Multi-size wide kernel convolutional neural network for bearing fault diagnosis

Kumar, Prashant; Raouf, Izaz; Song, Jinwoo; Prince,; Kim, Heung Soo

doi:10.1016/j.advengsoft.2024.103799

Advances in Engineering Software

2024

DOI: 10.1016/j.advengsoft.2024.103799

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Multi-size wide kernel convolutional neural network for bearing fault diagnosis

Prashant Kumar,

Izaz Raouf,

Jinwoo Song

et al.

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A Lightweight and Small Sample Bearing Fault Diagnosis Algorithm Based on Probabilistic Decoupling Knowledge Distillation and Meta-Learning

Luo,

Ren,

Zhang

et al. 2024

Sensors

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Rolling bearings play a crucial role in industrial equipment, and their failure is highly likely to cause a series of serious consequences. Traditional deep learning-based bearing fault diagnosis algorithms rely on large amounts of training data; training and inference processes consume significant computational resources. Thus, developing a lightweight and suitable fault diagnosis algorithm for small samples is particularly crucial. In this paper, we propose a bearing fault diagnosis algorithm based on probabilistic decoupling knowledge distillation and meta-learning (MIX-MPDKD). This algorithm is lightweight and deployable, performing well in small sample scenarios and effectively solving the deployment problem of large networks in resource-constrained environments. Firstly, our model utilizes the Model-Agnostic Meta-Learning algorithm to initialize the parameters of the teacher model and conduct efficient training. Subsequently, by employing the proposed probability-based decoupled knowledge distillation approach, the outstanding performance of the teacher model was imparted to the student model, enabling the student model to converge rapidly in the context of a small sample size. Finally, the Paderborn University dataset was used for meta-training, while the bearing dataset from Case Western Reserve University, along with our laboratory dataset, was used to validate the results. The experimental results demonstrate that the algorithm achieved satisfactory accuracy performance.

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A Lightweight and Small Sample Bearing Fault Diagnosis Algorithm Based on Probabilistic Decoupling Knowledge Distillation and Meta-Learning

Luo,

Ren,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

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Multi-size wide kernel convolutional neural network for bearing fault diagnosis

Cited by 1 publication

References 34 publications

A Lightweight and Small Sample Bearing Fault Diagnosis Algorithm Based on Probabilistic Decoupling Knowledge Distillation and Meta-Learning

A Lightweight and Small Sample Bearing Fault Diagnosis Algorithm Based on Probabilistic Decoupling Knowledge Distillation and Meta-Learning

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