基于高阶统计特征实值阴性克隆选择算法的轴承故障检测

Bearing Fault Detection Using Real-valued Negative Clone Selection Algorithm Based on Higher Order Statistics

为解决轴承故障检测领域中异常样本数据不易收集的现实应用问题,提出一种基于实值阴性克隆选择算法(Real-valued negative clone selection,RNCS)的一类轴承故障检测模型。该模型只需要正常样本数据进行训练,利用改进的RNCS生成故障检测器集合以此实现轴承故障检测。该算法通过引入自适应变异算子和克隆成熟度判定算子。能够提高原有算法抗体的检测能力并加快算法收敛速度。为解决因高阶统计特征(Higher order statistics,HOS)信息繁多而无法有效实现智能检测的不足,模型利用HOS特征矩阵分解的奇异值谱为特征进行检测,该方法不仅有效地减少了数据维度及训练时间,同时还降低了噪声影响提高了检测性能。试验中对不同参数选择及不同正常训练样本个数情况下的检测器性能进行了分析。不同检测器个数之间的性能比较也在试验中给出。将建议的方法同原有算法进行比较,试验结果验证了设计思想的正确性和算法的高效检测性能。

In order to solve the practical application problems, including abnormal data insufficiency and unavailability which often happen in bearing fault diagnosis application, one-class bearing fault detection using improved real-valued negative clone selection （RNCS） algorithm based on higher order statistics （HOS） is presented. In this model, only normal sample data are needed for training purposes, RNCS is used to generate probabilistically a set of fault detectors that can detect any abnormalities （including faults and damages） in the behavior pattern of bearings. The clone mature operator and self-adaptive mutation operator are adopted in order to improve detection rate of antibodies and the convergence rate. Further, as the extracted HOS feature matrixes from original signal are too abundant to make further intelligent detection and diagnosis using HOS, feature matrixes are transformed to singular value spectrums which are used as features for overcoming this problem. The behavior of the classifier based on parameter selection and number of normal training samples is analyzed. Comparison of the performance of detection of RNCS with different detector＇s numbers is experimented. The proposed approach are compared with other detection techniques. The results show the relative effectiveness of the proposed classifiers in detection of the bearing condition with some concluding remarks.