Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures

2026-02-22

AI Summary

This work addresses the challenge of predicting remaining useful life in complex manufacturing environments where existing models—reliant on fixed, known failure modes and labeled data—struggle to handle unknown, unlabeled faults arising in high-mix or adaptive production settings. To overcome this limitation, the authors propose a Bayesian nonparametric framework that integrates a Dirichlet process mixture model with a neural network–based prediction module, enabling joint online learning of fault mode discovery and remaining useful life estimation through an iterative feedback mechanism. The approach dynamically expands, merges, or infers novel fault modes without requiring static assumptions about their number or form. Evaluated on both synthetic and real-world aircraft engine datasets, the model demonstrates robustness and competitive or superior performance compared to state-of-the-art methods, highlighting its suitability for digital twin–enabled health management in dynamic manufacturing systems.

Technology Category

• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 competitively with or significantly better than existing approaches. It also exhibits robust online adaptation capabilities, making it well-suited for digital-twin-based system health management in complex manufacturing environments.

Problem

• prognostics
• failure modes
• multisensor systems
• unknown failures
• unlabeled data

Innovation

• Bayesian nonparametrics
• Dirichlet process mixture
• unsupervised failure mode discovery
• prognostics
• digital twin