Parameter estimation of lifetime distribution for the meta-action unit with uncertainty failure modes under type-I censored data

Abstract

This paper presents a parameter estimation method for the lifetime distribution of the Meta-Action Unit (MAU) with uncertainty failure modes under type-I censored data. The MAU is regarded as the basic functional unit to accomplish the function of mechanical equipment, and its failure modes are classified according to the abnormal kinematic parameters in Meta-Action (MA), which are more succinct than the traditional mechanical failure modes on parts. However, there is some uncertain information about the failure data and censored data of MAU because of the technology limitations and the space accessibility constraints for monitoring the kinematic parameters of MA, which uncertainty information can impact the parameter estimates of MAU lifetime distribution. In order to avoid the impacts on the estimating accuracy of distribution parameters, the evidential likelihood function based on the belief function theory is constructed in view of the credibility level of the failure data and censored data. In addition, the Evidential Expectation Maximization (E2M) algorithm is proposed to estimate the parameters of the mixed exponential distribution of MAU lifetime under type-I censored data. Finally, an application of an Automatic Pallet Changer (APC) is used to illustrate the validity of the MAU failure modes classification. The simulations of the E2M algorithm are conducted to show that the proposed parameters estimation method can integrate uncertain information in the failure data and the censored data, and obtain more stable results than those based on the conventional Expectation-Maximization (EM).