基于物元-阴性选择算法的轴箱轴承故障检测

Fault Detection of Axle Box Bearing Based on Matter-Element and Negative Selection Algorithm

针对高速列车轴箱轴承故障数据获取困难的问题,提出了一种无需先验知识的利用物元和阴性选择算法进行轴承故障检测的方法.首先利用多维物元构建阴性选择算法的检测器模型,以检测器与训练样本之间的综合关联度作为匹配规则,并在综合关联度约束范围内引入控制参数,实现检测器对非己空间的更大覆盖;其次,根据匹配规则和控制参数构建适应度函数,采用粒子群优化算法生成候选检测器,分析控制参数对检测器生成和粒子群优化算法收敛速度的影响;此外,为降低候选检测器集合的冗余度,基于关联度提出了检测器特征参数区间的合并规则,将成熟检测器个数降低至18个;最后,通过信号模拟方法生成轴箱轴承的各类故障信号,建立100组测试样本,并利用18个成熟检测器进行故障检测.研究结果表明:成熟检测器对不同类轴承故障均具有较好的检测性能,正常样本的检测器激活率为1.11%,故障样本的检测器激活率不低于96.67%.

In view of the difficulty in obtaining the fault data of high-speed train axle box bearings,a fault detection method combined matter-element model and negative selection algorithm(NSA)was proposed,which did not need prior knowledge.Firstly,detectors of NSA were constructed by means of multi-dimensional matter-element model,and the comprehensive correlation degree(CCD)between detectors and training samples was used as the matching rule.In order to achieve greater coverage of the non-self space by the detector,a control parameter was introduced within the constraint range of CCD.Then,the fitness function was constructed according to the matching rule and the control parameter,and the particle swarm optimization(PSO)algorithm was utilized to generate candidate detectors.The influence of control parameter on detector generation and convergence speed of particle swarm optimization algorithm was analyzed.In order to reduce the redundancy of the candidate detector set,the merging rules of characteristic parameters intervals of candidate detectors were proposed based on the correlation function,and the number of mature detectors was reduced to 18.Various fault signals of axle box bearings were generated by signal simulation method,100 sets of test samples were established,and 18 mature detectors were used for fault detection.The results show that the mature detectors have good detection performance for different types of bearing fault,and the detector activation rate of normal sample is 1.11%,while its minimum value of fault samples is 96.67%.