Detection of Wheel Polygonization Based on Wayside Monitoring and Artificial Intelligence

Guedes, A.; Silva, Rúben; Ribeiro, Diogo; Vale, Catarina; Mosleh, Araliya; Montenegro, Pedro Aires; Meixedo, Andreia

doi:10.3390/s23042188

Cited by 12 publications

(6 citation statements)

References 54 publications

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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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“…A conceptual wayside monitoring system is defined to assess rail accelerations resulting from the passage of a train. In previous work [27], it was concluded that with a minimum number of two sensors (one on each rail), it was possible to detect out-of-roundness (OOR) defects in wheels. Therefore, four accelerometers were initially considered (two on each rail) for developing the polygonal wheel classification methodology in the present work.…”

Section: Wayside System Layoutmentioning

confidence: 99%

“…Recently, machine learning (ML) algorithms were implemented based on dynamic responses, such as artificial neural networks (ANN) [22], deep neural networks (DNN) [23][24][25][26], principal component analysis (PCA) [27], wavelet continuous transform (CWT) [28], and autoregressive (AR) models [29]. Among them, artificial neural networks and deep neural network algorithms have been applied in diverse areas through the years.…”

Section: Introductionmentioning

confidence: 99%

“…Skarlatos et al [40] proposed a method based on the statistical processing of vibration signals to characterize the state of the wheels according to established vibration limits using data acquired by accelerometers installed on the rail. More recently, automatic methodologies based on machine learning have been implemented, with very good results in detecting wheel flat and polygonal wheels [17,27,29]. Lourenço et al [42] established a reliable damage index for diagnosing wheel defects in real-time based on Hidden Markov Model (HMM) and processing the signal using a Short-time Fourier Transform (STFT).…”

Section: Introductionmentioning

confidence: 99%

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Clustering-Based Classification of Polygonal Wheels in a Railway Freight Vehicle Using a Wayside System

Guedes,

Silva,

Ribeiro

et al. 2024

Applied Sciences

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Polygonal wheels are one of the most common defects in train wheels, causing a reduction in comfort levels for passengers and a higher degradation of vehicle and track components. With the aim of contributing to the safety and reliability of railway transport, this paper presents the development of an innovative methodology for classifying polygonal wheels based on a wayside system. To achieve that, a numerical train-track interaction model was adopted to simulate the passage of a freight train over a virtual wayside monitoring system composed of a set of accelerometers installed on the rails. Then, the acquired acceleration time series was transformed to a frequency domain using a Fast Fourier transform (FFT), and on this data, damage-sensitive features were extracted. The features based on Principal Component Analysis (PCA) showed great sensitivity to the harmonic order, while the ones based on Continuous Wavelet Transform (CWT) model showed great sensitivity to the defect amplitude. One step further, all features are merged using the Mahalanobis distance in order to obtain a damage index strongly correlated with the polygonal defect. Finally, a cluster analysis allowed the automatic classification of polygonal wheels, according to the harmonic order (harmonic-based) and defect amplitude (amplitude-based). The proposed methodology demonstrated high efficiency in identifying different types of polygonal wheels using a minimum layout of two sensors.

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“…Track-mounted sensors can provide detailed information regarding track conditions; however, due to the lengthy extension of railway lines, it is usually only feasible to instrument small track sections, limiting this application only to very critical parts of the track. On the other hand, this approach is particularly interesting when it comes to vehicle monitoring since a small instrumented track section may be used to monitor all the passing vehicles [2,5].…”

Section: Introductionmentioning

confidence: 99%

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

et al. 2023

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Railways are one of the most important means of transportation, allowing people and goods to move quickly, environmentally beneficially, and efficiently over long distances. To ensure safe and reliable operations, regular condition-based assessments of trains and track are of paramount importance. Drive-by methodologies, which utilize data collected by onboard monitoring systems as the vehicle travels over the track, have gained popularity as an economically viable strategy for monitoring extensive track networks as well as vehicles traveling on them. This paper presents a critical review of these methodologies applied to railway tracks and vehicles. It assesses research on track irregularities, rail conditions, and the condition of rail supporting elements, highlighting important early developments and recent papers that provide insights into future practical applications. Additionally, the paper explores works related to global vehicle condition evaluation, focusing on the identification of suspension and wheelset element damage and also discussing challenges towards commercial application. The findings suggest that drive-by methodologies have several promising future applications. These include track maintenance optimization, proactive fault detection, predictive maintenance, track performance evaluation, vehicle health monitoring, and data-driven decision-making. By leveraging drive-by assessments, railway operators can optimize maintenance efforts, detect faults early, predict remaining component life, evaluate track performance, monitor vehicle health, and make informed decisions based on data analysis. Finally, a comprehensive conclusion summarizes the achievements thus far and provides perspectives for forthcoming developments. The future practical applications of drive-by methodologies discussed in this review have the potential to revolutionize railway track and vehicle assessments, leading to safer and more efficient railway operations in the days to come.

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Detection of Wheel Polygonization Based on Wayside Monitoring and Artificial Intelligence

Cited by 12 publications

References 54 publications

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

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

Clustering-Based Classification of Polygonal Wheels in a Railway Freight Vehicle Using a Wayside System

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

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