面向广义数学形态颗粒特征的灰色马尔科夫剩余寿命预测方法

摘要

In the domain of rolling bearing condition monitor and fault prognosis,to solve the key problem of rolling bearing de-generate feature extraction,a new approach based on generalized mathematical morphological particle is proposed in the paper, the new approach,which is founded on the mathematical morphological particle analysis,introduces corrosion and dilation op-erators in morphological calculation and takes the calculated generalized mathematical morphological particle as feature indica-tor,therefore,the performance degenerate degree could be reflected in quantity.The effectiveness of this approach is test and verified with simulation and actual signal.On this basis,in order to describe the whole tendency and random fluctuating feature for rolling bearings,grey Markov model is applied in the remaining service life prediction for rolling bearing,A method of re-maining service life prediction based on generalized mathematical morphological particle and grey Markov model is proposed thereby.Rolling bearing fatigued life testing was proceeded with Hangzhou Bearing Test & Research Center,the approach is proved effective with the collecting bearing inner race whole life data in fatigued life testing.%在滚动轴承状态监测与故障预测领域中，针对滚动轴承退化特征提取这一关键问题，提出了一种基于广义数学形态颗粒的特征提取新方法，该方法以数学形态颗粒分析为理论基础，在形态运算中引入腐蚀和膨胀算子，以计算出的广义数学形态颗粒值作为特征指标，定量地反映滚动轴承的性能退化程度。分别通过仿真信号和实例信号对该方法进行了有效性验证。在此基础上，为准确拟合滚动轴承性能退化过程的整体趋势与随机波动规律，将灰色马尔科夫模型应用到滚动轴承剩余寿命预测中，从而建立一种基于广义数学形态颗粒与灰色马尔科夫模型的剩余寿命预测方法。依托杭州轴承试验研究中心进行了滚动轴承疲劳寿命强化试验，以采集得到的轴承内圈全寿命试验数据验证了方法的有效性。