基于语音识别技术和最小二乘支持向量机的冲击螺栓松动检测

Furui Wang, Xuemin Chen, G. Song
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引用次数: 2

摘要

针对海底法兰螺栓松动的检测问题,提出了一种基于语音识别技术和最小二乘支持向量机的冲击检测方法。特别地,为了从冲击声信号中提取特征,我们采用了mel倒谱系数(MFCC)。最后,通过实验验证了所提方法的有效性。与现有的螺栓松动检测方法相比,该方法避免了传感器与结构的持续接触,大大提高了实用性,为基于网络物理系统的结构健康监测提供了指导。
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Percussion-based Detection of Bolt Looseness Using Speech Recognition Technology and Least Square Support Vector Machine
In this paper, to detect bolt looseness of a subsea flange, we develop a new percussion method using speech recognition technology and least square support vector machine. Especially, to extract features from percussion-induced sound signals, we employ the mel frequency cepstral coefficient (MFCC). Finally, an experiment is conducted to verify the effectiveness of the proposed method. Compared to current detection methods for bolt loosening, the proposed method can avoid constant contact between sensors and structures, which significantly improves practicability and provides guidance for structural health monitoring based on the cyber-physics systems.
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