基于AFI混合聚类算法的轴承故障诊断方法

Bearing Fault Diagnosis Method Based on AFI Hybrid Clustering Algorithm

针对滚动轴承振动信号标记数据量小、故障模式多样的现状,提出了一种基于AFI混合聚类算法的半监督式轴承振动信号故障诊断方法。利用小波包分解方法提取了信号的能量特征谱,并通过主成分分析方法增强了信号的特征;参考迭代自组织数据分析的“分裂”和“合并”的思想,为人工鱼群算法中的个体鱼增加了“分裂进化”和“合并进化”行为;采用模糊C均值方法定义了隶属度矩阵和目标函数,并利用改进的人工鱼群算法,迭代搜寻了目标函数的全局最优解,得到了各故障模式的聚类中心;通过计算测试数据的最近邻聚类中心,实现了故障模式识别。结果表明,该方法无需指定聚类簇数,能在标记数据量小的情况下完成训练,较同类方法表现出了更优的故障模式识别性能。

Aiming at the current situation that the labeled vibration data of rolling bearings is small and the failure modes are diverse,a semi-supervised fault diagnosis method for bearing vibration signal is proposed,which is based on AFSA-FCM-ISODATA(AFI)hybrid clustering algorithm.The wavelet packet decomposition method is used to extract the signal's energy feature spectrum,and the principal component analysis method is used to enhance the feature.Referring to the idea of“split”and“merge”in iterative self-organizing data analysis,we add“split evolution”and“merge evolution”behaviors for individual fish in the artificial fish swarm algorithm.Using the membership matrix and loss function defined by the fuzzy C-means method and the improved artificial fish swarm algorithm,the global optimal solution of the objective function can be searched,which means the centers of clusters representing each failure mode are also found.Fault diagnosis then can be realized by calculating the nearest neighbor cluster center of the test data.The results show that this method not only does not need to specify the number of clusters,but also can complete the training with a small amount of labeled data.Compared with other similar methods,it also achieves better failure modes recognition performance.