Analyzing Major Track Quality Indices and Introducing a Universally Applicable TQI

Offenbacher, Stefan; Neuhold, Johannes; Veit, Peter; Landgraf, Matthias

doi:10.3390/app10238490

Cited by 24 publications

(20 citation statements)

References 21 publications

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“…where S z , S y , S w , and S e are the standard deviation of the vertical irregularities, horizontal irregularities, twist, and gauge, respectively. Besides these TQIs, Offenbacher et al [5] presented fourteen track indicators used by railway infrastructure managers in different countries and applied them for a real five-kilometer test section.…”

Section: Condition Assessmentmentioning

confidence: 99%

Prediction of Railway Track Condition for Preventive Maintenance by Using a Data-Driven Approach

Vale

Simões

2022

Infrastructures

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For scheduling track maintenance, infrastructure managers perform inspections to assess the track condition. When the irregularities are higher than the threshold limits, a track has a defect that should be corrected to avoid future failures or traffic disruption. Scheduling maintenance actions contribute to reliability and availability but demand the prediction of the evolution of track degradation. In recent years, several degradation models have been defined to forecast geometrical evolution over time and/or tonnage, mainly for heavy rail systems. Nevertheless, most of those models have limitations when dealing with measured data collected in different time intervals as happens in reality. To overcome this problem, a data-driven model based on the logistic binary function is presented and validated with real inspection measurements. The results prove that the model has a 91.1% success rate, an excellent discrimination ability, and a high sensitivity, classifying correctly 84.1% of inspections in need of maintenance. The model also has high specificity as it classifies 94.5% of inspections with no demand of maintenance action. The model is easy to implement, which is also an advantage for the track asset management with guaranty of excellent sensitivity and discrimination.

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Section: Condition Assessmentmentioning

confidence: 99%

Prediction of Railway Track Condition for Preventive Maintenance by Using a Data-Driven Approach

Vale

Simões

2022

Infrastructures

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“…[8][9][10][11][12][13][14][15][16][17][18][19][20][21]2024 ters, 25-70 meters, and 70-200 meters in length. The 200-meter long segment is the most commonly employed, although in certain instances, a specific track or even the entire network is assessed using just a single TQI value [23]. Recently, two approaches have been proposed to define the railway track segments, where both approaches have generated variable segment lengths along the railway track.…”

Section: Introductionmentioning

confidence: 99%

Use of Zero-crossings Segmentation for Track Quality Assessment

Dawod,

Terdik

2024

Acta Tech. Jaurinensis

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This study concerns track quality assessment of standard-gauge railways in the context of the Hungarian railway system. Data gathered by multipurpose track recording vehicles matched the EN 13,848 requirements. Track Quality Index (TQI) measurement systems (The Federal Railroad Administration (FRA), the Netherlands’, and the Chinese TQI) are considered where three types of predetermined segment techniques: separate, moving, and Zero-crossings segmentation are employed. The importance of track segmentation in quality assessment, which affects maintenance planning, is shown by key findings. For heterogeneous data, the TQIs might be deceptive, highlighting the need for alternatives. The robustness of the Zero-crossings method makes it possible to analyze deterioration factors in great detail and in some efficient way. Longer analytical segments and smoothing of the data improved precision. Based on empirical data, we advise considering a Zero-crossings strategy for precise and efficient track-quality evaluations. With the help of this study, track quality can be better evaluated for train systems.

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“…In addition to the evaluation of local defects, they also calculate track quality indices over predefined lengths. In practice, fixed and moving window standard deviations are widely used [16,17], but some railway operators also use specific evaluation methods based on area under curve and space curve length calculations [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Track Gauge and Alignment Parameters of Ballasted Railway Tracks Based on Real Measurements Using Signal Processing Techniques

et al. 2023

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This paper deals with the time-frequency characteristic analysis for track geometry irregularities using field data recorded by a comprehensive track inspection train. The parameters of the track gauge and the left and right rail alignment are considered to identify their characteristic wavelengths and the locations of their waveforms. In addition to the conventional time and frequency domain analysis, auto-adaptive signal decomposition techniques are used on four pre-selected track sections. During the time series analysis of the track gauge, the cumulative difference from the mean value is calculated, which makes it possible to distinguish the track section constructed with non-standard initial track gauges. The sensitive wavelengths of the track irregularities are obtained from the proper allocation of wavelength ranges in the Fourier Amplitude Spectrum of the original signal and the Fourier transform of the components detected by the Variational Mode Decomposition. This analysis can elucidate the wavelengths and positions of track irregularities that affect vehicle responses.

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Analyzing Major Track Quality Indices and Introducing a Universally Applicable TQI

Cited by 24 publications

References 21 publications

Prediction of Railway Track Condition for Preventive Maintenance by Using a Data-Driven Approach

Prediction of Railway Track Condition for Preventive Maintenance by Using a Data-Driven Approach

Use of Zero-crossings Segmentation for Track Quality Assessment

Investigation of Track Gauge and Alignment Parameters of Ballasted Railway Tracks Based on Real Measurements Using Signal Processing Techniques

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