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.

该研究提出了一种基于降维技术 UMAP 的故障模式诊断方法，通过将每个单元的退化轨迹投影和可视化到低维空间，并利用这些退化轨迹开发基于时间序列的聚类方法来识别训练单元的故障模式。最后，引入一种具有单调约束的预测模型，能够同时预测测试单元的故障模式标签和剩余寿命，并保持剩余寿命预测值在连续时间步长中的单调性。该模型通过飞机燃气涡轮发动机数据集进行了验证。