In the large amount of available data, information insensitive to faults in historical data interferes in gear fault feature extraction. Furthermore, as most of the fault diagnosis models are learned from offline data collected under single/fixed working condition only, this may cause unsatisfactory performance for complex working conditions (including multiple and unknown working conditions) if not properly dealt with. This paper proposes a transfer learning-based fault diagnosis method of gear faults to reduce the negative effects of the abovementioned problems. In the proposed method, a cohesion evaluation method is applied to select sensitive features to the task with a transfer learning-based sparse autoencoder to transfer the knowledge learnt under single working condition to complex working conditions. The experimental results on wind turbine drivetrain diagnostics simulator show that the proposed method is effective in complex working conditions and the achieved results are better than those of traditional algorithms.

中文翻译：

---

复杂工况下基于深度学习的变速箱故障诊断

在大量可用数据中，对历史数据中的故障不敏感的信息会干扰齿轮故障特征的提取。此外，由于大多数故障诊断模型都是从仅在单个/固定工作条件下收集的脱机数据中获悉的，因此，如果处理不当，可能会导致复杂工作条件（包括多个和未知工作条件）的性能无法令人满意。提出了一种基于传递学习的齿轮故障诊断方法，以减少上述问题的负面影响。在提出的方法中，采用凝聚力评估方法通过基于转移学习的稀疏自动编码器选择任务的敏感特征，以将在单个工作条件下学习到的知识转移到复杂的工作条件下。