Fault Diagnosis of Multi-source Heterogeneous Information Fusion Based on Deep Learning

Zhou, Funa; He, Yifan; Han, Haotian

doi:10.1109/ddcls.2019.8909017

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…As a key component of a motor, fault diagnosis research on rolling bearings plays an important role in ensuring the safe and stable operation of the motor [ 1 , 2 , 3 ]. As an effective data-driven fault diagnosis approach, deep learning is not limited by a precise physical model or adequate expert knowledge and can automatically extract fault features from raw data [ 4 , 5 , 6 ]. Therefore, the fault diagnosis methods based on deep learning have received widespread attention.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree

Zhou

Yang

Wang

et al. 2023

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Federated learning is an effective means to combine model information from different clients to achieve joint optimization when the model of a single client is insufficient. In the case when there is an inter-client data imbalance, it is significant to design an imbalanced federation aggregation strategy to aggregate model information so that each client can benefit from the federation global model. However, the existing method has failed to achieve an efficient federation strategy in the case when there is an imbalance mode mismatch between clients. This paper aims to design a federated learning method guided by intra-client imbalance degree to ensure that each client can receive the maximum benefit from the federation model. The degree of intra-client imbalance, measured by gain of a class-by-class model update on the federation model based on a small balanced dataset, is used to guide the designing of federation strategy. An experimental validation for the benchmark dataset of rolling bearing shows that a 23.33% improvement of fault diagnosis accuracy can be achieved in the case when the degree of imbalance mode mismatch between clients is prominent.

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