Assessment of local faults in helical geared system using vibro-acoustic signals based on higher order spectrum analysis

Gunasegaran, V.; Amarnath, M.; Chelladurai, H.; Krishna, I. R. Praveen

doi:10.1016/j.apacoust.2023.109237

Cited by 5 publications

(4 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Han et al developed a dynamic model of a gear rotor bearing system to determine the effect of meshing excitation on the gearbox vibration and noise, and an acoustic boundary element method was used to predict the gearbox noise distribution [15]. Gunasegaran used a higher-order spectral analysis technique to obtain acoustic data from vibration and airborne sound signals from a gearbox using a bispectral analysis [16]. Recently, experts have also utilized digital twin technology to achieve good results in emerging areas such as dynamics in various industrial contexts.…”

Section: Introductionmentioning

confidence: 99%

Noise Prediction Study of Traction Arc Tooth Cylindrical Gears for New Generation High-Speed Electric Multiple Units

Tang,

Chen,

Sun

et al. 2023

Lubricants

View full text Add to dashboard Cite

As the speed of the new generation of high-speed electric multiple units (EMU) increases, the requirements for vibration and noise reduction in traction gear trains are becoming higher and higher. Although most researchers have focused on the vibration mechanics analysis of gears, the actual noise has the most direct impact on passenger experience and safety. To address this problem, a new type of curved cylindrical gear is proposed to analyze the dynamic characteristics of the gear pair and predict its radiated noise based on the acoustic-vibration coupling theory using the finite element-boundary element method. Parametric modeling of the gear pair using CREO and assembly motion analysis were performed. ANSYS was used to analyze the stress distribution, inherent frequency, and inherent vibration pattern of the gear pair, and harmonic response analysis was performed using the modal superposition method to solve the displacement frequency response curve and vibration characteristics. ACTRAN was used to construct the free-field model, create acoustic excitation based on the acoustic-vibration coupling equation, set the field points, and predict radiated noise. The research results show that the noise is mainly concentrated in the tooth meshing area, and the root mean square RMS range of its sound pressure level value is 91–100 dB. Its dynamic characteristics and noise values are in line with the traction requirements of high-speed EMU, providing a new idea for improving the noise prediction of traction gears for new generation high-speed EMU, which in turn strongly support the noise control of high-speed EMU stock and thus improve the passenger experience and driving environment.

show abstract

Section: Introductionmentioning

confidence: 99%

Noise Prediction Study of Traction Arc Tooth Cylindrical Gears for New Generation High-Speed Electric Multiple Units

Tang,

Chen,

Sun

et al. 2023

Lubricants

View full text Add to dashboard Cite

show abstract

“…The automotive, diesel, turbine, and electric machine building industries have all adopted this approach. 33,34 The second stage of controlling and normalizing vibration parameters is the selection of the optimal frequency analysis bandwidth.…”

Section: Regulation Stages For Machine Vibrationmentioning

confidence: 99%

“…The automotive, diesel, turbine, and electric machine building industries have all adopted this approach. 33,34…”

Section: Regulation Stages For Machine Vibrationmentioning

confidence: 99%

Elevating electric motor performance through rigorous vibration control and standardization

Arhun,

Hnatov,

Mygal

et al. 2024

Advances in Mechanical Engineering

View full text Add to dashboard Cite

This study addresses the critical need for enhanced vibration control and standardization in electric motors, pivotal to the operational efficiency of electric vehicles and thus, environmental sustainability. Recognizing the gap in unified standards for managing machine vibration, we propose a novel, integrated framework combining advanced vibrodiagnostic techniques and rigorous standardization processes. Our methodology involves a detailed analysis of existing vibration control practices, identifying deficiencies in current standards, and presenting an improved, comprehensive guideline tailored to electric motors’ unique requirements in transportation applications. The innovation of our approach lies in its dual focus on technical precision and environmental sustainability, offering a significant leap forward in vibrodiagnostics. We demonstrate the framework’s practical value through its potential to revolutionize electric motor maintenance strategies, enhance machine longevity, and reduce environmental impact. This research contributes to the broader goals of reducing greenhouse gas emissions and advancing sustainable transportation, emphasizing the importance of vibration control standards in achieving energy efficiency and reliability in electric transport systems.

show abstract

“…Shuen De Wu [2] Vibration Signal Dongfang Zhao [3] Vibration Signal Gunasegaran V [4] Vibration Signal ZamudioRamirez Israe [6] Stray flux signals Israel Zamudio-Ramirez [7] Stray flux signals noncontact inspection…”

Section: Contact Inspectionmentioning

confidence: 99%

Wind Turbine Gearbox Gear Surface Defect Detection Based on Multiscale Feature Reconstruction

Gao

Cao

et al. 2023

Electronics

View full text Add to dashboard Cite

The fast and accurate detection of wind turbine gearbox surface defects is crucial for wind turbine maintenance and power security. However, owing to the uneven distribution of gear surface defects and the interference of complex backgrounds, there are limitations to gear-surface defect detection; therefore, this paper proposes a multiscale feature reconstruction-based detection method for wind turbine gearbox surface defects. First, the Swin Transformer was used as a backbone network based on the PSPNet network to obtain global and local features through multiscale feature reconstruction. Second, a Feature Similarity Module was used to filter important feature sub-blocks, which increased the inter-class differences and reduced the intra-class differences to enhance the discriminative ability of the model for similar features. Finally, the fusion of contextual information using the pyramid pooling module enhanced the extraction of gear surface defect features at different scales. The experimental results indicated that the improved algorithm outperformed the original PSPNet algorithm by 1.21% and 3.88% for the mean intersection over union and mean pixel accuracy, respectively, and significantly outperformed semantic segmentation networks such as U-Net and DeepLabv3+.

show abstract

Assessment of local faults in helical geared system using vibro-acoustic signals based on higher order spectrum analysis

Cited by 5 publications

References 53 publications

Noise Prediction Study of Traction Arc Tooth Cylindrical Gears for New Generation High-Speed Electric Multiple Units

Noise Prediction Study of Traction Arc Tooth Cylindrical Gears for New Generation High-Speed Electric Multiple Units

Elevating electric motor performance through rigorous vibration control and standardization

Wind Turbine Gearbox Gear Surface Defect Detection Based on Multiscale Feature Reconstruction

Contact Info

Product

Resources

About