工业工程2024，Vol.27Issue(4)：9-18,10.DOI:10.3969/j.issn.1007-7375.240076
基于基尼的深度解卷积方法在机械装备故障诊断中的应用研究
Application of Gini Index-Based Deep Deconvolution in Mechanical Equipment Fault Diagnosis
摘要
Abstract
Deconvolution methods are powerful tools for mechanical equipment fault diagnosis;however,traditional research still relies on shallow feature extraction,making it difficult to handle extremely low signal-to-noise ratios.To address this issue,by introducing the idea of feature learning into the traditional deconvolution theory,a Gini index(GI)based sparse deep deconvolution(GI-SDD)method is proposed for early fault diagnosis of mechanical equipment.First,a band-averaging strategy is adopted to initialize the input layer filter,providing direction for subsequent deconvolution.Next,GI that can represent sparse features of mechanical faults is utilized as the loss function to guide the training of the deep network.Weight optimization is implemented based on the generalized eigenvector algorithm(EVA),thereby learning weak fault features layer by layer.Finally,correlation coefficients and envelope kurtosis(EK)criteria are utilized to evaluate the fault information,reducing dimensionality to output the most significant fault components.Simulation and experiment results demonstrate that the proposed method is robust against strong background noise with fault features being greatly enhanced.Furthermore,the EK of the proposed method improves by 163.43%and 187.11%compared with traditional MED and MGID results respectively.关键词
基尼指数/特征向量法/深度解卷积/特征学习/故障诊断Key words
Gini index(GI)/eigenvector algorithm(EVA)/deep deconvolution/feature learning/fault diagnosis分类
管理科学引用本文复制引用
石惠芳,苗永浩,夏雨..基于基尼的深度解卷积方法在机械装备故障诊断中的应用研究[J].工业工程,2024,27(4):9-18,10.基金项目
国家重点研发计划资助项目(2021YFB2500604) （2021YFB2500604）
