基于二级排序元学习算法的轴承智能故障诊断

Based on secondary-sequence meta-learning for bearing intelligent fault diagnosis

面对不同轴承故障数据集,由于小样本及不同数据集分布存在差异,导致模型在跨数据集诊断中精度不高。受人类学习过程启发,提出基于二级排序元学习算法对跨数据集的轴承故障诊断。首先对两个不同数据集进行第一级排序,将其对应为简单数据集和复杂数据集;其次把简单数据集分为元训练集和元测试集,仅对元训练集的所有任务进行第二级排序,再用少量元测试集的数据对二级排序后的模型进行微调,完成对简单数据集的故障诊断;最后用少量复杂数据集的数据对二级排序后的模型进行微调,完成对复杂数据集的故障诊断,实现跨数据集故障诊断。试验证明,与其他模型相比,该模型在轴承的小样本数据集及跨数据集上的分类结果表现更优异。

In the face of different bearing fault datasets,the small sample and differences in the distribution of each dataset lead to poor accuracy of the model for cross-dataset diagnosis.The proposed method introduces a fault detection approach that is based on the secondary-sequence meta-learning(SSML)algorithm,it is taken inspiration from the way humans gain knowledge.Firstly,the two independent datasets are sorted at the first sequence,which are divided into simple dataset and complex dataset correspondingly.Secondly,the simple dataset is divided into two datasets:one for meta-training and the other for meta-testing.Only the tasks in the meta-training set are sorted at the second sequence.The secondly sorted model is fine-tuned with a small amount of data in the meta-testing set to complete the fault diagnosis of the simple datasets.Finally,the model after secondary sequence is fine-tuned with a small amount of data from the complex dataset to complete the fault diagnosis of the complex dataset and realize the cross-data set fault diagnosis.SSML demonstrates superior performance compared to other models,this makes it very useful for diagnosing faults in complicated situations with small sample datasets and across different datasets.