Keywords: squat, key performance indicators, axle box acceleration, measurement. .

This paper proposes a growth model for squats in railway infrastructure for design of key performance indicators (KPIs) in a maintenance time horizon. The main concept of the paper is to make the growth model robust and predictive by the capturing all possible growth scenarios over time. The squats are detected using an axle box acceleration (ABA) measurement system. A methodology is proposed to estimate the visual length of squats using the power spectral density of the ABA signal. Next, a robust model is employed for predicting the visual length, including fast, average and slow growth prediction scenarios. The purpose of using the robust model is to consider stochasticity the squat growth in order to cover the most important uncertainties. Relying on the prediction model, five KPIs are defined to reflect track condition over time, in five different segments of the track from Eindhoven to Weert in the Dutch railway network. By using the proposed prediction model, the infrastructure manager will be able to plan condition based maintenance for tracks.

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