【摘要】 针对强背景噪声干扰且变转速下行星齿轮箱早期微弱故障特征难以被有效识别的问题，提出一种改进特征模态分解（Improved Feature Mode Decomposition,IFMD）的时变工况行星齿轮箱微弱故障诊断方法。对于特征模态分解算法中的关键输入参数分解模态个数n、滤波器个数K和滤波器长度L需要依靠人为经验反复尝试而不具有自适应的问题，提出通过尺度空间谱划分来确定所需分解模态个数n；在此基础上，以谱基尼指数（Spectral Gini Index,SGI）作为目标函数，采用粒子群算法自动确定最佳的滤波器个数K和滤波器长度L。最优输入参数组合下，采用IFMD对故障信号进行最佳模态分解，并选取SGI值最大的分量作为敏感模态。从敏感分量的包络阶次谱中提取显著故障特征阶次来准确判别故障类型。通过变转速仿真信号和工程实验数据分析表明，相比PSO-VMD方法、MED方法、SGMD方法和快速谱峭度方法，所提方法能够更加清晰、全面地提取微弱故障信息，提高了时变工况下行星齿轮箱早期故障特征的表征能力和诊断精度。更多还原

【Abstract】 The incipient fault characteristics of planetary gearbox are weak and difficult to effectively identify under strong background noise interference and variable working conditions. To address these issues, an improved feature mode decomposition(IFMD) algorithm is proposed to extract the weak fault characteristics of planetary gearbox under time-varying speed conditions. Firstly, for the key input parameters of the FMD algorithm, such as the number of decomposition mode n, the number of filter K, and the length of filter L, which need to be set manually and lack adaptability, an adaptive scale space spectrum segmentation method is proposed to determine the required number of decomposition modes n. On this basis, the Spectral Gini Index(SGI) is used as the objective function, and particle swarm optimization algorithm is used to automatically determine the optimal filter number K and filter length L. Subsequently, the IFMD is applied to perform optimal modal decomposition on the fault signal under the optimal parameter combination, and the decomposed component with the highest SGI value is selected as the sensitive modal component. Finally, significant fault feature orders are extracted from the envelope order spectrum of sensitive component to accurately diagnose the fault type and location of planetary gearbox. The analysis results of variable speed simulation signals and engineering experimental data indicate that compared to the PSO-VMD method, MED method, SGMD method, and fast spectral kurtosis method, the proposed method can extract weak fault information more clearly and comprehensively, thereby improving the characterization ability and diagnostic accuracy of early fault features of planetary gearbox under time-varying speed conditions.更多还原