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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