摘要
滚动轴承作为机械系统的重要组成部件,由于工作环境恶劣,极易发生故障。故障轴承振动信号包含瞬态冲击成分、谐波成分、背景噪声等多种成分。为准确提取故障特征,基于稀疏表示理论,提出Laplace小波字典的轴承故障诊断方法。首先,截取振动信号片段若干,运用相关滤波法找到相关系数最大时的信号片段,依据此确定基底函数,构造Laplace小波原子并扩展成稀疏字典;然后,采用OMP算法,完成信号在字典下的稀疏重构,实现降噪;最后,对降噪信号进行包络分析,提取故障特征,确定故障类型。仿真和实验均验证了所提方法的有效性和可行性,具有一定的应用价值。
As an important component of mechanical system,rolling bearing is prone to failure due to harsh working environment.The vibration signal of faulty bearing includes transient impact components,harmonic components,background noise and other components.In order to extract fault features accurately,based on sparse representation theory,a bearing fault diagnosis method based on Laplace wavelet dictionary is proposed.First,a number of vibration signal fragments are intercepted,and the correlation filtering method is used to find the signal fragment with the largest correlation coefficient,and the basis function is determined accordingly,and Laplace wavelet atoms are constructed and expanded into a sparse dictionary.Then,the OMP algorithm is used to complete the sparse reconstruction of the signal under the dictionary to achieve noise reduction.Finally,the envelope analysis is performed on the noise reduction signal to extract the fault features and determine the fault type.Both simulation and experiment verify the effectiveness and feasibility of the proposed method,and it has certain application value.
作者
王伟
马萍
王聪
Wang Wei;Ma Ping;Wang Cong(School of Electrical Engineering,Xinjiang University,Urumqi 830017,China)
出处
《电子测量技术》
北大核心
2023年第2期136-145,共10页
Electronic Measurement Technology
基金
天山青年计划(2020Q066)
国家自然科学基金(52065064,51967019)项目资助
