Operating units often experience various failure modes in complex systems, leading to distinct degradation paths. Relying on a prognostic model trained on a single failure mode may lead to poor generalization performance across multiple failure modes. Therefore, accurately identifying the failure mode is of critical importance. Current prognostic approaches either ignore failure modes during degradation or assume known failure mode labels, which can be challenging to acquire in practice. Moreover, the high dimensionality and complex relations of sensor signals make it challenging to identify the failure modes accurately. To address these issues, we propose a novel failure mode diagnosis method that leverages a dimension reduction technique called UMAP (Uniform Manifold Approximation and Projection) to project and visualize each unit's degradation trajectory into a lower dimension. Then, using these degradation trajectories, we develop a time series-based clustering method to identify the training units' failure modes. Finally, we introduce a monotonically constrained prognostic model to predict the failure mode labels and RUL of the test units simultaneously using the obtained failure modes of the training units. The proposed prognostic model provides failure mode-specific RUL predictions while preserving the monotonic property of the RUL predictions across consecutive time steps. We evaluate the proposed model using a case study with the aircraft gas turbine engine dataset.

翻译：在复杂系统中，运行单元常经历多种失效模式，导致不同的退化路径。依赖单一失效模式训练的预后模型在多失效模式下泛化能力较差，因此准确识别失效模式至关重要。现有预后方法要么忽略退化过程中的失效模式，要么假设已知失效模式标签——这类标签在实际中难以获取。此外，传感器信号的高维度和复杂关联性增加了准确识别失效模式的难度。针对这些问题，我们提出一种新型失效模式诊断方法：首先利用降维技术UMAP（均匀流形逼近与投影）将每个单元的退化轨迹投影至低维空间进行可视化；随后基于这些退化轨迹开发时间序列聚类方法识别训练单元的失效模式；最后引入单调约束预后模型，通过训练单元的失效模式同时预测测试单元的失效模式标签与剩余使用寿命（RUL）。该模型在提供失效模式特异性RUL预测的同时，保留了RUL预测随时间步进变化的单调性。我们使用飞机燃气轮机发动机数据集案例对所提模型进行了评估。