Railway track maintenance is becoming a real challenge for Railway Engineers due to the need of meeting increasingly high quality requirements by means of cost-effective procedures. Frequently, this can be only achieved by implementing some technological developments from other fields into the railway sector, such as Digital Signal Processing. Indeed, the present work delves into data acquisition and processing techniques in order to enhance track surveying processes. For this purpose, run tests on the Metropolitan Rail Network of Valencia (Spain) were carried out, and axlebox accelerations were gathered and analysed in different ways. The results determined the optimal sampling and filtering frequencies as well as the location of accelerometers along the train. Furthermore, by means of spectral analysis and time-frequency representations, diverse track defects, track singularities and vibration modes can be clearly identified. It is shown how, with a Hamming time window of 0.5 s and an overlapping of 95%, a wide set of track defects can be detected, without the need of complementary analyses. These values yield the best results as they are a good compromise between time and frequency resolution and allow for appropriate pattern recognition of the corresponding track singularities and resonant frequencies.

Undergrounds and other metropolitan railway systems are characterized by their intense traffic, lasting up to 20 h per day, and so they need their maintenance work programmes to be optimized, implying an optimization of the monitoring processes. This article proposes an alternative to the traditional optical methods for monitoring rail profiles which can only be carried out by special vehicles. This is a new procedure that obtains the rail profile by means of inertial methods. The model this work is based on takes its input from the vertical accelerations produced in railway axles measured in trains running on regular services and calculates the rail irregularities that have originated them. The model uses the Fourier transform in order to solve the equations and find the transfer function that relates the input function and the output function in the frequency domain. The solution is then reverted into the time domain by applying the inverse Fourier transform. Data input comes from real measurements taken on line 9 of the Madrid underground, and the model's effectiveness was then analysed by comparing the output data with the rail profile taken using optical methods.

No abstract

No abstract

No abstract