基于人工免疫算法的转子系统扭矩扰动识别方法

Torque Disturbance Identification Based on Artificial Immune Algorithm for Rotor System

提出了一种对多支承转子系统在运转过程中所受的扭矩扰动进行识别的方法.该方法基于人工免疫系统的阴性选择算法,提取转子转速和轴承载荷作为特征参量,首先获得转子系统正常工作状态下的自己模式串,并随机产生初始检测器;进而利用人工免疫系统的进化学习机制,对转子扭矩受扰后获得的异常工况下的非己模式串进行学习和记忆;进化学习后生成典型扭矩扰动下的成熟检测器,能够区分和标记不同类型的扭矩在状态空间上所对应的区域.实验结果表明:该方法能有效地检测出转子系统异常扭矩扰动,并能对扭矩类型做出很好的识别.

An identification is proposed for the torque disturbance in a rotating multi-bearing rotor system. It is based on negative selection algorithm of artificial immune system taking the rotor speed and bearing load as feature vector. First of all, self-patters of the rotor system were obtained according to the normal working status and the initial detectors were produced at random. Secondly, non-self-patterns responded to the abnormal working status after the disturbance of the rotor system were learnt and memorized, taking advantage of the evolution learning mechanism based on the artificial immune theory. At last, the mature detector was produced after evolution learning under the typical torque disturbance, and the corresponding zones of different torque types on states space were distinguished and marked using the mature detector. The experimental results show that the method is effective in detecting abnormal torque disturbance of the rotor system and identifying the torque type.