Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations

Baasch, Benjamin; Heusel, Judith; Roth, Michael; Neumann, Thorsten

doi:10.3390/app11041432

Cited by 15 publications

(13 citation statements)

References 25 publications

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“…Also for railway wheels, different approaches have been proposed to monitor their condition [15]. Several studies have focused on using on-board health monitoring systems, which enable continuous monitoring [16][17][18][19]. However, with the ongoing increase in rolling stock in European rail networks [20], on-board systems do not scale well due to cost-and time-intensive installations and maintenance.…”

Section: Related Workmentioning

confidence: 99%

Contrastive Feature Learning for Fault Detection and Diagnostics in Railway Applications

Rombach¹,

Michau²,

Kajan³

et al. 2022

SSRN Journal

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Section: Related Workmentioning

confidence: 99%

Contrastive Feature Learning for Fault Detection and Diagnostics in Railway Applications

Rombach¹,

Michau²,

Kajan³

et al. 2022

SSRN Journal

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“…Ma et al 30 proposed a data-driven fault diagnosis method based on timefrequency analysis and a deep residual network method. However, the feature-learning capability of neural networks tends to decrease when dealing with noisy Time synchronous average (TSA) 18 Autoregressive moving average (ARMA) 19 Principal component analysis (PCA) 20 Correlation-based analysis 21 Frequency-domain analysis Fast Fourier transform (spectrum analysis) 22 Hilbert transform (envelope analysis) 8,23 Inverse Fourier Transform of logarithmic power spectrum (cepstrum analysis) 24 Time-Frequency analysis Short-time Fourier transform (STFT) 25 Wigner-Ville transform (WVT) 25 Wavelet transform (WT) 25,26 Hilbert-Huang transform (HHT) 27…”

Section: Related Workmentioning

confidence: 99%

Wheel fault diagnosis model based on multichannel attention and supervised contrastive learning

Chen

Zhao

Kim³

et al. 2021

Advances in Mechanical Engineering

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As wheels are important components of train operation, diagnosing and predicting wheel faults are essential to ensure the reliability of rail transit. Currently, the existing studies always separately deal with two main types of wheel faults, namely wheel radius difference and wheel flat, even though they are both reflected by wheel radius changes. Moreover, traditional diagnostic methods, such as mechanical methods or a combination of data analysis methods, have limited abilities to efficiently extract data features. Deep learning models have become useful tools to automatically learn features from raw vibration signals. However, research on improving the feature-learning capabilities of models under noise interference to yield higher wheel diagnostic accuracies has not yet been conducted. In this paper, a unified training framework with the same model architecture and loss function is established for two homologous wheel faults. After selecting deep residual networks (ResNets) as the backbone network to build the model, we add the squeeze and excitation (SE) module based on a multichannel attention mechanism to the backbone network to learn the global relationships among feature channels. Then the influence of noise interference features is reduced while the extraction of useful information features is enhanced, leading to the improved feature-learning ability of ResNet. To further obtain effective feature representation using the model, we introduce supervised contrastive loss (SCL) on the basis of ResNet + SE to enlarge the feature distances of different fault classes through a comparison between positive and negative examples under label supervision to obtain a better class differentiation and higher diagnostic accuracy. We also complete a regression task to predict the fault degrees of wheel radius difference and wheel flat without changing the network architecture. The extensive experimental results show that the proposed model has a high accuracy in diagnosing and predicting two types of wheel faults.

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“…4 Such defects may lead to excessive vibrations which, in turn, may cause other detrimental consequences. 4 Therefore, the potential wheel defects should be carefully monitored. Wheel monitoring methods can be categorized into onboard and wayside methods.…”

Section: Introductionmentioning

confidence: 99%

Experimental detection of train wheel defects using wayside vibration signal processing

Salehi

Bagherzadeh

Fakhari

2023

Structural Health Monitoring

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While many studies have been used onboard monitoring methods to detect train wheel defects, this paper aims to extract damage features from vibrational signals obtained by the wayside monitoring system. To provide an appropriate tool for the decomposition of experimental nonstationary vibration signals containing impacts, an analytical amplitude-based signal decomposition method is provided. A two-axle motor car with a flat wheel defect is studied at different operating conditions. Vibrational signals arising from wheel-rail interactions are gathered by the Wayside Data Collection Setup (WDCS) with five piezoelectric accelerometers. The tests are performed at the constant speeds of 10, 20, 30, and 40 km/h. The acquired signals are decomposed by the Multi-channel Singular Spectrum Analysis (MSSA) into the contributing components. Based on the results, increases in the amplitudes of the second reconstructed components (RCs) of the vibrational signals sensed by all the accelerometers can be observed at a specific time instance as a repeating pattern. Similar patterns are obtained at different motor car positions relative to the measurement area, as well as different motor car speeds. To verify the consistency of the second RCs, the crest factors are obtained for all the angular positions. The results show sudden increases in the crest factor at a fixed angular position that indicate the existence of a defect at that specific angular position of the wheel.

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Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations

Cited by 15 publications

References 25 publications

Contrastive Feature Learning for Fault Detection and Diagnostics in Railway Applications

Contrastive Feature Learning for Fault Detection and Diagnostics in Railway Applications

Wheel fault diagnosis model based on multichannel attention and supervised contrastive learning

Experimental detection of train wheel defects using wayside vibration signal processing

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