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

Abstract

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.

Dynamics and Fault Diagnosis of Railway Vehicle Gearboxes: A Review

“…In fact, this technology has found extensive application in the critical systems of railway vehicles, including the bogie system, traction system, brake system, train electrical system, and information control system [7]. Existing review articles primarily focus on bearings and wheels [2], [8], [9], [10], [11]. While some review articles have covered railway vehicle gearboxes, they are relatively superficial [5], [7].…”

RSDNet: A New Multiscale Rail Surface Defect Detection Model

“…This method uses sensors to capture vibration and acceleration signals during operation and analyzes these signals to identify abnormalities in the wheel-rail system and determine faults [ 8 ]. Fu et al [ 9 ] simulate flatness anomalies using the multi-body dynamics software SIMPACK and generate spectral images for anomaly detection by analyzing acceleration signals. Xie et al [ 10 ] developed a vehicle-track coupled dynamics model to simulate the dynamic response of the axle box under different speeds and track wear excitations.…”

Wayside Condition Monitoring of Metro Wheelsets Using Vibration and Acoustic Sensors

“…Various methods have been proposed, including time-domain statistical features like kurtosis, crest factor [39], variance, skewness, kurtosis, higher-order moments [40], impulse and clearance factors [41], DWT-Discrete Wavelet Transform for denoising, TSA-Time-synchronous Averaging, [42], Kurtograms and wavelets [43,44], MCKD-Maximum Correlated Kurtosis Deconvolution [45], Gabor wavelets and, wavelet transform [46], HHT-Hilbert Huang Transform and SVMs-Support Vector Machines [47], time domain analysis combined with fuzzy C-means [48], envelope analysis from the Kurtogram [49] as well as various statistical features [50], have been proposed in the literature for detecting rotating component faults. Moreover, recent advancements in deep learning techniques such as LSTM (Long Short Time Memory), RNN (Recurrent Neural Networks), DBN (Deep Belief Network) [51], Multi-Layer Perceptron (MLP) [52], and CNN-Convolutional Neural Networks [53] are also examined in vibration based condition monitoring.…”