机电工程技术2024，Vol.53Issue(8)：41-46,6.DOI:10.3969/j.issn.1009-9492.2024-00045
基于自适应聚类与预聚合的联邦学习故障诊断方法研究
Research on Federated Learning Fault Diagnosis Method Based on Adaptive Clustering and Pre-aggregation
摘要
Abstract
Federated learning shows promising prospects in the field of rotating machinery fault diagnosis.However,facing the challenge of data heterogeneity,existing methods suffer from a severe decline in accuracy.To address this issue,a federated learning framework based on adaptive clustering and pre-aggregation(FLACPA)is proposed.By improving genetic algorithms,adaptive clustering without presetting clusters is achieved,optimizing data set partitioning.Additionally,the framework introduces a pre-aggregation strategy,where each data cluster is pre-aggregated simultaneously.By training local models on intermediary servers,local information is effectively stored,offering a novel perspective for the development of federated learning.The experimental results on two datasets show that the proposed method improves accuracy compared to the previous federated learning framework,with an average accuracy improvement of about 2.75%.Meanwhile,this method reduces the computational resource requirements for model training,with a maximum convergence time improvement of 43%,significantly improving the efficiency of model training.This innovative federated learning framework provides an effective and feasible solution for the field of rotating machinery fault diagnosis.关键词
故障诊断/联邦学习/自适应聚类/预聚合Key words
fault diagnosis/federated learning/adaptive clustering/pre-aggregation分类
信息技术与安全科学引用本文复制引用
高聪,谢怡宁,石江涛..基于自适应聚类与预聚合的联邦学习故障诊断方法研究[J].机电工程技术,2024,53(8):41-46,6.基金项目
黑龙江省科技厅省级重点研发计划指导项目(GZ20220088) （GZ20220088）
黑龙江省科技厅省重点研发计划应用研究项目(SC2022ZX06C0025) （SC2022ZX06C0025）
哈尔滨市科技局制造业创新人才项目(CXRC20221110393) （CXRC20221110393）
