故障机理与领域自适应混合驱动的机械故障智能迁移诊断

Intelligent migration diagnosis of mechanical faults driven by hybrid fault mechanism and domain adaptation

航空发动机的健康稳定对于保障飞行器的安全运行具有重要的作用,针对各台发动机建立具备高准确率的智能诊断模型是飞行器稳定运行的关键。现有故障诊断方法在具备故障数据的条件下能取得较好效果,但实际应用中往往因仅含正常数据,无法实现诊断模型的构建。针对该问题,提出一种故障机理与领域自适应混合驱动的机械故障智能迁移诊断方法,该方法首先依据故障机理和源域数据建立旋转机械故障虚拟样本生成模型,再采用目标域正常数据实现生成模型对目标域的自适应,最后通过虚拟样本训练得到目标域故障诊断模型。采用标准数据集和实验室轴承数据对提出方法进行验证,结果表明,提出方法对不同型号轴承诊断时取得88.61%的平均准确率,相比对比方法高41.22%。

The health and stability of aircraft engines play an important role in ensuring the safe operation of aircraft,and the establishment of intelligent diagnostic models with high accuracy for different engines is the key to the stable operation of aircraft.The existing fault diagnosis methods can achieve good results when fault data are available,but the actual application cannot always realize the construction of the diagnosis model because it only contains normal data.To address this problem,a hybrid fault mechanism and domain adaptive driven intelligent migration diagnosis method for mechanical faults is proposed.This method firstly establishes a virtual sample generation model for rotating mechanical faults based on the fault mechanism and source domain data,then the normal data of the target domain is used to realize the adaption of the generation model to the target domain,and finally,the target domain fault diagnosis model is obtained by virtual sample training.The proposed method is validated using standard data sets and laboratory bearing data,and the results show that the proposed method achieves an average accuracy of 88.61% when diagnosing different types of bearings,which is 41.22% higher compared with the comparison method.