摘要
针对齿轮箱启动过程中振动信号表现为非平稳非高斯特征,传统诊断方法诊断精度不高的现状,将阶次小波包和粗糙集理论引入到轴承的复合故障诊断中,利用计算阶次跟踪算法对瞬态振动信号进行重采样,采用小波包对该信号分解-重构,并对每个频段的能量进行归一化,构成一个特征向量,通过粗糙集理论得到清晰、简明的决策规则。并通过复合故障实例验证了此方法的有效性。
The vibration signals at start-up in the gearbox are non -stationary signals, and traditional ways of diagnosis have low precision. Order tracking and wavelet packet and rough sets theory are introduced in the compound - fault diagnosis of bearing. First, the vibration signals at start - up were resampled using computer order tracking arithmetic, and wavelet packet is used for equal angle distributed vibration signals decomposition and reconstruction. Second, energy distribution of every frequency band can be calculated according to normalization process. A new feature vector and clear and concise decision rules can be obtained by rough sets theory. Finally, the result of compound - fault example proves that the proposed method has high validity.
出处
《轴承》
北大核心
2009年第9期53-56,共4页
Bearing
基金
国家自然科学基金资助(项目编号:50775219)
关键词
滚动轴承
复合故障诊断
小波包
粗糙集理论
阶次跟踪
rolling bearing
compound-fault diagnosis
wavelet packet
rough sets theory
order tracking
