电子测量与仪器学报

谢锋云,符羽,刘昆,肖乾,刘海涛.基于多域特征的螺栓松动检测方法研究[J].电子测量与仪器学报,2021,35(4):109-117

基于多域特征的螺栓松动检测方法研究

Research on bolt looseness detection method based on multi-domain feature

DOI：

英文关键词:bolt looseness VMD LSSVM multi domain feature detection method

基金项目:（中）基金项目┫┫国家自然科学基金项目（51805168，51565015）、江西省教育厅项目（GJJ180301，GJJ190307）、常州高技术重点实验室项目（CM20183004）资助

作者	单位
谢锋云	1. 华东交通大学机电与车辆工程学院
符羽	1. 华东交通大学机电与车辆工程学院
刘昆	2. 株洲国创轨道科技有限公司
肖乾	1. 华东交通大学机电与车辆工程学院
刘海涛	1. 华东交通大学机电与车辆工程学院

Author	Institution
Xie Fengyun	1. School of Mechanical and Electrical Engineering and Vehicle Engineering, East China Jiaotong University
Fu Yu	1. School of Mechanical and Electrical Engineering and Vehicle Engineering, East China Jiaotong University
Liu Kun	2. Zhuzhou Guochuang Rail Technology Co. , Ltd.
Xiao Qian	1. School of Mechanical and Electrical Engineering and Vehicle Engineering, East China Jiaotong University
Liu Haitao	1. School of Mechanical and Electrical Engineering and Vehicle Engineering, East China Jiaotong University

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中文摘要:

螺栓作为机械设备最常用的连接件,螺栓连接的稳定性对保障机械设备安全运行起着至关重要的作用,对螺栓松动程度进行检测有着重要意义。针对螺栓松动 4 种不同状态,提出了一种基于变分模态分解(VMD)及时频敏感特征与最小二乘支持向量机(LSSVM)相结合的螺栓松动检测方法。针对螺栓松动的 4 种不同状态,搭建了螺栓松动检测模拟实验平台,通过加速度传感器获取螺栓松动 4 种不同状态振动响应数据;提取了时频域敏感特征量,结合 VMD 分解的 IMF 分量能量熵组成状态检测敏感多特征向量,将提取的多特征向量结合 LSSVM 对螺栓不同松动状态进行识别,并对比基于经验模态分解(EMD)-LSSVM 及 EMD-多特征-LSSVM 检测结果。结果显示,基于多域特征的螺栓松动检测方法识别率优于 EMD-LSSVM 检测方法。

英文摘要:

Bolts are the most commonly used connectors for mechanical equipment. The stability of bolt connection plays an important role in ensuring the safe operation of mechanical equipment. It is of great significance to detect the state of bolt looseness. Aiming at the four different states of bolt loosening, a bolt looseness detection method based on variational mode decomposition (VMD) and timefrequency sensitive feature combined with least square support vector machine (LSSVM) is proposed in this paper. In order to identify the four different states of Bolt looseness, a simulation experimental platform for bolt loosening detection is built, and the vibration response data of four different states of bolt looseness are obtained by accelerometer. The time-frequency sensitive features are extracted, and the IMF component energy entropy decomposed by VMD is combined to form the sensitive multi-feature vector. The extracted multifeature vectors are combined with least square support vector machine to detect different looseness states of bolts. The recognition results are compared with the results of empirical mode decomposition ( EMD)-LSSVM and EMD multi-feature-LSSVM recognition. The recognition rate of bolt looseness detection method based on proposed VMD multi-feature in this paper is better than that of EMD-LSSVM detection method.

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