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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 literature, neither the detection nor forecasting of failure progression has been reported. This paper presents a simple state-based prognostic (SSBP) method that aims to detect and forecast failure progression in electromechanical 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 electromechanical 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.