Abstract: An ensemble deep transfer learning method for fault diagnosis based on soft voting is proposed for diagnosing waterjet pump faults under variable working conditions. The source domain and few target domain data samples are normalized after FFT transformation and then fed into three deep transfer learning diagnosis models for training: the CORAL based deep transfer metric learning model, the MMD based deep transfer metric learning model, and the transfer component-based deep belief network. The target domain test samples are diagnosed and analyzed based on this approach. An ensemble deep transfer diagnosis model is subsequently established by combing the soft voting method to obtain the final diagnosis results. Through the diagnosis of three different types of faults in waterjet pumps under variable working conditions, the results show that the proposed ensemble deep transfer diagnosis model not only effectively addresses the high-precision fault diagnosis of waterjet pumps under variable working conditions, but also has better diagnostic accuracy than the single deep transfer fault diagnosis model.

Key words: waterjet pump, variable working conditions, deep transfer learning, ensemble deep transfer learning, fault diagnosis

摘要： 该文针对变工况下喷泵故障诊断难题,提出了基于软投票表决的集成深度迁移故障诊断方法。首先将源域和少量目标域数据样本经快速傅里叶变换（fast Fourier transform,FFT）后再进行归一化,分别输入基于相关性对齐（correlation alignment,CORAL）法的深度迁移度量学习模型、基于最大平均偏差（maximum mean discrepancy,MMD）法的深度迁移度量学习模型和基于迁移成分的深度信念网络等3个深度迁移诊断模型进行训练,并分别对目标域测试样本进行诊断分析;然后结合软投票表决法建立集成深度迁移诊断模型,进而获取最终诊断结果。通过对变工况下喷泵3种不同故障类型的诊断分析,表明该文提出的集成深度迁移诊断模型不仅可有效解决变工况下的喷泵故障高精度诊断难题,而且诊断精度也优于单个深度迁移故障诊断模型。

关键词: 喷泵, 变工况, 深度迁移, 集成深度迁移, 故障诊断