Detection of early squats by axle box acceleration

This thesis discusses a new method for detection of short track irregularities, particularly squats, with axle box acceleration (ABA) measurements. A squat is a surface initiated short track defect, associated with high frequency vibrations of the wheel-rail system. High stresses in the contact patch at squats cause accumulation of plastic deformation of the rail and growth of cracks. Cracks growing in the subsurface can cause a rail fracture. Light squats can be treated by grinding of the rail surface; while mature squats lead to replacement of the rail section. For cost effective maintenance policy and operational safety squats should be detected at an early stage. Detection of light squats is the main aim of this study. Till now, ultrasonic measurements have been mainly used for detection of squats. By that method the depth of cracks is measured; hence, it is applicable only to detection of severe squats with sufficiently large cracks. In the present work, ABA measurements were employed. The advantages associated with this method are that ABA measurements can be performed on standard operating vehicles travelling with usual traffic speeds and squats can be detected at their early stage. The first goal of this study was to find a relationship between squats and ABA characteristics, such as magnitude and frequency content, and apply them for detection of squats. To this end, a three-dimensional finite element (FE) model was applied for dynamic simulations of the wheel-track interaction in the high frequency range. By parameter variation, the influence of the geometry of squats, speed of the train and location of the squats relative to the sleepers on ABA characteristics was studied. Local frequency characteristics of ABA at squats were obtained and their relation with the severity of squats was established. These frequency characteristics can be applied for detection of squats and their assessment. The second goal was to improve the signal-to-noise ratio of ABA measurements to enable detection of light squats. Several methods to improve signal-to-noise ratio of ABA measurements were suggested. These included noise reduction techniques, reduction of disturbances from the wheel defects and signal enhancement by improvement of the measuring system by using longitudinal ABA. Owing to the improvement of the signal-to-noise ratio, the hit rate of moderate squats increased from 60% to 100% and the hit rate of light squats together with trivial defects (small rail surface defects which are so small that they will be worn away) increased from 57% to 85%. Since light squats are larger than trivial defects and, therefore, easier to detect, the hit rate of light squats, which depends on the threshold that separates light squats from trivial defects, is higher. The third goal was to develop an algorithm for automatic detection of squats, which enables continuous analysis of track. The initial results indicated that 78% of light squats and trivial defects can be detected automatically by ABA. The hit rate of severe squats was 100%. The presented ABA method enables automatic detection of squats at their earliest stage, when preventive and early corrective actions can be taken. The employment of such method can significantly reduce life cycle costs of a track infected by squats.Structural EngineeringCivil Engineering and Geoscience

Method and instrumentation for detection of rail defects, in particular rail top defects

A method and instrumentation for detection of rail defects, in particular rail top defects, in a railway-track by measuring an axle box acceleration signal of a rail vehicle, wherein a longitudinal axle box acceleration signal is used as a measure to detect the occurrence of said rail defects, in particular rail top defects. The method also includes measuring a vertical axle box acceleration signal of said rail vehicle, whereby the longitudinal axle box acceleration signal is used in combination and simultaneously with said vertical axle box acceleration signal. It is further preferred that the longitudinal axle box acceleration signal is used to remove from said vertical axle box acceleration signal a signal-part that relates to vibrations of the rail vehicle's wheelset, including the bearing and axle box (3), and that the axle box acceleration signals are filtered for removing signal-parts contributed by vibrations of the track, including the rail (1), rail pads, fasteners, sleepers, and ballast.Design and ConstructionCivil Engineering and Geoscience

Automatic detection of squats in railway infrastructure

This paper presents an automatic method for detecting railway surface defects called “squats” using axle box acceleration (ABA) measurements on trains. The method is based on a series of research results from our group in the field of railway engineering that includes numerical simulations, the design of the ABA prototype, real-life implementation, and extensive field tests.We enhance the ABA signal by identifying the characteristic squat frequencies, using improved instrumentation for making measurements, and using advanced signal processing. The automatic detection algorithm for squats is based on wavelet spectrum analysis and determines the squat locations. The method was validated on the Groningen–Assen track in The Netherlands and accurately detected moderate and severe squats with a hit rate of 100%, with no false alarms. The methodology is also sensitive to small rail surface defects and enables the detection of squats at their earliest stage. The hit rate for small rail surface defects was 78%.Railway Engineerin

Experimental Investigation Into the Condition of Insulated Rail Joints by Impact Excitation

This paper presents a feasibility study to determine if the health condition of Insulated Rail Joints (IRJs) can be assessed by examining their dynamic response to impact excitation. First, a reference dynamic behavior is defined in the frequency domain of 50-1200 Hz based on field hammer test measurements performed on a IRJ baseline (i.e., a set of IRJ without visible damage). Then, measurements on IRJs with different damage states are compared to the IRJ baseline response via the frequency response function (FRF) based statistical method. Three cases of IRJs are analyzed: a IRJ with a broken fastening, a IRJ with a damaged insulation layer and a IRJ with a rail top with plastic deformation. Combining hammer test measurements, hardness measurements and pictures of the IRJs, two frequency bands were identified as characteristic for damaged IRJs. In the identified high frequency band (1000-1150 Hz), the measured dynamic response with both a vehicle-borne health monitoring system and hammer tests shows a clear difference between the damaged IRJs and the IRJ baseline. Furthermore, different damage types may be able to be identified by examining the dynamic responses in the identified mid-frequency band (420-600 Hz). Further analysis over a larger number of IRJs may complete and support the promising results so that the information can be employed for the condition assessment and monitoring of IRJs.Structural EngineeringCivil Engineering and Geoscience

Key performance indicators using robust prediction modelling to consider squats in railway infrastructure

Accepted Author Manuscript, Paper 159Railway Engineerin

Health condition monitoring of insulated joints based on axle box acceleration measurements

This paper presents a health condition monitoring system for insulated rail joints (IRJs) based on axle box acceleration (ABA) measurements. The ABA signals from all the wheels of the measuring train are processed to extract those characteristics that better represent the quality of the IRJ. Then, different indicators are used for damage assessment, the most relevant being a set of frequency bands in the ABA power spectrum. A detection algorithm is proposed based on the derived frequency characteristics of the ABA signal. We compared the responses of IRJs in good condition with those in poor condition. Track inspections were performed to validate the health condition monitoring methodology in different IRJs of the Dutch railway network. The hit rate of IRJs detection was 84% for two validated tracks. The damage assessment procedure allowed to prioritize the IRJs that require maintenance. This information is useful for the railway inframanagers as it allows to predict where safety compromises will be faced. plants.Railway Engineerin