International Journal of Performability Engineering, Vol. 2, No. 2, pp. 149-161, April 2006

Methods for Binning and Density Estimation of Load Parameters for Prognostics and Health Management

N. Vichare, P. Rodgers, and M. Pecht
CALCE - Electronic Products and Systems Center
University of Maryland
College Park, MD 20742


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.

Click here for complete article.

This article was originally published in the International Journal of Performability Engineering (IJPE)   (

[Home Page] [Articles Page]
Copyright © 2006 by CALCE and the University of Maryland, All Rights Reserved