Axle Box Acceleration Measurements Research Articles

A Methodology for Continuous Monitoring of Rail Corrugation on Subway Lines Based on Axlebox Acceleration Measurements

Rail corrugation is a degradation phenomenon that manifests as a quasi-periodic irregularity on the running surface of the rail. It is a critical problem for urban railway lines because it induces ground-borne vibrations transmitted to the buildings near the infrastructure, causing complaints from the inhabitants. A typical treatment to mitigate the rail corrugation problem is the periodic grinding of the rails, performed by dedicated vehicles. The scheduling of rail maintenance is particularly critical because it can be performed only when the service is interrupted. A procedure for the continuous monitoring of rail corrugation is proposed, based on axlebox acceleration measurements. The rail irregularity is estimated from the measured acceleration by means of a frequency domain model of vertical dynamics of the wheel–rail interaction. The results obtained by using two different methods (a state-of-the-art method and a new one) are compared. Finally, the study of the evolution of the power content of the rail irregularity enables the identification of the track sections where corrugation is developing and rail grinding is necessary.

Evaluating Rail Surface Roughness from Axle-Box Acceleration Measurements: Computational Metrology Approach

This study develops a new methodology based on computational metrology techniques to measure rail surface roughness from vertical axle-box accelerations. A VIA rail passenger rail car, operating in eastern Ontario, Canada, was instrumented with accelerometers on the car body, trucks, and axle-boxes. The rail surface was measured by a heavy track geometry inspection car. A Gaussian filter was applied to the measured rail surface data and the rail surface roughness was quantified by the root mean square (RMS), as recommended by ISO 11562 for evaluating engineering surfaces. Values of rail surface RMS roughness calculated from axle-box accelerometer data are verified with the measured surface RMS roughness. The overlap ratio and length of the moving window over which the rail surface RMS roughness is calculated are studied with respect to roughness wavelengths, statistical considerations, and maintenance planning purposes. The effect of rail car operating speed as well as the difference between axle-box accelerations measured at two axles of the instrumented rail car on the accuracy of estimating the rail surface RMS roughness are assessed. Filtering techniques and application limitations for calculating roughness are also discussed. The results of this study suggest axle-box acceleration data provide a useful assessment of rail surface roughness for the typical wavelengths between 3 and 25 m and a complementary technique to light and heavy track geometry inspections.

On the genesis of squat-type defects on rails – Toward a unified explanation

Different origination theories for squat-type rail defects are examined and confronted with experimental evidence. Based on the morphology of the three-dimensional defect crack pattern, with a leading crack directivity governed by the tangential stress history, it is shown that theories assuming dynamic wheel-rail interaction as a direct initiation mechanism of the double-lobed defect violate the causality principle. Rail surface anomalies of three categories increase the tangential stress exposure and thus the risk of defect development: pertaining to the material properties, contact geometry (including both the global scale, involving the dynamic stress level transmitted by the contact area, and the local scale, involving transient stress redistribution within this area), and contact stiffness. Both detection measures focusing on geometrical surface deviations giving rise to dynamic wheel-rail interaction (such as axle-box acceleration measurements) and the idea of a critical diameter for such imperfections are inadequate. Instead, and apart from the surface geometry, steel micro-cleanliness and chemical composition, phase transformation mechanisms of surface material (due to operational conditions or treatment by grinding or milling) and welding deserve attention. Fluid entrapment remains a potentially contributing factor in early growth, while crack face oxidation and corresponding volumetric expansion may contribute at any stage after initiation.

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

This paper presents a Pareto-based maintenance decision system for rail welds in a regional railway network. Weld health condition data are collected using a train in operation. A Hilbert spectrum-based approach is used for data processing to detect and assess the weld quality based on multiple registered dynamic responses in the axle box acceleration measurements. The assessment of the welds is stochastic in nature and variant over time, so a set of robust and predictive key performance indicators is defined to capture the weld degradation dynamics during a given maintenance period. Using a scenario-based approach, two objective functions are defined, performance and the number of weld replacements. Evolutionary multiobjective optimization is employed to optimize the objective functions so that the tradeoffs between performance and cost support decision-making for railway network maintenance. The results of the proposed methodology show that the infrastructure manager can localize field inspections and maintenance efforts on the area with the most critical welds. To showcase the capability of the proposed methodology, measurements from a regional railway network in Transylvania and Romania are employed.

A decision support approach for condition-based maintenance of rails based on big data analysis

In this paper, a decision support approach is proposed for condition-based maintenance of rails relying on expert-based systems. The methodology takes into account both the actual conditions of the rails (using axle box acceleration measurements and rail video images) and the prior knowledge of the railway track. The approach provides an integrated estimation of the rail health conditions to support the maintenance decisions for a given time period. An expert-based system is defined to analyse interdependency between the prior knowledge of the track (defined by influential factors) and the surface defect measurements over the rail. When the rail health conditions is computed, the different track segments are prioritized, in order to facilitate grinding planning of those segments of rail that are prone to critical conditions. In this paper, real-life rail conditions measurements from the track Amersfoort-Weert in the Dutch railway network are used to show the benefits of the proposed methodology. The results support infrastructure managers to analyse the problems in their rail infrastructure and to efficiently perform a condition-based maintenance decision making.

Smart technology solutions for the NeTIRail-INFRA case study lines: Axle box acceleration and ultra-low cost smartphones

In this paper, we present preliminary results on the development of smart technology solutions for lower density railway lines. The goal is to reach a cost effective inspection and asset management to minimize maintenance interventions time/cost without dedicated inspection vehicles. The proposed methods include axle box acceleration measurements and ultra-low cost smartphones. The collected data will be further used to increase knowledge of the condition of the railway track and to estimate the comfort of passengers. In order to make use of the data, the data is interpreted and converted from monitoring information into management information. Feasibility and preliminary studies were conducted in the railway lines of Romania. The results presented in this paper were obtained in the framework of the H2020 project NeTIRail-INFRA, Work Package 4: Monitoring and Smart Technology.

Evaluating Degradation at Railway Crossings Using Axle Box Acceleration Measurements.

In this paper, we investigate the capability of an axle box acceleration (ABA) system to evaluate the degradation at railway crossings. For this purpose, information from multiple sensors, namely, ABA signals, 3D rail profiles, Global Positioning System (GPS) and tachometer recordings, was collected from both nominal and degraded crossings. By proper correlation of the gathered data, an algorithm was proposed to distinguish the characteristic ABA related to the degradation and then to evaluate the health condition of crossings. The algorithm was then demonstrated on a crossing with an unknown degradation status, and its capability was verified via a 3D profile measurement. The results indicate that the ABA system is effective at monitoring two types of degradations. The first type is uneven deformation between the wing rail and crossing nose, corresponding to characteristic ABA frequencies of 230–350 and 460–650 Hz. The second type is local irregularity in the longitudinal slope of the crossing nose, corresponding to characteristic ABA frequencies of 460–650 Hz. The types and severity of the degradation can be evaluated by the spatial distribution and energy concentration of the characteristic frequencies of the ABA signals.

Robust and Predictive Fuzzy Key Performance Indicators for Condition-Based Treatment of Squats in Railway Infrastructures

This paper presents a condition-based treatment methodology for a type of rail surface defect called squat. The proposed methodology is based on a set of robust and predictive fuzzy key performance indicators. A fuzzy Takagi-Sugeno interval model is used to predict squat evolution for different scenarios over a time horizon. Models including the effects of maintenance to treat squats, via either grinding or replacement of the rail, are also described. A railway track may contain a huge number of squats distributed in the rail surface with different levels of severity. This paper proposes to aggregate the local squat interval models into track-level performance indicators including the number and density of squats per track partition. To facilitate the analysis of the overall condition, a single fuzzy global performance indicator per track partition is proposed based on a fuzzy expert system that combines all the scenarios and predictions over time. The proposed methodology relies on the early detection of squats using axle box acceleration measurements. Real-life measurements from the Meppel-Leeuwarden track in the Dutch railway network are used to show the benefits of the proposed methodology. The use of robust and predictive fuzzy performance indicators facilitates the visualization of the track health condition and eases the maintenance decision process.

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.

Monitoring bolt tightness of rail joints using axle box acceleration measurements

Rail joints are a weak component in railway tracks because of the large impact and wheel-rail contact forces. Every train passage contributes to the deterioration of rail joints, causing visible (e.g. battered rails) and invisible (e.g. loose bolts) damages. The invisible damage cannot be detected by the commonly performed visual inspection, which is labor intensive, unreliable, intrusive and unsafe. In this paper, a vehicle-borne monitoring system is used to automatically detect and assess the tightness condition of bolts at rail joints. The monitoring method is developed based on field Axle Box Acceleration (ABA) measurements using different bolt tightness conditions. The suitability of the method is assessed by bolt tightness prediction and verification of a set of rail joints in the tram network of Sheffield, United Kingdom. The results show that ABA system can be employed to monitor bolt tightness conditions at rail joints.With this information better planning for selective preventive maintenance actions can be taken over rail joints.

Improvements in Axle Box Acceleration Measurements for the Detection of Light Squats in Railway Infrastructure

Squats are a type of short-wave rolling contact fatigue defect whose early detection can contribute to cost reductions in the railway industry. This paper demonstrates how the early detection of squats is possible via enhanced instrumentation based on axle box acceleration (ABA) and adequate postprocessing. Three improvements are discussed. The first corresponds to the use of longitudinal ABA to enhance measurement sensitivity to light squats. Compared to vertical ABA, longitudinal ABA does not contain the vibrations of the rail, fastening, sleepers, and ballast, and thus, the impact-related vibration is considerably stronger in the signal. The second improvement considers the use of multiple sensors, noise-reduction techniques, and repeated measurements. Due to hunting, the wheels of a measuring train do not always pass over small squats; thus, light squats are more likely to be detected using multiple sensors and multiple measurement runs. The third improvement concerns the signal-processing solution for the reduction of disturbances from wheel defects. Extensive field measurements show that these improvements make the characteristics of squats more visible in signals and allow the squats to be distinguished from vibrations of other origins.

Axle box acceleration: Measurement and simulation for detection of short track defects

Abstract Short track defects cause large dynamic contact forces at the wheel–rail interface, leading to fast deterioration of the track. Early detection of such defects is very important for timely maintenance. The track technical state can be assessed with the aid of axle box acceleration measurements, which can indicate short track defects like squats, welds with poor finishing quality, insulated joints, corrugation, etc. As a first attempt to determine a quantitative relationship between the characteristics of the accelerations and the track defects, axle box acceleration at a squat and a thermite weld were simulated through finite-element modeling. The magnitude and frequency contents of axle box acceleration at squats agreed with measurements. The results for welds had some discrepancies, which were mainly attributed to the fact that the modeled geometry of the weld deviated from the real one.