault diagnosis. However, due to the non-stationarity and diversity of mechanical system vibration signals, as well as the dual-scale characteristics of different fault features, it is often difficult for existing single-scale capsule networks to fully mine vital discriminative features in the data, which is not conducive to accurate identification of mechanical faults. This paper studies an attention-based dual-scale feature fusion capsule network for mechanical fault diagnosis. It first extracts dual-scale features from grayscale images obtained from vibration signals using convolutional layers composed of kernels of different sizes; secondly, an attention-based two-branch network is designed to calculate the weights of features at different scales, and accordingly dual-scale feature fusion is performed; finally, the obtained features are entered into the capsule layers, and the classification and identification of mechanical faults are realized by optimizing the model using the classification loss and reconstruction loss. A rolling bearing experimental dataset and a motor fault dataset are adopted to assess the performance of the proposed method, and the comparison results confirm its effectiveness and superiority, indicating that it has the potential to be a useful tool for detecting mechanical faults." @default.
• W4311496177 created "2022-12-26" @default.
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• W4311496177 date "2023-02-01" @default.
• W4311496177 modified "2023-09-30" @default.
• W4311496177 title "Mechanical fault intelligent diagnosis using attention-based dual-scale feature fusion capsule network" @default.
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• W4311496177 doi "https://doi.org/10.1016/j.measurement.2022.112345" @default.
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