Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

Abstract

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.

Experimental Investigation on Condition Monitoring Opportunities of Tramway Tracks

“…The irregular vehicle movement can refer to track irregularities and different exciter frequency components are registered in the recorded signal, depending on the defect types. There are numerous solutions in literature to detect different wavelengthtrack defects by using the techniques of signal processing, for instance conventional Fourier Transform [18] [25] [26] and wavelet approaches [9] [27] to [29]. Due to the fact that in tramway operation the sensors cannot be mounted on axle boxes, so a not-commonly used vehicle dynamic measurement set-up is carried out by the Authors [8] [30] to detect poor track geometry and structural problems.…”

Pareto-Based Maintenance Decisions for Regional Railways With Uncertain Weld Conditions Using the Hilbert Spectrum of Axle Box Acceleration

“…The detection and assessment of rail surface defects are normally realized by analyzing frequency-based features. Previously, methods such as the power spectrum density [9], short-time Fourier transform [10] and wavelet transform [11] methods have been adopted for detecting defects in wheels, axle bearings and rails. ABA Pareto-based maintenance decisions for regional railways with uncertain weld conditions using the Hilbert spectrum of axle box acceleration Alfredo Núñez, Senior Member, IEEE, Ali Jamshidi, Hongrui Wang, Student Member, IEEE R Pareto-based maintenance decisions for regional railways with uncertain weld conditions using the Hilbert spectrum of axle box acceleration signals from regional railways are particularly noisy and are affected not only by train speed and wheel conditions but also by a less accurate GPS location.…”

Enhancement of Fault Feature Extraction from Displacement Signals by Suppressing Severe End Distortions via Sinusoidal Wave Reduction

“…Owing to the long term service of rotating machinery under hash working conditions, faults are likely to happen on mechanical components, which may lead to economic loss as well as catastrophic accidents [3]. Vibration measurement plays an important role in detecting mechanical faults at an early stage, because it carries abundant information regarding the failure of rotating machines [1,[3][4][5][6].…”