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Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism
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Online publication date: 2023-01-27
Publication date: 2023-01-27
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(1):16
  • Multiscale 2-D convolutional neural networks with different sizes are proposed.
  • The CWT analysis of vibration signals and deep learning methods are combined.
  • An improved channel attention mechanism is developed.
  • The model can be applied to single and advanced fault-diagnosing eventualities.
  • The algorithm reduces the dependence on prior knowledge and manual labor.
Accurate fault diagnosis is critical to operating rotating machinery safely and efficiently. Traditional fault information description methods rely on experts to extract statistical features, which inevitably leads to the problem of information loss. As a result, this paper proposes an intelligent fault diagnosis of rolling bearings based on a continuous wavelet transform(CWT)-multiscale feature fusion and an improved channel attention mechanism. Different from traditional CNNs, CWT can convert the 1-D signals into 2-D images, and extract the wavelet power spectrum, which is conducive to model recognition. In this case, the multiscale feature fusion was implemented by the parallel 2-D convolutional neural networks to accomplish deeper feature fusion. Meanwhile, the channel attention mechanism is improved by converting from compressed to extended ways in the excitation block to better obtain the evaluation score of the channel. The proposed model has been validated using two bearing datasets, and the results show that it has excellent accuracy compared to existing methods.
Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults
Jose Alberto Maestro-Prieto, José Miguel Ramírez-Sanz, Andrés Bustillo, Juan José Rodriguez-Díez
Applied Intelligence
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