Intelligent rubbing fault identification using multivariate signals and a multivariate one-dimensional convolutional neural network
作者:
Highlights:
• Multivariate signals and a multivariate one-dimensional convolutional neural network is proposed.
• The proposed method is for diagnosing rubbing faults of various intensities.
• The proposed methodology is tested on two different rubbing fault datasets.
• The proposed framework is capable of differentiating rubbing faults of various intensities.
摘要
•Multivariate signals and a multivariate one-dimensional convolutional neural network is proposed.•The proposed method is for diagnosing rubbing faults of various intensities.•The proposed methodology is tested on two different rubbing fault datasets.•The proposed framework is capable of differentiating rubbing faults of various intensities.
论文关键词:Convolutional neural networks,Deep learning,Fault diagnosis,ModCNN,Multivariate signal,Rub-impact fault
论文评审过程:Received 31 March 2020, Revised 26 February 2021, Accepted 8 March 2022, Available online 11 March 2022, Version of Record 16 March 2022.
论文官网地址:https://doi.org/10.1016/j.eswa.2022.116868