WVAELET TRANSFORM IN TANDEM WITH AUTOREGRESSIVE TECHNIQUE FOR MONITORING AND DIAGNOSIS OF MACHINERY

A new approach is advanced based on wavelet transform in tandem with autoregressive technique. Wavelets and wavelet packets decompose dynamic signal into different bands and pro- vide multiresolution or multiscale view of signal. Autoregressive technique possesses effective function for short data processing. The approach is abopted to analyse nonstationary vibration sig- nals of mining excavator. The results indicate that nonstationary operating condition can be moni- tored and diagnosed. Transient toothmeshing frequency and rotating speed are measured. Weak vibration signal of defective ball bearing is extracted from strong vibration of gearbox and diag- nosed successfully. This approach is effective for nonstationary condition monitoring and early di- agnosis of latent fault.

基于PCA与蚁群算法的机械故障聚类诊断方法

针对现代机械复杂化、智能化的特点,为快速准确地诊断出设备故障,提出了基于PCA与蚁群算法的机械故障聚类诊断新方法。定义了聚类准确率判别因子,对主元的选取进行自适应调整,利用基于高斯径向基核函数的主元分析方法实现了故障特征提取。以蚁群算法解决旅行商问题为原型,定义了城市圈,改进蚁群算法实现了双重寻优,把故障聚类转化为蚁群算法最擅长的寻求最优解问题,将改进的蚁群算法用于故障特征样本的聚类。实例分析证明了该方法的有效性。

可逆性后部脑病综合征的研究进展

可逆性后部脑病综合征是近年来临床发现的一种与大脑后部白质病变相关的综合征,由多种病因引发,发病机制尚不明确。常见临床症状为头痛、恶心、呕吐、抽搐、视觉障碍及精神状态改变等。特征性神经影像学异常通常见于顶-枕叶的白质,CT或MRI显示双侧大脑后部为主的对称性片状脑水肿。及时正确地治疗可使临床症状和影像学改变完全逆转,故早期正确诊断和治疗十分重要。