滚动轴承退化指标选取方法研究

Method Study of Selecting Indicator of Rolling Bearings Degradation Process

针对滚动轴承退化过程指标选取问题,提出了一种基于噪声辅助的多维EMD(Noise-Assisted Multivariate EMD)和主成分分析(PCA)相结合来提取退化指标的方法。该方法首先利用NA-MEMD对同源双通道信号和噪声辅助通道信号进行分解得到一系列多元IMF分量,然后采用相关系数准则选取敏感分量重构信号,其次计算出轴承退化过程中重构信号的退化指标序列,再根据序列的单调性和鲁棒性,选择优良指标进行PCA融合,最后把第一主成分作为反映滚动轴承退化过程的最终指标。对PRONOSITS平台提供的全寿命周期的数据进行分析,结果表明,在滚动轴承的退化过程中,较单一指标,基于NA-MEMD和PCA融合的指标能够比较完整的表征滚动轴承的退化过程。

Aiming at how to select proper indicator of rolling bearings degradation process,a method combining noise-assisted multivariate EMD with principle component analysis is proposed to extract the degradation indicator. NA-MEMD is firstly used to decompose the two initial channel and a noise channelsignals to gain a series ofmultiple IMF components,the sensitive ones are reconstructed which is chosen by correlation coefficient criteria. After figuring out degradation indicator sequences of reconstructed signals in the degradation process,some fine indicators are then extracted based on monotonicity and robustness of the sequences. By the use of PCA fusion,the selected indicators are combined. At last,the first principle component is considered as final indicator of rolling bearing＇s degradation process. The full life cycle data is analyzed which is provided by PRONOSITS platform.It indicates that the indicator based on NA-MEMD and PCA represents the rolling bearing＇s degradation process better than any signal indicator.