Wheel Out-of-Roundness Detection Using an Envelope Spectrum Analysis

Gonçalves, Vítor; Mosleh, Araliya; Vale, Catarina; Montenegro, Pedro Aires

doi:10.3390/s23042138

Cited by 5 publications

(1 citation statement)

References 53 publications

(63 reference statements)

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“…The most commonly used wheel flat detection method is the stress-based method. In this method, the dynamic stress of the track when the train passes can be measured by different stress sensors such as strain gauges, accelerometers, and fiber Bragg gratings (FBG) [51][52][53][54][55][56][57][58][59][60][61][62][63][64][65].…”

Section: Stress-based Wheel Flat Signal Acquisition Methodsmentioning

confidence: 99%

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Feng

et al. 2023

Sensors

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Wheel flats are amongst the most common local surface defect in railway wheels, which can result in repetitive high wheel–rail contact forces and thus lead to rapid deterioration and possible failure of wheels and rails if not detected at an early stage. The timely and accurate detection of wheel flats is of great significance to ensure the safety of train operation and reduce maintenance costs. In recent years, with the increase of train speed and load capacity, wheel flat detection is facing greater challenges. This paper focuses on the review of wheel flat detection techniques and flat signal processing methods based on wayside deployment in recent years. Commonly used wheel flat detection methods, including sound-based methods, image-based methods, and stress-based methods are introduced and summarized. The advantages and disadvantages of these methods are discussed and concluded. In addition, the flat signal processing methods corresponding to different wheel flat detection techniques are also summarized and discussed. According to the review, we believe that the development direction of the wheel flat detection system is gradually moving towards device simplification, multi-sensor fusion, high algorithm accuracy, and operational intelligence. With continuous development of machine learning algorithms and constant perfection of railway databases, wheel flat detection based on machine learning algorithms will be the development trend in the future.

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Section: Stress-based Wheel Flat Signal Acquisition Methodsmentioning

confidence: 99%

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Feng

et al. 2023

Sensors

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Condition Monitoring of Rolling Stock Supported by Artificial Intelligence Technique

Mosleh,

Meixedo,

Ribeiro

et al. 2024

Digital Innovations in Architecture, Engineering and Construction

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Time series data mining for railway wheel and track monitoring: a survey

Lourenço,

Ribeiro,

Fernandes

et al. 2024

Neural Comput & Applic

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The railway sector has witnessed a significant surge in condition-based maintenance, thanks to the proliferation of sensing technologies and data-driven methodologies, such as machine learning. However, despite the plethora of algorithms designed to detect and classify track irregularities and wheel out-of-roundness, they often fall short when put to the test in real-world scenarios. These shortcomings typically stem from their inability to meet all four critical requirements for constructing an effective maintenance plan: (R1) suitability of the condition-based maintenance strategy, (R2) availability of relevant data, (R3) proper problem formulation, and (R4) accurate evaluation of data mining methods. In response to the absence of a unified framework and standardized guidelines, this survey delves into the realm of time series sensor data and wheel-track interface components for railway structural health monitoring. This survey aims to bridge this gap by offering an extensive categorization, pinpointing existing challenges, and outlining potential directions for future research. Through these efforts, this survey provides a more thorough and targeted exploration of the subject matter, contributing to the advancement of this field.

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Wheel Out-of-Roundness Detection Using an Envelope Spectrum Analysis

Cited by 5 publications

References 53 publications

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Condition Monitoring of Rolling Stock Supported by Artificial Intelligence Technique

Time series data mining for railway wheel and track monitoring: a survey

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