Signal Filter Cut-Off Frequency Determination to Enhance the Accuracy of Rail Track Irregularity Detection and Localization

Bhardwaj, Bhavana; Bridgelall, Raj; Chia, Leonard; Lu, Pan; Dhingra, Neeraj

doi:10.1109/jsen.2019.2947656

Cited by 9 publications

(9 citation statements)

References 23 publications

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“…Bhardwaj et al [ 322 ] researched a system with inertial sensors onboard service trains for the detection and localization of railroad track irregularities. In such a system, noise and reduced signal strength were additionally generated by the geospatial position of the GPS receivers and the non-uniform sampling of the inertial sensors.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

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Condition monitoring of rail transport systems has become a phenomenon of global interest over the past half a century. The approaches to condition monitoring of various rail transport systems—especially in the context of rail vehicle subsystem and track subsystem monitoring—have been evolving, and have become equally significant and challenging. The evolution of the approaches applied to rail systems’ condition monitoring has followed manual maintenance, through methods connected to the application of sensors, up to the currently discussed methods and techniques focused on the mutual use of automation, data processing, and exchange. The aim of this paper is to provide an essential overview of the academic research on the condition monitoring of rail transport systems. This paper reviews existing literature in order to present an up-to-date, content-based analysis based on a coupled methodology consisting of bibliometric performance analysis and systematic literature review. This combination of literature review approaches allows the authors to focus on the identification of the most influential contributors to the advances in research in the analyzed area of interest, and the most influential and prominent researchers, journals, and papers. These findings have led the authors to specify research trends related to the analyzed area, and additionally identify future research agendas in the investigation from engineering perspectives.

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Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

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“…Prior to producing the EAR, the workflow included distance interpolation from a spatial reference position to align the signals and to extract approximately equal length segments, as described in previous work [26]. Additional processes described in previous work included computing a Fast Fourier Transform (FFT) and ensemble average of the FFT (EA-FFT) across multiple signals to inform an appropriate filter cutoff frequency, and applying a finite impulse response (FIR) low-pass filter [9]. Fig.…”

Section: Methodsmentioning

confidence: 99%

“…However, the non-uniform sample rate and inaccurate geospatial position estimates from low-cost GPS receivers pose significant challenges for signal processing, feature extraction, and signal combination [7], [8]. Appropriate noise filtering is necessary to maximize the signal-to-noise ratio (SNR) of each signal prior to feature extraction [9]. This research applies a method of roughness feature extraction called the Road Impact Factor (RIF) transform.…”

Section: Introductionmentioning

confidence: 99%

Signal Feature Extraction and Combination to Enhance the Detection and Localization of Railroad Track Irregularities

Bhardwaj

Bridgelall

et al. 2021

IEEE Sensors J.

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Tracks are critical and expensive railroad asset, requiring frequent maintenance. The stress from heavy car axle loads increases the risk of deviations from uniform track geometry. Irregularities in track geometry, such as track warping, can cause an excessive harmonic rocking condition that can lead to derailments, traffic delays, and associated financial losses. This paper presents an approach to enhance the location identification accuracy of track geometry irregularities by combining measurements from sensors aboard Hi-Rail vehicles. However, speed variations, position recording errors, low GPS update rates, and the non-uniform sampling rates of inertial sensors pose significant challenges for signal processing, feature extraction, and signal combination. This study introduces a method of extracting features from the fused data of inertial sensors and GPS receivers with multiple traversals to locate and characterize irregularities of track geometry. The proposed method provides robust detection and enhanced accuracy in the localization of irregularities within spatial windows along the track segment. Tradeoff analysis found that the optimal spatial window size is 5-meter.

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“…e acceleration data of the simulation test were filtered with a CFC60 SAE filter. e acceleration data of the sled test were measured at a sampling rate of 1000 Hz and filtered with a CFC60 filter [27]. In this paper, the x-axis pointed forward from the sled or vehicle.…”

Section: Correlation Model Between the Velocity Variation Error And Influencing Factorsmentioning

confidence: 99%

Method to Correct the Velocity Variation Information of an Automatic Crash Notification System in Vehicle‐to‐Rigid Barrier Frontal Collisions

Lü

Shu

2021

Shock and Vibration

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Automatic crash notification systems (ACNSs) play a key role in post-accident safety. To improve the accuracy and efficiency of ACNSs, a method to correct the velocity variation information of ACNSs was established. First, after the acceleration data of sled crash tests were analysed, the factors affecting the accuracy of the velocity variation information were determined, and the influence of the discrimination threshold and acceleration curve shape on the velocity variation information was examined. Second, according to the acceleration data generated by the simulation model of a sled crash, the correlation between the accuracy of the velocity variation information and influencing factors was modelled. Third, an automatic crash notification algorithm involving a velocity variation correction function (VVCF) was proposed based on the correlation model. Finally, to verify its reliability, the improved algorithm was applied to an automatic crash notification system (ACNS) terminal. The validation results show that the ACNS terminal can accurately identify collisions and transmit accident information. Moreover, more accurate velocity variation information can be retrieved.

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Signal Filter Cut-Off Frequency Determination to Enhance the Accuracy of Rail Track Irregularity Detection and Localization

Cited by 9 publications

References 23 publications

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Signal Feature Extraction and Combination to Enhance the Detection and Localization of Railroad Track Irregularities

Method to Correct the Velocity Variation Information of an Automatic Crash Notification System in Vehicle‐to‐Rigid Barrier Frontal Collisions

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