Study on Fusion Indicator Construction Method for Bearing Degradation Condition Evaluation Based on Manifold Learning
The health indicators extracted by the traditional data fusion methods might be failure for characterizing the bearing degradation condition under the strong background noise,such as violent fluctuation,difficulty in balancing the global and lo-cal structures.As a result,a novel fusion health indicator construction method for bearing degradation condition evaluation is presented based on uniform manifold approximation and projected.First,the health indicators of bearing degradation condition were calculated in time and frequency domain,respectively,which were employed to produce an original high-dimensional fea-ture set.Then,the finer health indicators were selected from the high-dimensional feature set via defining the sensitive criterion for bearing degraded process.Finally,these selected sensitive features were fused by the uniform manifold approximation and pro-jection,meanwhile,the exponentially weighted moving average was utilized to make the fusion feature more smooth.Two run-to-failure bearing data sets were used to verify the effectiveness of the proposed method.The results validate that the presented method owns a better ability for enhancing the monotonicity and tendency of bearing health indicators than the traditional data fusion methods and overcomes the shortcomings of limited representation ability of single indicator.
NETL
NSTL
万方数据
维普
