Adaptive sparse representation based on circular-structure dictionary learning and its application in wheelset-bearing fault detection

Ding, Jianming; Zhao, Wentao; Miao, Bingrong; Lin, Jianhui

doi:10.1016/j.ymssp.2018.04.012

Cited by 45 publications

(17 citation statements)

References 33 publications

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“…When all the fault impulse signals are obtained, the fault information that is applied to monitor the running condition of a wheelset bearing could be achieved by using the Hilbert envelope spectrum of these impulse signals [2].…”

Section: Basic Theory Of Si-k-svdmentioning

confidence: 99%

“…A wheelset bearing is a key part of a wheelset, which maintains the energy-stable transmission between the driven system and the wheelset. e rapid and long-term alternating transmission may lead to the accelerated wear and failure of the train wheelset bearings and ultimately endanger the safety of the railway service [2]. erefore, it is very important to monitor the running condition of the train wheelset bearings.…”

Section: Introductionmentioning

confidence: 99%

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Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift‐Invariant Dictionary Learning

Zhang

Ding

Lin

2019

Shock and Vibration

Self Cite

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A wheelset bearing is a crucial energy transmission element in high-speed trains. Any parts of the wheelset bearing that have faults may endanger the safety of the railway service. Therefore, it is important to monitor the running condition of a wheelset bearing. The multifault on a wheelset bearing is very common, and these impulsive components generated by different types of faults may interact with each other, which increases the difficulty of entirely identifying those faults. To solve the multifault problem, this paper proposed a hierarchical shift-invariant K-means singular value decomposition (H-SI-K-SVD) to hierarchically separate those multifault impulsive components based on their fault power levels. Each of the separated impulse signals contains only one fault impulse, and the fault information could be highlighted both in time domain and frequency domain. In addition, the sparsity of envelope spectrum (SES) is introduced as an indicator to adaptively tune a key parameter in this method. The effectiveness of the proposed method is verified by both simulation and experimental signals. Compared with ensemble empirical model decomposition (EEMD), the proposed method exhibits better performance in separating the multifault impulsive components and detecting the faults of a wheelset bearing.

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Section: Basic Theory Of Si-k-svdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift‐Invariant Dictionary Learning

Zhang

Ding

Lin

2019

Shock and Vibration

Self Cite

View full text Add to dashboard Cite

show abstract

“…It shows better noise robustness, and can effectively separate two pure harmonic signals with similar frequencies. However, when the VMD method decomposes the signal, the decomposition effect is seriously affected by the number of decomposition components [56][57][58][59][60]. Some other methods have been proposed to realize signal analysis and fault diagnosis in recent years [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Liu

Yuan

et al. 2019

Applied Sciences

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The variational mode decomposition (VMD) method for signal decomposition is severely affected by the number of components of the VMD method. In order to determine the decomposition modal number, K, in the VMD method, a new center frequency method of the multi-threshold is proposed in this paper. Then, an improved VMD (MTCFVMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs). The Hilbert transformation is used to calculate the envelope signal of each IMF component, and the maximum frequency value of the power spectral density is obtained in order to effectively and accurately extract the fault characteristic frequency and realize the fault diagnosis. The rolling element vibration data of the motor bearing is used to test the effectiveness of proposed methods. The experiment results show that the center frequency method of the multi-threshold can effectively determine the number, K, of decomposed modes. The proposed fault diagnosis method based on MTCFVMD and Hilbert transformation can effectively and accurately extract the fault characteristic frequency, rotation frequency, and frequency doubling, and can obtain higher diagnostic accuracy.

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“…erefore, highspeed train monitoring signal analysis not only needs to deal with high dimensions and high complexity but also many uncertain factors. Some efforts have been performed for fault diagnosis of high-speed train, including suspension system [10][11][12][13], traction system [14,15], wheelset bearing system [16,17], and on-board equipment [18,19]. Gasparetto et al [20] proposed a data-driven approach for detecting the incipient instability of the bogie, which is based on random decrement technique and Prony method.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of High‐Speed Train Bogie Based on Synchrony Group Convolutions

Jin

Ren

et al. 2019

Shock and Vibration

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Health monitoring and fault diagnosis of a high-speed train is an important research area in guaranteeing the safe and long-term operation of the high-speed railway. For a multichannel health monitoring system, a major technical challenge is to extract information from different channels with divergence patterns as a result of distinct types and layout of sensors. To this end, this paper proposes a novel group convolutional network based on synchrony information. The proposed method is able to gather signals with similar patterns and process these channels with specific groups of neurons while simultaneously assigning signals with significant difference to different groups. In this approach, the feature can be extracted more effectively and the performance can be improved, owing to the sharing of filters for similar patterns. The effectiveness of the method is validated on high-speed train fault dataset. Experiments show that the proposed model performs better than normal convolutions and normal group convolutions on this task, which achieves an accuracy of 98.27% (σ = 1.73) with good computational efficiency.

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Adaptive sparse representation based on circular-structure dictionary learning and its application in wheelset-bearing fault detection

Cited by 45 publications

References 33 publications

Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift‐Invariant Dictionary Learning

Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift‐Invariant Dictionary Learning

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Fault Diagnosis of High‐Speed Train Bogie Based on Synchrony Group Convolutions

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