不同工况振动谱的空间滚动轴承寿命同尺度表征和预测方法研究

项目名称： 不同工况振动谱的空间滚动轴承寿命同尺度表征和预测方法研究

项目编号： No.51305471

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 机械、仪表工业

项目作者： 陈仁祥

作者单位： 重庆交通大学

项目金额： 24万元

中文摘要： 空间滚动轴承是影响空间飞行器高可靠长寿命的最关键的活动零部件之一。针对常用的轴承摩擦力矩、温度等测试手段不能有效预测空间滚动轴承寿命的问题，提出利用轴承振动信号进行不同工况下空间滚动轴承寿命状态同尺度表征和预测的新方法。该方法的研究思路为：提出集微弱振动特征提取、同尺度表征、寿命状态识别与预测于一体的空间滚动轴承寿命表征和预测理论体系；提出不同工况条件下轴承寿命特征向量间的映射新方法，突破不同工况下轴承寿命状态特征向量间不具有可比性的难题，实现不同工况下空间滚动轴承寿命状态同尺度表征；提出小子样非类同条件下的空间滚动轴承寿命状态识别与预测方法，实现空间滚动轴承寿命的预测；最后研发基于振动谱的空间滚动轴承寿命状态表征与预测系统原型，进行系统性能评估。该方法对于指导空间活动零部件的设计与制造、建立使用寿命的评估准则、提高后续空间飞行器的在轨寿命和使用性能都具有重要的意义。

中文关键词： 空间滚动轴承；寿命特征；同尺度表征；深度神经网络；寿命退化预测

英文摘要： Space rolling bearing is one of the most critical activity components that affect the using life and high reliability to spacecraft. In view of the common bearing friction torque, temperature test, etc. can not effectively predict the space rolling bearing life, a novel method concerning life identification and prediction for space rolling bearings based on vibration spectrum under different working condition with the same criterion is proposed. The research stratagem of this proposed method is as follows. A novel idea, which integrates the vibration spectrum acquisition, the life state feature vector extraction, the life state identification and prediction under the multi-condition with same criterion, will be proposed to form the new principle and the new methods for space rolling bearing life state identification and prediction. A novel method of life feature vector mapping under the multi-condition for space rolling bearing will be proposed. This method can solve the problem for space rolling bearing that the life state feature vectors are not comparable under different working conditions, and achieve space rolling bearing life state representation under the multi-condition with same criterion. A novel method of life state identification and prediction under small samples with different classes will be propo

英文关键词： Space rolling bearing；life feature；Representation under the same criterion；Deep neural network；Life state prediction