Based on SincNet Network Combined with L2 Regularization Fault Diagnosis
Troubleshooting is critical to keeping equipment and systems up and running.It can help increase efficiency,reduce costs,enhance security,improve user satisfaction,and support decision-making and optimi-zation.Through timely fault diagnosis and resolution,productivity can be improved,risks can be reduced,and better products and services can be provided.Aiming at the shortcomings of traditional fault diagnosis meth-ods based on physical information model and data-driven model,which are not strong in interpretability and low in fault diagnosis accuracy,this paper proposes a fault diagnosis method based on convolution neural net-work,SincNet and L2 regularization.By taking the bearing as an example,experimental verification is carried out and compared with traditional CNN,the accuracy rate reaches 99.5%,which is also more interpretable.Compared with traditional CNN,the model has stronger interpretability and higher fault diagnosis accuracy.
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