With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
【目的】相比直线振动信号,行星轮扭振信号不受行星轮通过效应和信号传递路径的影响,频谱结构更加简单。因此,基于扭振信号开展行星齿轮箱故障诊断有望得到更好的诊断结果。提出一种基于扭振信号调制信号双谱(Modulation Signal Bispect...【目的】相比直线振动信号,行星轮扭振信号不受行星轮通过效应和信号传递路径的影响,频谱结构更加简单。因此,基于扭振信号开展行星齿轮箱故障诊断有望得到更好的诊断结果。提出一种基于扭振信号调制信号双谱(Modulation Signal Bispectrum,MSB)分析的行星齿轮箱故障诊断新方法。【方法】首先,对编码器信号使用希尔伯特(Hilbert)变换方法求解瞬时转速信号;然后,对瞬时转速信号进行MSB分析,寻找最优载波频带;最后,对选取的最优载波频带构建MSB最优载波频带复合谱,并作为行星齿轮箱的故障诊断特征。【结果】试验结果表明,所提出方法可以更直观地反映行星轮的故障状态以及故障信息,验证了该方法在行星轮故障诊断方面的有效性和优越性。展开更多
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
文摘【目的】相比直线振动信号,行星轮扭振信号不受行星轮通过效应和信号传递路径的影响,频谱结构更加简单。因此,基于扭振信号开展行星齿轮箱故障诊断有望得到更好的诊断结果。提出一种基于扭振信号调制信号双谱(Modulation Signal Bispectrum,MSB)分析的行星齿轮箱故障诊断新方法。【方法】首先,对编码器信号使用希尔伯特(Hilbert)变换方法求解瞬时转速信号;然后,对瞬时转速信号进行MSB分析,寻找最优载波频带;最后,对选取的最优载波频带构建MSB最优载波频带复合谱,并作为行星齿轮箱的故障诊断特征。【结果】试验结果表明,所提出方法可以更直观地反映行星轮的故障状态以及故障信息,验证了该方法在行星轮故障诊断方面的有效性和优越性。
