Degradation Modeling and Prognostic Analysis Under Unknown Failure Modes

• URL: http://arxiv.org/abs/2402.19294v1
• Date: Thu, 29 Feb 2024 15:57:09 GMT
• Title: Degradation Modeling and Prognostic Analysis Under Unknown Failure Modes
• Authors: Ying Fu, Ye Kwon Huh and Kaibo Liu
• Abstract summary: operating units often experience various failure modes in complex systems. Current prognostic approaches either ignore failure modes during degradation or assume known failure mode labels. High dimensionality and complex relations of sensor signals make it challenging to identify the failure modes accurately.
• Score: 17.72961616186932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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