A Parameter Estimation Method for Stress- Strength Model Based on Extending Markov State-Space With Variable Transition Rates

Abstract

A stress-strength model usually has more than one failure mode since the component suffers at least two types of stresses, complicating the expression of the likelihood function and increasing the computational complexity of the parameter estimation for general distributions (non-exponential distributions). A phase-type distribution (also known as a PH distribution) is dense and has closure properties, which makes it suitable to reduce the computational complexity of the stress-strength model. The traditional expectation-maximization (EM) method for estimating the parameters of the PH distribution cannot be used directly when the strength changes over time since the PH distribution is a continuous-time Markov process that must satisfy the relevant properties of the infinitesimal generator in the Markov statespace. Therefore, a parameter estimation method based on extending the Markov state-space with variable transition rates for the stress-strength model is proposed. Both failure and censored samples are considered. First, the stress-strength model based on the PH distribution is briefly introduced, and the likelihood functions for different failure modes are derived. Subsequently, the principle of the method is described in detail, the derivation process of the relevant equations is provided, and the limitations of the method are discussed. The performance of the method is evaluated using two simulation cases.