AM-FM算子分解方法在滚动轴承故障诊断中的应用
AM-FM Operator Decomposition Method and Its Application in Rolling Bearing Fault Diagnosis
黄武 1郑近德 1童靳于 1潘海洋 1刘庆运1
作者信息
- 1. 安徽工业大学 机械工程学院,安徽马鞍山 243032
- 折叠
摘要
基于算子的零空间追踪算法能够实现复杂信号的自适应分解,其关键在于信号模型的构造与求解.通过定义一种新的可完全消除调幅调频信号的调幅调频算子(AFO),进一步建立了一种基于AFO的信号分解新模型.为了提高参数对信号分解的鲁棒性,将非参数正则化(NPR)方法用于解决上述模型的约束优化问题,提出了一种基于NPR的自适应信号分解方法——NPR-AFO.论文将NPR-AFO方法引入到机械故障诊断中,并通过仿真和滚动轴承局部故障实测数据分析,与现有的其他分解方法进行了对比.结果表明:所提方法不仅可以有效的提取故障特征,而且状态故障特征更加明显.
Abstract
The operator-based null-space tracking algorithm can realize the adaptive decomposition of complex signals,and it is a key step to construct and solve the signal model.A new signal decomposition model based on AFO is further established by defining a new AM-FM operator(AFO)which can completely annihilate AM-FM signals.In order to improve the robustness of parameters to signal decomposition,a nonparametric regularization(NPR)method is used to solve the constrained optimization problem of the above models,and an NPR-based adaptive signal decomposition method,called NPR-AFO,is proposed.This paper introduces the NPR-AFO method into mechanical fault diagnosis,and compares it with other existing decomposition methods through simulation and analysis of the measured data of local faults of rolling bearings.The results show that the proposed method can not only effectively extract fault features,but also the state failure characteristics are more obvious.
关键词
经验模态分解/零空间算子/调幅调频信号/故障诊断/非参数正则化Key words
empirical mode decomposition/null space operator/amplitude-modulated and frequency-modulated(AM-FM)signal/fault diagnosis/nonparametric regularization引用本文复制引用
基金项目
国家自然科学基金项目(51975004)
安徽省自然科学基金项目(2008085QE215)
出版年
2024
国家科技期刊平台
NSTL
万方数据