数据集偏差情况下滚动轴承的故障诊断

Hou, Liangsheng; Zhang, Jundong

doi:10.1007/s12204-021-2320-6

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Mg F F D W T  1

Analysis Of the Causes Of Doppler Aberrations In Rolling Bea...1

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2023

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Cited by 2 publications

(2 citation statements)

References 33 publications

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“…Through a large number of data measurement of competing cars, it is found that there are great differences in  for different models of SUVs and cars. In general, when the  of SUVs is 0.662 and the  of cars is 0.5, it is more in line with the actual situation and the error of calculation results is the smallest 11 . Equivalent volume formula:…”

Section: Mg F F D W T  mentioning

confidence: 54%

Construction of performance evaluation model of production vehicle based on big data

Liu,

Shi

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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Section: Mg F F D W T  mentioning

confidence: 54%

Construction of performance evaluation model of production vehicle based on big data

Liu,

Shi

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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“…Consequently, the acquisition device also captures the bearing fault sound-source signal with non-linear variations. However, due to the equal time intervals at which the acquisition device collects the signal, a linear relationship is established during acquisition [28]. The sound source of the bearing fault undergoes a displacement in the vertical-horizontal plane during its propagation.…”

Section: Analysis Of the Causes Of Doppler Aberrations In Rolling Bea...mentioning

confidence: 99%

Study on Extraction Method of Track Edge Acoustic Feature Based on Cyclic Stationary Analysis

Zhao,

Lu,

Chang

et al. 2023

Preprint

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Due to the non-contact measurement characteristics of trackside acoustic technology, it is now utilized for train bearing fault diagnosis. However, the relative motion between the train and the trackside acoustic detection device introduces Doppler aberration in the collected acoustic signal, which affects bearing fault diagnosis. Moreover, when a fault occurs in the train bearing, its acoustic signal exhibits cyclic smoothness characteristics that can be effectively analyzed using the cyclic smoothness method for more accurate judgment. In order to minimize diagnostic errors and enhance accuracy in bearing fault diagnosis, this study integrates bearing fault characteristics with Doppler aberration correction methods and cyclic smoothness techniques for current-stage bearings diagnostics. The overall time-domain graph becomes more compact with an approximately 50% increase in amplitude after correction compared to pre-correction values; other parameters experience enhancements ranging from 20-60%. These results validate the feasibility of our proposed approach and establish a framework for conducting bearing fault diagnosis based on cyclically smoothed Doppler aberration correction.

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数据集偏差情况下滚动轴承的故障诊断

Cited by 2 publications

References 33 publications

Construction of performance evaluation model of production vehicle based on big data

Construction of performance evaluation model of production vehicle based on big data

Study on Extraction Method of Track Edge Acoustic Feature Based on Cyclic Stationary Analysis

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