基于阶次小波包与Markov链模型的转子早期故障诊断

针对转子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精度不高的现状,将阶次小波包和Markov链模型引入转子的早期故障诊断中,提出了一种新的自适应故障诊断模型。首先利用阶次跟踪算法对瞬态振动信号重采样,得到等角度分布诊断信号;其次采用小波包对该信号分解——重构,提取其在各频带的能量特征向量,通过Markov链模型对其进行预测;最后通过故障实例验证,结果表明:将阶次小波包变换和Markov链模型相结合进行故障诊断是可行而有效的。

A New and Effective Early Fault Diagnosis of Rotor Using Order Wavelet Packet and Markov Chain Model

The vibration signals at the start-up stage are non-stationary and non-Gaussian,and their diagnosis precision obtained with traditional diagnosis methods is not good.So we introduce the order wavelet packet and the Markov chain model that is based on particle swarm optimization into the early fault diagnosis of a rotor,thus proposing a new adaptive model of fault diagnosis.Sections 1 through 3 explain the early fault diagnosis mentioned in the title,which we believe is new and effective.Their core consists of:(1) we use the order tracking algorithm to carry out the resampling of the transient vibration signal,thus obtaining the diagnosis signal with equal angle distribution;(2) with the order wavelet packet,we decompose and reconstruct the equal angle distribution diagnosis signals and then extract their energy feature vectors at every frequency band;section 3 gives a five-step procedure for predicting the vectors with the Markov chain model.Section 4 conducts experiments on the early fault diagnosis of the rotor which uses vibration signals as its state signals;the experimental results,given in Tables 2 and 3,and their analysis show preliminarily that the short-term prediction results with our fault diagnosis model are very close to the actual values and have good prediction accuracy.