Information Entropy Analysis of Frictional Vibration under Different Wear States

Yu, Hai Jun; Wei, Haijun

doi:10.1080/10402004.2021.1998739

Cited by 1 publication

(3 citation statements)

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“…Then, one must calculate the probabilities of each state and use them to calculate the information entropy. The formula for information entropy is as follows [ 27 , 28 ]:

where

represents the probability of

being

. Equation (14) demonstrates that the information entropy of a system is inversely proportional to the probability of each state multiplied by the logarithmic sum of the probabilities.…”

Section: Methodology and Accuracy Metricsmentioning

confidence: 99%

“…Then, one must calculate the probabilities of each state and use them to calculate the information entropy. The formula for information entropy is as follows [27,28]:…”

Section: Information Entropymentioning

confidence: 99%

“…Studies have shown that the information entropy of mechanical friction vibration signals is closely related to the change of friction coefficient. This relationship can be used to monitor and evaluate the running-in process and wear state of machinery [ 26 , 27 ]. Ying et al proposed a rolling bearing fault diagnosis algorithm that utilizes ensemble entropy, the Holder coefficient theory, and the grey relation algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Vibration Characteristics of Bridge Structures under Seismic Excitation

Li,

Huang

2024

Entropy

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Bridges may undergo structural vibration responses when exposed to seismic waves. An analysis of structural vibration characteristics is essential for evaluating the safety and stability of a bridge. In this paper, a signal time-frequency feature extraction method (NTFT-ESVD) integrating standard time-frequency transformation, singular value decomposition, and information entropy is proposed to analyze the vibration characteristics of structures under seismic excitation. First, the experiment simulates the response signal of the structure when exposed to seismic waves. The results of the time-frequency analysis indicate a maximum relative error of only 1% in frequency detection, and the maximum relative errors in amplitude and time parameters are 5.9% and 6%, respectively. These simulation results demonstrate the reliability of the NTFT-ESVD method in extracting the time-frequency characteristics of the signal and its suitability for analyzing the seismic response of the structure. Then, a real seismic wave event of the Su-Tong Yangtze River Bridge during the Hengchun earthquake in Taiwan (2006) is analyzed. The results show that the seismic waves only have a short-term impact on the bridge, with the maximum amplitude of the vibration response no greater than 1 cm, and the maximum vibration frequency no greater than 0.2 Hz in the three-dimensional direction, indicating that the earthquake in Hengchun will not have any serious impact on the stability and security of the Su-Tong Yangtze River Bridge. Additionally, the reliability of determining the arrival time of seismic waves by extracting the time-frequency information from structural vibration response signals is validated by comparing it with results from seismic stations (SSE/WHN/QZN) at similar epicenter distances published by the USGS. The results of the case study show that the combination of dynamic GNSS monitoring technology and time-frequency analysis can be used to analyze the impact of seismic waves on the bridge, which is of great help to the manager in assessing structural seismic damage.

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“…Then, one must calculate the probabilities of each state and use them to calculate the information entropy. The formula for information entropy is as follows [ 27 , 28 ]:

where

represents the probability of

being

. Equation (14) demonstrates that the information entropy of a system is inversely proportional to the probability of each state multiplied by the logarithmic sum of the probabilities.…”

Section: Methodology and Accuracy Metricsmentioning

confidence: 99%

“…Then, one must calculate the probabilities of each state and use them to calculate the information entropy. The formula for information entropy is as follows [27,28]:…”

Section: Information Entropymentioning

confidence: 99%