| 周成江,徐 淼,贾云华,叶志霞,杨 鹏,袁徐轶.自适应 VMD 及其在状态跟踪及故障检测中的应用[J].电子测量与仪器学报,2022,36(12):55-66 |
| 自适应 VMD 及其在状态跟踪及故障检测中的应用 |
| Adaptive VMD and its application in state tracking and fault detection |
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| DOI： |
| 中文关键词: 变分模态分解 蚱蜢优化算法 机械零件 状态跟踪 故障检测 |
| 英文关键词:variational modal decomposition grasshopper optimization algorithm mechanical parts state tracking fault detection |
| 基金项目:云南省基础研究计划项目（202201AU070055）、云南省教育厅研究基金项目（2022J0131）资助 |
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| Author | Institution |
| Zhou Chengjiang | 1. School of Information Science and Technology, Yunnan Normal University,2. The Laboratory of Pattern Recognition and Artificial Intelligence |
| Xu Miao | 1. School of Information Science and Technology, Yunnan Normal University,2. The Laboratory of Pattern Recognition and Artificial Intelligence |
| Jia Yunhua | 1. School of Information Science and Technology, Yunnan Normal University,2. The Laboratory of Pattern Recognition and Artificial Intelligence |
| Ye Zhixia | 1. School of Information Science and Technology, Yunnan Normal University,2. The Laboratory of Pattern Recognition and Artificial Intelligence |
| Yang Peng | 1. School of Information Science and Technology, Yunnan Normal University,2. The Laboratory of Pattern Recognition and Artificial Intelligence |
| Yuan Xuyi | 3. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, 4. Engineering Research Center for Mineral Pipeline Transportation of Yunnan Province |
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| 中文摘要: |
| 针对变分模态分解(variational modal decomposition, VMD)的特征提取性能受到参数影响的问题,以及故障状态跟踪的
实时性较差的问题,提出一种状态预警线构造方法和自适应 VMD 方法并将其用于机械零件的故障检测。 首先,提取机械零件
全寿命振动信号的退化特征,基于 2σ 准则构造状态预警线来跟踪机械零件的退化状态并检测故障预警点。 然后,引入能量熵
和互信息构造适应度函数,通过蚱蜢优化算法(grasshopper optimization algorithm, GOA)构造自适应 VMD 模型来检测预警点附
近机械零件的故障状态。 结果表明,提出的状态预警线能更及时有效地检测出故障预警点,自适应 VMD 能更准确地检测出机
械零件故障,具有良好的应用价值。 |
| 英文摘要: |
| Aiming at the problem that the feature extraction performance of variational modal decomposition (VMD) is affected by its
parameters and the poor real-time performance of fault state tracking, an early warning approach and adaptive VMD method are proposed
and applied to mechanical part fault detection. Firstly, the degradation characteristics of the full-life vibration signal of mechanical parts
are extracted, and then the state warning line is constructed based on the 2σ criterion. Through the early warning line, the degradation
state of mechanical parts can be tracked and the fault early warning points can be detected. Then, the energy entropy and mutual
information are introduced to construct the fitness function, and an adaptive VMD model is constructed by grasshopper optimization
algorithm (GOA) to detect the fault state of mechanical parts near the early warning point. The results show that the proposed state early
warning line can detect the fault early warning points timelier and more effectively, and the adaptive VMD can detect the faults of
mechanical parts more accurately, which have good application value. |
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