基于振动声调制信号协整分析的螺栓预紧状态识别

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (21) : 1-6.

论文

张俊树1，李丹1，任伟新2

作者信息 +

Identification of bolt pre-tightening state based on cointegration analysis of vibro-acoustic modulation signals

ZHANG Junshu1, LI Dan1, REN Weixin2

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文章历史 +

摘要

振动声调制（vibro-acoustic modulation ,VAM）利用低频振动和高频信号在损伤处相互作用所产生的非线性旁瓣信号进行结构损伤识别。在实际工程中，边界条件往往会影响被监测结构，干扰旁瓣的识别，导致损伤状况的误判。本文针对边界条件影响下的螺栓连接状态监测问题，提出了基于VAM信号协整分析的螺栓预紧状态识别方法。首先提取不同边界固定力下旁瓣信号的幅值作为协整变量，然后根据残差序列判断螺栓的预紧状态，建立具有鲁棒性的螺栓预紧力状态量化指标。实验结果表明，协整分析可以消除边界条件对VAM的影响，能够很好地表征螺栓的状态；协整残差的均方根（root mean square，RMS）值作为量化指标，能够有效地识别螺栓预紧力状态。
关键词：振动声调制（VAM）；螺栓预紧力；边界条件；协整分析；残差

Abstract

Vibro-acoustic modulation (VAM) can identify the structural damage using the nonlinear side-band signals induced by the interaction of low-frequency vibration and high-frequency waves. However, the side-band signals are susceptible to the boundary conditions, resulting in false diagnosis in practice. In light of the bolt connection monitoring under the influence of boundary conditions, a method based on cointegration analysis is proposed. Firstly, the amplitudes of side-band signals under different boundary fixing preloading of bolts are extracted as the cointegration variable. By performing the cointegration analysis, the bolt looseness can be identified. A robust preloading state index of bolt connections is then proposed according to the residual. The experimental results have shown that the proposed method can isolate damage-sensitive features from boundary conditions to detect the bolt looseness state, and the bolt preloading can be quantified accordingly by root mean square (RMS) value of the residual.
Key words: vibro-acoustic modulation (VAM); bolt preloading; boundary conditions; cointegration analysis; residual

导出引用

张俊树1，李丹1，任伟新2. 基于振动声调制信号协整分析的螺栓预紧状态识别[J]. 振动与冲击, 2022, 41(21): 1-6

ZHANG Junshu1, LI Dan1, REN Weixin2. Identification of bolt pre-tightening state based on cointegration analysis of vibro-acoustic modulation signals[J]. Journal of Vibration and Shock, 2022, 41(21): 1-6

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