Degradation Modeling and Prognostic Analysis Under Unknown Failure Modes
This work addresses prognostic analysis for maintenance in systems like aircraft engines, but it is incremental as it builds on existing dimension reduction and clustering techniques.
The paper tackles the problem of predicting remaining useful life (RUL) in complex systems with multiple unknown failure modes by proposing a method that uses UMAP for dimension reduction and time-series clustering to identify failure modes, achieving failure mode-specific RUL predictions with monotonic constraints, as evaluated on an aircraft gas turbine engine dataset.
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.