High-speed train wheel set bearing fault diagnosis and prognostics: Research on acoustic emission detection mechanism

Hou, Dongming; Qi, Hongyuan; Li, Defa; Wang, Cuiping; Han, Defu; Luo, Honglin; Peng, Chenglin

doi:10.1016/j.ymssp.2022.109325

Cited by 12 publications

(7 citation statements)

References 24 publications

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“…Therefore, to improve the sensitivity of the sensor and reduce the interference of low-frequency environmental noise, the resonance frequency of the piezoelectric ceramic is set at 150 kHz, and the bandwidth of the damage AE signal is 20-400 kHz. 13,16 Among AE waves, Rayleigh waves account for the largest proportion of energy and propagate along the surface of the tested structure, and their amplitudes attenuate slower than those of P and S waves; so that Rayleigh waves can propagate farther. 33,34 Therefore, the AE waves along the surface of the tested structure are mainly Rayleigh waves, which induce phase differences in the excitation on the piezoelectric ceramic surface.…”

Section: Dimensional Parameter Model Of Piezoelectric Ceramicsmentioning

confidence: 99%

Section: Structural Analysis and Mathematical Model Studymentioning

confidence: 99%

“…Furthermore, our team has conducted a lot of research and exploration in the field of high-speed train bearing fault diagnosis and prognosis. [13][14][15][16] The fault diagnosis technique based on AE has been proved to be a highly effective technique for monitoring the conditions of bearings in high-speed trains. A number of studies have reported on AE signal perception.…”

Section: Introductionmentioning

confidence: 99%

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A novel acoustic emission sensor design and modeling method for monitoring the status of high-speed train bearings

Han

Hou

et al. 2023

Structural Health Monitoring

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Acoustic emission (AE) technology is suitable for monitoring the status of high-speed train bearings owing to its high sensitivity and real-time dynamic monitoring capabilities. However, a complete theoretical model of the AE sensor, which is the core component of AE signal sensing equipment, has not yet been reported. The existing matching layer models do not account for wave attenuation in the matching layer. To address these shortcomings, we established a novel piezoelectric AE sensor design and modeling method. First, a rough contact model is established for piezoelectric ceramics, and the influence of roughness on size selection of piezoelectric ceramics is analyzed. Second, the sound intensity transmission coefficient (SITC) model of the matching layer is established considering the attenuation of AE waves, and the corresponding relationship between the attenuation coefficient and the optimum thickness of the matching layer is derived. Then a complete finite element (FE) model of an AE sensor is established, and the electroacoustic properties of the AE sensor are numerically simulated based on acoustic and piezoelectric coupling. Furthermore, AE sensors with different thicknesses of matching layers are constructed, and the validity of the mathematical model of the matching layer is verified through a lead-breaking experiment. Thereafter, a novel comprehensive performance evaluation index (CPEI) is designed through principal component analysis (PCA) based on hit parameters of AE sensors. Finally, the effectiveness and environmental adaptability of the AE sensor is verified by performance testing under complex conditions near an actual high-speed train line. The proposed method can provide a valuable theoretical framework for AE sensor design and status monitoring of high-speed train bearings.

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Section: Dimensional Parameter Model Of Piezoelectric Ceramicsmentioning

confidence: 99%

Section: Structural Analysis and Mathematical Model Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel acoustic emission sensor design and modeling method for monitoring the status of high-speed train bearings

Han

Hou

et al. 2023

Structural Health Monitoring

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“…34 The correlation between AE energy and damage evolution and severity is further explored. 35,36 Hidle et al 37 propose a detector for subsurface cracks based on the pulse integration method. And in comparative studies also involving vibration monitoring, AE demonstrates earlier damage detection 36,38 – particularly when combined with spectral kurtosis 33 – and fusion of vibration and AE data has shown to increase the reliability with respect to either method separately.…”

Section: Introductionmentioning

confidence: 99%

“…35,36 Hidle et al 37 propose a detector for subsurface cracks based on the pulse integration method. And in comparative studies also involving vibration monitoring, AE demonstrates earlier damage detection 36,38 – particularly when combined with spectral kurtosis 33 – and fusion of vibration and AE data has shown to increase the reliability with respect to either method separately. 39 Besides these, k-means clustering of AE waveform features has been applied to identify crack initiation and propagation.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings

Scheeren

Kaminski

Pahlavan

2023

Structural Health Monitoring

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This article presents an approach to identify naturally developed damage in low-speed bearings using waveform-similarity-based clustering of acoustic emissions (AEs) under fatigue loading. The approach is motivated by the notation that each recorded AE signal from a particular damage is defined by the convolution of the source signal, transfer function of the propagation path and transfer function of the utilised sensor, and may thusly be used to identify consistent AE sources, for example due to crack growth. A sequential clustering procedure is proposed, that is based on waveform cross-correlation. The supporting theoretical background of waveform similarity, rooted in an analytical formulation of waveform propagation and transmission in complex structures, is discussed. The presented methodology is evaluated through application to AE data obtained in a low-speed run-to-failure experiment utilising a densely instrumented purpose-built linear bearing segment. The implemented sensor system comprises arrays of three types of AE transducers, that is relatively low - (40–100 kHz), mid - (95–180 kHz) and high-frequency (180–580 kHz), that are situated on both the raceways and supporting substructures of either side of the bearing. Over the course of 225,000 cycles of extension and retraction, wear has been developed. A total of about ∼2,300,000 AE signals have been recorded. Analysis of the recorded data suggests the rate of degradation increases from around 70,000 cycles onwards. Highly consistent structures of clusters indicative of a localised defect in the raceway have been identified from around 170,000 cycles onwards. These clusters are characterised by hit-rates in the range of 1–2 hits per cycle and an average similarity of 93%, they comprise about half the AE activity for the periods they have been identified for. These results highlight that the proposed cross-correlation-based clustering of AE waveforms and identification of multi-channel formations in said clusters compose a suitable methodology for assessment of damage in low-speed roller bearings.

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Quantitative Relationship Between Sliding-Generated Acoustic Emission and Friction Conditions at Bolted Joint Interfaces

Sun,

Li,

Yang

et al. 2024

Tribol Lett

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High-speed train wheel set bearing fault diagnosis and prognostics: Research on acoustic emission detection mechanism

Cited by 12 publications

References 24 publications

A novel acoustic emission sensor design and modeling method for monitoring the status of high-speed train bearings

A novel acoustic emission sensor design and modeling method for monitoring the status of high-speed train bearings

Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings

Quantitative Relationship Between Sliding-Generated Acoustic Emission and Friction Conditions at Bolted Joint Interfaces

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