State-Based Prognostics with State Duration Information

Failure prediction (i.e. prognostics) is critical for effective maintenance because it greatly impacts the competitiveness of organizations through its direct connection with operating and support costs, system availability, and operational safety. In recent years, research has focused on state-based prognostics that forecast future progression by first identifying the current state. The duration spent in a state is a factor that influences the expected time to be spent in that state in the future; however, previous works on state-based prognostics have ignored the effect of duration. Hidden Markov Models are the most famous state-based prognostics methods in the literature with practicality problems such as computational complexity, requirement of excessive data, and dependency on initialization. This paper presents a new, simple and easy to implement state-based prognostic method using state duration information. The presented method is applied to two real systems (railway turnout systems and drill bits), and the results are compared with the existing methods presented in the literature. The results show that the presented method outperforms the existing methods. Copyright (c) 2012 John Wiley & Sons, Ltd.