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In this work we present the results of a field trial with a FBG sensor array system for the real time monitoring of railway traffic and for the structural health monitoring of both the railway track and train wheels. The test campaign is performed on the 2nd line of Milan metropolitan underground, employing more than 50 FBG sensors along 1.5 km of the rail track, where the trains are tested during daily passenger rail transport, with a roughly maximum speeds of 90 km/h. The measurements were continuatively performed for over 6 months, with a sampling frequency of about 400 Hz. The large amount of data/sensors allows a rather accurate statistical treatment of the measurement data and permits, with dedicated algorithms, the estimation of rail and wheel wear, key traffic parameters such as the number of axles, the train speed and load, and, in the next future, the detection of localized imperfections. [ABSTRACT FROM AUTHOR]

Abstract: A novel technique for the identification of the tire–road grip conditions is presented. This is based on the use of strain information inside the tire, from which relevant characteristics of the tire–road contact can be extracted also through a factor named area slip ratio. This process forms the basis of a technology for grip identification that requires a new model of the tire dynamics. The model permits to determine closed form analytical relationships between the measured strain and the area slip ratio. On this basis, a procedure that can extract the contact kinematic parameter from the time history of the internal strain of the rolling tire is presented. Numerical simulations offer the chance to validate the identification algorithm. [Copyright &y& Elsevier]