#prognostics News & Analysis

4 articles tagged with #prognostics. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · Jun 56/10

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Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models

Researchers propose applying Tabular Foundation Models to industrial Prognostics and Health Management (PHM) tasks by converting time-series signals into tabular representations. The approach demonstrates superior performance across diagnostics and prognostics compared to sequence models and transformers, while achieving high data efficiency in low-data industrial settings.

AINeutralarXiv – CS AI · Jun 16/10

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Benchmarking Machine Learning Uncertainty Quantification Methodologies for Predicting Turbine Gas Temperature Degradation

Researchers benchmarked five machine learning uncertainty quantification methods for predicting turbine gas temperature in engine health management systems. The study reveals distinct trade-offs between prediction interval coverage, width, and stability, providing practical guidance for selecting appropriate methods in real-world prognostics applications.

AINeutralarXiv – CS AI · Jun 16/10

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Scientific Machine Learning for Engine Health Management and Remaining Useful Life Prediction

Researchers present a multi-task machine learning framework for predicting turbine remaining useful life (RUL) and thermal indicators with quantified uncertainty. The system combines convolutional neural networks with bidirectional LSTMs to handle heterogeneous real-world fleet data and provides prediction intervals rather than point estimates, enabling risk-aware maintenance decisions.

AINeutralarXiv – CS AI · May 286/10

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Picid: A Modular Evaluation Infrastructure for Reproducible PHM Across Tasks and Domains

Researchers introduce Picid, a standardized evaluation infrastructure for Prognostics and Health Management (PHM) that addresses the reproducibility crisis in predictive maintenance across industries. The framework formalizes dataset construction, preprocessing, and evaluation metrics to enable fair comparisons of fault detection, diagnostics, and prognostics models across diverse domains like batteries, bearings, and engines.

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