直升机主减半主动反共振隔振技术研究

针对滚动轴承剩余寿命预测中的特征评估及模型优化问题，提出了面向轴承寿命的特征评估与模型优化的方法。该方法在轴承特征进行单调性与敏感性评估的基础上，对轴承运行状态跟踪能力进行量化评估，进而筛选出表征轴承性能退化的多维特征集。为了减少多维特征集之间相关冗余信息对寿命预测的影响，采用相似近邻传播（affinity propagation ,简称AP）聚类方法对多维特征集进行聚类和筛选。为了统一描述AP聚类后的特征对轴承退化状态的表征信息，采用自组织神经网络（self-organizing feature map ,简称SOM）构建轴承健康指数。最后，利用自适应混沌粒子群算法（adaptive chaos particle swarm optimization, 简称ACPSO）优化双指数模型预测轴承剩余寿命。试验表明，该方法可以准确描述轴承运行状态时期，并有效地预测了轴承的剩余寿命。

Helicopter Main Reducer Anti-vibration Isolation Technology

Aiming at feature evaluation and model optimization problem of remaining life prediction of rolling bearings,a method of feature evaluation and model optimization for bearing life is proposed. Based on the monotonicity and sensitivity evaluation of the bearing characteristics, this method quantitatively evaluates the bearing capacity tracking ability, and then selects the multidimensional feature sets that characterize the degradation of bearing performance. In order to reduce the influence of the redundant information between the multidimensional feature sets on the life prediction, multidimensional feature sets are clustered and filtered by using the Affinity propagation (AP) clustering method. In order to describe the characterization of the bearing degradation state uniformly after AP clustering, self-organizing feature map (SOM) is used to construct the bearing health index. Finally, the dual exponential model is optimized by adaptive chaos particle swarm optimization (ACPSO) to predict the remaining bearing life. The test shows that the method can accurately describe the bearing operating period and effectively predict the bearing remaining life.