Methods for binning and density estimation of load parameters for prognostics and health management

Environmental and usage loads experienced by a product in the field can be monitored in-situ and used with prognostic models to assess and predict the reliability of the product. This paper presents an approach for recording in-situ monitored loads in a condensed form without sacrificing the load information required for subsequent prognostic assessments. The approach involves optimally binning data in a manner that provides the best estimate of the underlying probability density function of the load parameter. The load distributions were developed using non-parametric histogram and kernel density estimation methods. The use of the proposed binning and density estimation techniques with a prognostic methodology were demonstrated on an electronic assembly.