Applicability of a new composite amusement ride safety device based on acoustic emission monitoring technology

Under the premise of ensuring safety, it is of great significance to realize the lightweight of the non-main load-bearing parts of amusement facilities. The purpose of this study is to study the failure process of a newly designed carbon fiber bumper by using acoustic emission technology. First of a...

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Bibliographic Details
Published in:Materials research express 2024-05, Vol.11 (5), p.55304
Main Authors: Liu, Ran, Liang, Yu-Jiao, Liu, Shuo, Zhang, Peng-Fei, Qiao, Shuai, Li, Qing, Ma, Lian-Hua, Zhou, Wei
Format: Article
Language:English
Subjects:
acoustic emission
Acoustic emission testing
Acoustics
amusement ride
Amusement rides
Bearing capacity
Bumpers
Carbon fibers
Classifiers
Collision avoidance
Composite structures
Damage
damage identification
Entropy (Information theory)
Fourier transforms
Machine learning
Monitoring
Safety devices
Safety factors
Supervised learning
Window functions
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Summary:Under the premise of ensuring safety, it is of great significance to realize the lightweight of the non-main load-bearing parts of amusement facilities. The purpose of this study is to study the failure process of a newly designed carbon fiber bumper by using acoustic emission technology. First of all, the design of carbon fiber anti-collision bar can effectively restrain passengers, and its weight is reduced by nearly two-thirds compared to traditional metal materials. Subsequently, the load-bearing capacity of the bumper was tested and acoustic emission monitoring was carried out. The test results show that this new type of combined structure of amusement facility has high reliability, which exceeds the safety factor of 3.5 required by the steel structure of amusement facility. In addition, Renyi entropy was used to select the best window function of short-time Fourier transform, and the frequency domain characteristics of acoustic emission signals of typical damage modes were discussed through appropriately selected windows function. A classifier based on supervised machine learning is established by combining frequency features and acoustic emission feature parameters. Furthermore, the use of classifiers helps to understand the damage behaviour of composite structures.
ISSN:2053-1591
2053-1591
DOI:10.1088/2053-1591/ad43c0