自适应带宽核密度估计在旋转机械劣质监测数据识别中的应用

The abnormal operating environments, human factor interference and acquisition equipment failures might cause abnormal values or missing data irrelevant to the equipment health status in monitoring data of rotating machinery, resulting in misjudgment of mechanical health status and improper formulation of maintenance strategy. Therefore, an identification method of inferior monitoring data was proposed based on adaptive bandwidth kernel density estimation. Firstly, the zero drift and local noise were "impacted" by integrating the collected data in frequency domain and the kurtosis index after integration was calculated. Then the local mean error was used to adaptively select the Gaussian kernel bandwidth, the probability density function of kurtosis index was obtained, and the boundary of 95% confidence interval was used as the identification threshold of inferior data. Finally, the extraction method was verified by the whole life data of axle durability monitoring. The results show that compared with the fixed bandwidth and the kernel density estimation method based on quadtree segmentation algorithm, the proposed method has better recognition effectiveness on poor quality monitoring data.