To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a mult...To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory(LSTM)encoder,a Gated Recurrent Unit(GRU)decoder,and a multi-head attention mechanism.This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions,thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics.Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set,with MAE and RMSE of approximately 0.018 and 0.052,respectively,and a coefficient of determination reaching 0.98.This significantly outperforms traditional identification methods and single RNN models.Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead.Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities.This model can be applied to residualdriven early warning in health monitoring,and risk assessment with scheme optimization in test design.It enables near-real-time deployment feasibility,providing a practical data-driven technical pathway for reliability assurance in advanced equipment.展开更多
传统交流电磁场检测(alternating current field measurement,ACFM)技术因趋肤效应限制,难以有效检测导电材料的深层缺陷。为此,提出了一种脉冲交流电磁场检测(pulsed alternating current field measurement,PACFM)方法,以解决深层缺...传统交流电磁场检测(alternating current field measurement,ACFM)技术因趋肤效应限制,难以有效检测导电材料的深层缺陷。为此,提出了一种脉冲交流电磁场检测(pulsed alternating current field measurement,PACFM)方法,以解决深层缺陷检测难题。建立了PACFM理论模型,通过有限元仿真分析了磁芯形状和相对磁导率对特征信号的影响,结果表明:U形磁芯通过磁场汇聚效应显著增强了Bx信号特征差异,而磁芯相对磁导率为3000达到最佳性能。基于STM32H750微处理器设计的PACFM检测系统采用片上模数转换器(analog to digital converter,ADC)方案,取代了传统数据采集卡方案,系统成本至少降低60%。实验验证表明,该系统能够有效检测深度为3~9 mm的深层缺陷,检测信号随缺陷深度增加而更加明显,且特征量ΔB_(x)与深层缺陷深度呈现良好的线性正相关性,线性优度R^(2)=0.97。所提出的PACFM技术及其嵌入式检测系统为导电物体深层缺陷的高效检测提供了一种高性价比的解决方案。展开更多
基金supported by Natural Science Foundation of China(NSFC),Grant number 5247052693.
文摘To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory(LSTM)encoder,a Gated Recurrent Unit(GRU)decoder,and a multi-head attention mechanism.This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions,thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics.Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set,with MAE and RMSE of approximately 0.018 and 0.052,respectively,and a coefficient of determination reaching 0.98.This significantly outperforms traditional identification methods and single RNN models.Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead.Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities.This model can be applied to residualdriven early warning in health monitoring,and risk assessment with scheme optimization in test design.It enables near-real-time deployment feasibility,providing a practical data-driven technical pathway for reliability assurance in advanced equipment.
文摘传统交流电磁场检测(alternating current field measurement,ACFM)技术因趋肤效应限制,难以有效检测导电材料的深层缺陷。为此,提出了一种脉冲交流电磁场检测(pulsed alternating current field measurement,PACFM)方法,以解决深层缺陷检测难题。建立了PACFM理论模型,通过有限元仿真分析了磁芯形状和相对磁导率对特征信号的影响,结果表明:U形磁芯通过磁场汇聚效应显著增强了Bx信号特征差异,而磁芯相对磁导率为3000达到最佳性能。基于STM32H750微处理器设计的PACFM检测系统采用片上模数转换器(analog to digital converter,ADC)方案,取代了传统数据采集卡方案,系统成本至少降低60%。实验验证表明,该系统能够有效检测深度为3~9 mm的深层缺陷,检测信号随缺陷深度增加而更加明显,且特征量ΔB_(x)与深层缺陷深度呈现良好的线性正相关性,线性优度R^(2)=0.97。所提出的PACFM技术及其嵌入式检测系统为导电物体深层缺陷的高效检测提供了一种高性价比的解决方案。
