基于电-振信号联合的电动机故障诊断研究

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中图分类号：TD61 文献标志码：A

Abstract：To address theproblems of limited fault diagnosis accuracy of single-signal （curent or vibration） methodsand diffculty in identifying multiple coexisting faultsunder complex operating conditions of underground coal mine motors，a motor fault diagnosis method based on thecombinationof electrical and vibrationsignalswas proposed，leveraging the electromechanical coupling characteristicsand the complementarity of multi-sensor information.Fault information was captured from themotor current and vibrationsignals inthetime domain，frequencydomain，andtime-frequencydomain，respectively.The features were fused in thechannel dimension to generate feature color images containing multi-domain information， therebyenriching the fault characterization.A Dual-Channel Residual Network （DCResNet）model embedded with an Improved Convolutional Block Atention Module （ICBAM），namely ICBAM-DCResNet，was constructed.Through multiple residual blocks and the atention mechanismof ICBAM，deep features of image samples were extracted.Finally，feature fusion and classification were performed to achieve fault diagnosis based onthe combinationof electricaland vibration signals.Thecomparative experimental resultsshowed that multidomain fusion achieved higher diagnosisaccuracy than single-domain analysis，and the ICBAM-DCResNet model outperformed the Residual Network （ResNet） model， demonstrating stronger feature extraction capability for the signal samples.The experiment results on the public dataset demonstrated that the proposed motor fault diagnosis method based on the combination of electrical and vibration signals achieved an accuracy of 99.8% 5 with good identification performance for rotor and bearing faults and strong generalization ability.

Key words： motor fault diagnosis; current signal; vibration signal; multi-domain feature extraction; DualChannel Residual Network; Convolutional Block Attention Module

0引言

随着技术的发展进步，煤矿井下大型机电设备的应用越来越广泛[1-2]。（剩余13680字）