摘要
针对齿轮箱启动过程中振动信号表现为非平稳、非高斯特征及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轴承的故障诊断中,提出了一种新的故障诊断方法。首先利用阶次跟踪算法对瞬态振动信号进行重采样,得到等角度分布振动信号,其次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,构成特征向量,通过粗糙集理论得到清晰、简明的决策规则,最后通过故障实例验证该方法的有效性。
The vibration signals at start-up in the gearbox are non-stationary signals, and traditional ways of diagnosis have low precision. Order tracking wavelet packet and rough sets theory are introduced in the fault diagnosis of bearing. A new method of fault diagnosis is presented. First, the vibration signals at start- up are resampled using computer order tracking arithmetic and equal angle distributed vibration signals are obtained, and wavelet packet has been used for equal angle distributed vibration signals decomposition and reconstruction. Then, energy distribution of every frequency band can be calculated according to normalization process. A new feature vector can be obtained, then clear and concise decision rules can be obtained by rough sets theory. Finally, the result of fault example proves that the proposed method has high validity.
出处
《军械工程学院学报》
2010年第1期49-52,共4页
Journal of Ordnance Engineering College
基金
项目来源：国家自然科学基金资助项目（50775219）
关键词
阶次跟踪
小波包
粗糙集理论
轴承
决策规则
故障诊断
order tracking
wavelet packet
rough sets theory
bearing
decision rule
fault diagnosis
