Prognostics with autoregressive moving average for railway turnouts

Turnout systems are one of the most critical systems on railway infrastructure. Diagnostics and prognostics on turnout system have ability to increase the reliability & availability and reduce the downtime of the railway infrastructure. Even though diagnostics on railway turnout systems have been reported in the literature, reported studies on prognostics in railway turnout system is very sparse. This paper presents autoregressive moving average model based prognostics on railway turnouts. The model is applied to data collected from real turnout systems. The failure progression is obtained manually using the exponential degradation model. Remaining Useful Life of ten turnout systems have been reported and results are very promising

A simple state-based prognostic model for railway turnout systems

The importance of railway transportation has been increasing in the world. Considering the current and future estimates of high cargo and passenger transportation volume in railways, prevention or reduction of delays due to any failure is becoming ever more crucial. Railway turnout systems are one of the most critical pieces of equipment in railway infrastructure. When incipient failures occur, they mostly progress slowly from the fault free to the failure state. Although studies focusing on the identification of possible failures in railway turnout systems exist in the literature, neither the detection nor forecasting of failure progression has been reported. This paper presents a simple state-based prognostic method that aims to detect and forecast failure progression in electro-mechanical systems. The method is compared with Hidden Markov Model based methods on real data collected from a railway turnout system. Obtaining statistically sufficient failure progression samples is difficult considering that the natural progression of failures in electro-mechanical systems may take years. In addition, validating the classification model is difficult when the degradation is not observable. Data collection and model validation strategies for failure progression are also presented