Determination of burn‐in parameters and residual life for highly reliable products

Abstract

Today, many products are designed and manufactured to function for a long period of time before they fail. Determining product reliability is a great challenge to manufacturers of highly reliable products with only a relatively short period of time available for internal life testing. In particular, it may be difficult to determine optimal burn-in parameters and characterize the residual life distribution. A promising alternative is to use data on a quality characteristic (QC) whose degradation over time can be related to product failure. Typically, product failure corresponds to the first passage time of the degradation path beyond a critical value. If degradation paths can be modeled properly, one can predict failure time and determine the life distribution without actually observing failures. In this paper, we first use a Wiener process to describe the continuous degradation path of the quality characteristic of the product. A Wiener process allows nonconstant variance and nonzero correlation among data collected at different time points. We propose a decision rule for classifying a unit as normal or weak, and give an economic model for determining the optimal termination time and other parameters of a burn-in test. Next, we propose a method for assessing the product's lifetime distribution of the passed units. The proposed methodologies are all based only on the product's initial observed degradation data. Finally, an example of an electronic product, namely contact image scanner (CIS), is used to illustrate the proposed procedure.

Estimating Remaining Useful Life for Degrading Systems with Large Fluctuations

“…Under a recognized definition of lifetime, that is, first hitting time (FHT) [6,7], many RUL estimation approaches have been reported in literature [8][9][10]. References [6,11] provide a detailed overview of the degradation modeling and RUL predicting method.…”

Reliability Assessment of High-Speed Train Bearing Based on Random Performance Degradation in Minimum Sample

“…Wang [7] has studied the two kinds of the lifetime prediction based on Gamma processes. Pan [8] proposes a bivariate BirnbaumSaunders distribution and its marginal distributions to approximate the reliability function [9].…”

A novel Wiener process model with measurement errors for degradation analysis

“…It is worth noticing, however, that Tseng et al [13] restricted their attention to the case (1) (2) ( ) ( ) t t Λ = Λ in their study, because they concluded that it was rather difficult to obtain an explicit form of the lifetime distribution when (1) (2) ( ) ( ) t t Λ ≠ Λ . Therefore, they only proposed a general Wiener process model form with two transformed time scales without any essential studies or applications.…”