Expectation–Maximization Approach to Fault Diagnosis With Missing Data

This paper introduces a data-driven approach for fault diagnosis in the presence of incomplete monitor data. The expectation-maximization (EM) algorithm is applied to handle missing data in order to obtain a maximum-likelihood solution for the discrete (or categorical) distribution. Because of the nature of categorical distributions, the maximization step of the EM algorithm is shown in this paper to have an easily calculated analytical solution, making this method computationally simple. An experimental study on a ball-and-tube system is investigated to demonstrate advantages of the proposed approach.