Fault Diagnosis of Wheel Flat Using Empirical Mode Decomposition-Hilbert Envelope Spectrum

Jiang, Hua; Lin, Jianhui

doi:10.1155/2018/8909031

Cited by 6 publications

(3 citation statements)

References 35 publications

(39 reference statements)

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“…Nowakowski et al 23 proposed Hilbert transform-based diagnosis algorithm for wheel flats using vibration signals from wayside sensors. Jiang and Lin 27 studied the diagnosis of wheel flats using empirical mode decomposition-Hilbert envelope spectrum. Li et al 8 found significant differences in the Hilbert spectra between normal and faulty wheels and proposed a method of diagnosing two wheel out-of-round phenomena: wheel tread scuffing and wheel polygonalization.…”

Section: Related Workmentioning

confidence: 99%

“…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%

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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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Section: Related Workmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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“…Previous research has been focused on advanced signal processing methods to eliminate signal interference and spotlight the faulty signal patterns of wheel flats. Jiang et al [ 15 ] used the empirical mode decomposition (EMD) method to divide the signal into several intrinsic mode functions (IMF) which separates the faulty signal mode from interferences. Amini et al [ 13 ] proposed a method based on time–spectral kurtosis (TSK) to reduce the effect of noise and highlight the faulty signal patterns of wheel flats.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Learning Approach for Wayside Train Wheel Flat Detection

Mohammadi

Mosleh

Vale

et al. 2023

Sensors

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One of the most common types of wheel damage is flats which can cause high maintenance costs and enhance the probability of failure and damage to the track components. This study aims to compare the performance of four feature extraction methods, namely, auto-regressive (AR), auto-regressive exogenous (ARX), principal component analysis (PCA), and continuous wavelet transform (CWT) capable of automatically distinguishing a defective wheel from a healthy one. The rail acceleration for the passage of freight vehicles is used as a reference measurement to perform this study which comprises four steps: (i) feature extraction from acquired responses using the specific feature extraction methods; (ii) feature normalization based on a latent variable method; (iii) data fusion to enhance the sensitivity to recognize defective wheels; and (iv) damage detection by performing an outlier analysis. The results of this research show that AR and ARX extraction methods are more efficient techniques than CWT and PCA for wheel flat damage detection. Furthermore, in almost every feature, a single sensor on the rail is sufficient to identify a defective wheel. Additionally, AR and ARX methods demonstrated the potential to distinguish a defective wheel on the left and right sides. Lastly, the ARX method demonstrated robustness to detect the wheel flat with accelerometers placed only in the sleepers.

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