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[2602.19263] Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures
Summary
This article presents a novel Bayesian nonparametric framework for prognostics in multisensor systems, addressing challenges with unknown and unlabeled failure modes in manufacturing environments.
Why It Matters
The research is significant as it enhances predictive maintenance strategies by allowing for the discovery of new failure modes without requiring labeled historical data. This capability is crucial for modern manufacturing systems that face unpredictable failures, thereby improving system reliability and operational efficiency.
Key Takeaways
- Introduces a Bayesian nonparametric framework for failure mode discovery.
- Combines unsupervised learning with neural networks for improved prognostics.
- Demonstrates robust online adaptation for complex manufacturing environments.
- Shows competitive performance against existing prognostic models.
- Addresses limitations of traditional models that rely on labeled data.
Statistics > Applications arXiv:2602.19263 (stat) [Submitted on 22 Feb 2026] Title:Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures Authors:Kani Fu, Sanduni S Disanayaka Mudiyanselage, Chunli Dai, Minhee Kim View a PDF of the paper titled Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures, by Kani Fu and 3 other authors View PDF HTML (experimental) Abstract:Modern manufacturing systems often experience multiple and unpredictable failure behaviors, yet most existing prognostic models assume a fixed, known set of failure modes with labeled historical data. This assumption limits the use of digital twins for predictive maintenance, especially in high-mix or adaptive production environments, where new failure modes may emerge, and the failure mode labels may be unavailable. To address these challenges, we propose a novel Bayesian nonparametric framework that unifies a Dirichlet process mixture module for unsupervised failure mode discovery with a neural network-based prognostic module. The key innovation lies in an iterative feedback mechanism to jointly learn two modules. These modules iteratively update one another to dynamically infer, expand, or merge failure modes as new data arrive while providing high prognostic accuracy. Experiments on both simulation and aircraft engine datasets show that the proposed approach performs competitive...