摘要
针对恶劣环境影响下齿轮特征信息难以被有效提取出来的情况,提出了一种基于自适应随机共振降噪和改进局部均值分解的齿轮故障诊断算法。利用粒子群优化算法对随机共振参数进行优选,实现最佳随机共振输出,提取出微弱故障信息;基于故障特征频率信噪比,改进局部均值分解,剔除伪分量的干扰,提取模糊熵特征对齿轮类型进行诊断识别。实验研究表明,该方法能较好地识别出多种齿轮类型,是一种有效的齿轮故障诊断算法。
Under the influence of bad environment, the gear characteristic information is difficult to be extracted effectively. A gear fault diagnosis method based on adaptive stochastic resonance（ASR） denoising and improved local mean decomposition（LMD） is proposed. The particle swarm optimization（PSO） algorithm is used to optimize the parameters of the stochastic resonance. The best stochastic resonance output is obtained and the weak fault information is extracted. Then, the fault characteristic frequency signal noise ratio（SNR） is used to improve LMD and remove the false components. The fuzzy entropy feature is extracted and the gear types are diagnosed and identified. The research shows that the method can identify gear types effectively and it is an effective method for gear fault diagnosis.
出处
《电子技术应用》
北大核心
2017年第4期90-93,共4页
Application of Electronic Technique
关键词
自适应随机共振
局部均值分解
支持向量机
故障诊断
adaptive stochastic resonance
local mean decomposition
support vector machine
fault diagnosis
作者简介
边兵兵（1975-），通信作者，男，硕士，副教授，主要研究方向：机械设计与制造，E-mail：649436919@qq．com。
