# Degradation Modeling and Prognostic Analysis Under Unknown Failure Modes

arXiv: https://arxiv.org/abs/2402.19294 · HTML: https://mlsanity.com/paper/2402.19294/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 42 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Compact and Effective Representations for Sketch-based Image Retrieval](https://mlsanity.com/paper/2104.10278/) (2021)

## Built on by (most cited first, 5 of 5)

- [Prognostics for Autonomous Deep-Space Habitat Health Management under Multiple Unknown Failure Modes](https://mlsanity.com/paper/2411.12159/) (2024)
- [Optimized Spectral Fault Receptive Fields for Diagnosis-Informed Prognosis](https://mlsanity.com/paper/2506.12375/) (2025)
- [Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures](https://mlsanity.com/paper/2602.19263/) (2026)
- [STEP: Learning STructured Embeddings for Progressive Time Series](https://mlsanity.com/paper/2605.31061/) (2026)
- [General Value Functions for Remaining Useful Life and Failure-Mode Prediction](https://mlsanity.com/paper/2607.22268/) (2026)

## Abstract (arXiv)

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.