An adaptive boundary determination method for empirical wavelet transform and its application in wheelset-bearing fault detection in high-speed trains

Zhang, Qingsong; Ding, Jianming; Zhao, Wentao

doi:10.1016/j.measurement.2020.108746

Cited by 31 publications

(18 citation statements)

References 42 publications

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“…Since the coordinates of projectile explosion obtained by each unit basic array are different, it is generally considered that the errors introduced are not correlated with each other. So, the test data of projectile explosion can be approximated by formula (14). Formula (12) shows that each unit basic array has the basis for establishing the conditional probability density distribution of test data of projectile explosion.…”

Section: A Representation Methods Of Explosion Position Test Data At Unit Basic Arraymentioning

confidence: 99%

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Projectile Explosion Position Parameters Data Fusion Calculation and Measurement Method Based on Distributed Multi-Acoustic Sensor Arrays

Zhang

2022

IEEE Access

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In conventional weapon test field, the parameter of projectile explosion position is an important index to measure the control ability and damage efficiency of fuze. Due to the random deviation of gun firing and the difference of detection performance of fuze itself, the projectile explosion position is uncertain and relatively scattered in terminal ballistic, which is easy to miss the projectile explosion information. In order to obtain the parameters of projectile explosion position in large area of in terminal ballistic, this paper constructs a projectile explosion position testing system with distributed multiple acoustic sensor basic arrays, establishes the calculation model of projectile explosion position by a basic array, researches a multi-source data fusion algorithm based on the measurement data of multiple array acoustic basic arrays and gets real projectile explosion coordinates. To improve the measurement accuracy, we use the wavelet transform filtering method to filter the output signal of the acoustic sensors, and propose the time extraction algorithm of the projectile explosion acoustic signal with the wavelet modulus maximum. Through experiment and analysis, we collect the sound signal of the actual projectile explosion and verify the rationality and correctness of the proposed calculation model and algorithm in this paper.

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Section: A Representation Methods Of Explosion Position Test Data At Unit Basic Arraymentioning

confidence: 99%

“…Formula (12) shows that each unit basic array has the basis for establishing the conditional probability density distribution of test data of projectile explosion. Therefore, this also makes it possible to solve formula (14) in specific applications.…”

Section: A Representation Methods Of Explosion Position Test Data At Unit Basic Arraymentioning

confidence: 99%

Projectile Explosion Position Parameters Data Fusion Calculation and Measurement Method Based on Distributed Multi-Acoustic Sensor Arrays

Zhang

2022

IEEE Access

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“…However, extracting feature information with high valence density and designing a classification space that is suitable for strong nonlinear and non-stationary information are urgent problems to be solved in the field of fault diagnosis, and even in the field of machine learning. Reference [ 7 ] proposed an adaptive boundary determination method based on empirical wavelet transform and applied it to fault detection of high-speed train wheelset bearings. Park et al [ 8 ] proposed a minimum variance cepstrum based on cepstral analysis, which avoided the influence of the system frequency and the selection of the resonance band, and realized the detection of early faults of the rotating parts.…”

Section: Introductionmentioning

confidence: 99%

The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy

Zhuang

Liu

Zheng

et al. 2023

Sensors

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Rolling bearings are important supporting components of large-scale electromechanical equipment. Once a fault occurs, it will cause economic losses, and serious accidents will affect personal safety. Therefore, research on rolling bearing fault diagnosis technology has important engineering practical significance. Feature extraction with high price density and fault identification are two keys to overcome in the field of fault diagnosis of rolling bearings. This study proposes a feature extraction method based on variational modal decomposition (VMD) and sample entropy and also designs an improved sequence minimization algorithm with optimal parameters to identify the fault. Firstly, a variational modal decomposition system based on vibration signals is designed, and the sample entropy of the components is extracted as the eigenvalue of the signal. Secondly, in order to improve the accuracy of fault diagnosis, the sequence minimum optimization algorithm optimized by the bat algorithm is used as the classifier. Certainly, the traditional bat algorithm (BA) and the sequence minimum optimization algorithm (SMO) are improved, respectively. Therefore, a fault diagnosis algorithm based on IBA-ISMO is obtained. Finally, the experimental verification is designed to prove that the algorithm model has a good state recognition rate for bearings.

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“…As the core component of rotating machinery, rolling bearing has been widely used in high-speed train [1], [2], wind turbine [3], [4], aeroengine [5]. Because rolling bearing often operate under high speed, high load and other complex conditions, many localized faults will occur (e.g., cracks, pitting corrosion), which will lead to a series of transient impacts [6].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Double Difference Sparse Regularization and Convex Optimization for Bearing Fault Detection

Liu

Huang

et al. 2021

IEEE Access

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The localized faults of bearings often produce a series of periodic impacts. However, how to extract these repetitive transients from the signals with strong noise interference is still a challenging problem. In this paper, a new method called collaborative double difference sparse regularization (CDDSR) is proposed for bearing fault detection. To be specific, the first and second-order difference matrices are integrated into sparse regularization term, and the differential sparsity and denoising effect of fault signal are enhanced by collaboration. According to the time domain impulse characteristics of fault signal, the sparsity of signal itself will also be considered. Based on the majorization-minimization (MM) algorithm, the objective optimization model can be solved quickly. Furthermore, the selection of regularization parameters is deeply studied, and an adaptive parameter selection strategy is given. The performance of CDDSR is verified through simulation analysis and two experimental cases, and the ability of CDDSR to extract fault features is further evaluated by quantitative index. Results demonstrate its superiority in eliminating noise interference and extracting periodic impulses in comparison to other state-of-the-art methods.

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An adaptive boundary determination method for empirical wavelet transform and its application in wheelset-bearing fault detection in high-speed trains

Cited by 31 publications

References 42 publications

Projectile Explosion Position Parameters Data Fusion Calculation and Measurement Method Based on Distributed Multi-Acoustic Sensor Arrays

Projectile Explosion Position Parameters Data Fusion Calculation and Measurement Method Based on Distributed Multi-Acoustic Sensor Arrays

The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy

Collaborative Double Difference Sparse Regularization and Convex Optimization for Bearing Fault Detection

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