Loading...
Please wait, while we are loading the content...
Similar Documents
An Intelligent Diagnosis Method for Machine Fault Based on Federated Learning
Content Provider | MDPI |
---|---|
Author | Li, Zhinong Li, Zedong Li, Yunlong Tao, Junyong Mao, Qinghua Zhang, Xuhui |
Copyright Year | 2021 |
Description | In engineering, the fault data unevenly distribute and difficultly share, which causes that the existing fault diagnosis methods cannot recognize the newly added fault types. An intelligent diagnosis method for machine fault is proposed based on federated learning. Firstly, the local fault diagnosis models diagnosing the existing fault data and the newly added fault data are established by deep convolutional neural network. Then, the weight parameters of local models are fused into global model parameters by federated learning. Finally, the global model parameters are transmitted to each local model. Therefore, each local model update into a global shared model which can recognize the newly added fault types. The proposed method is verified by bearing data. Compared with the traditional model, which can only diagnose the existing fault data but cannot recognize newly added fault types, the federated fault diagnosis model fusing weight parameters can diagnose newly added faults without exchanging the data, and the accuracy is 100%. The proposed method provides an effective method to solve the poor sharing of fault data and poor generalization of fault diagnosis model for mechanical equipment. |
Starting Page | 12117 |
e-ISSN | 20763417 |
DOI | 10.3390/app112412117 |
Journal | Applied Sciences |
Issue Number | 24 |
Volume Number | 11 |
Language | English |
Publisher | MDPI |
Publisher Date | 2021-12-20 |
Access Restriction | Open |
Subject Keyword | Applied Sciences Industrial Engineering Manufacturing Engineering Federated Learning Fault Diagnosis Deep Convolutional Neural Network Model Fusion |
Content Type | Text |
Resource Type | Article |