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

2026-05-29 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

Data-driven Prognostics and Health Management (PHM) uses time-varying condition-monitoring data to diagnose system states and estimate remaining useful life in engineered assets. This work proposes a framework applying Tabular Foundation Models (TFMs) to industrial time series via in-context learning, converting raw unit-level signals into tabular rows. The evaluation compares TFMs (TabPFN, TabDPT) against sequence models and gradient-boosted trees across 12 PHM datasets for prognostics and diagnostics. Results indicate TFMs achieve the best average ranks (TabDPT 2.67, TabPFN 3.33 for prognostics; both 2.33 for diagnostics). TabPFN excelled on PHME20 (1.95 \u00b1 0.03 MAE) and Unibo (3.72 \u00b1 0.06 MAE), and TabDPT on N-CMAPSS Prognostics (6.85 \u00b1 0.02 MAE). TFMs demonstrated high data efficiency, performing competitively with 1-10% of training data, and robustness to missing values, with performance improving with increased sequence length on some datasets.

Key takeaway

For AI/ML engineers developing PHM solutions, consider Tabular Foundation Models (TFMs) like TabPFN or TabDPT. These models offer strong performance, data efficiency, and robustness to missing values across prognostics and diagnostics tasks. You can achieve competitive results with limited labeled data by leveraging their in-context learning capabilities. Ensure your context sets are distributionally rich to maximize TFM effectiveness.

Key insights

Tabular Foundation Models, via tabularization and in-context learning, offer a data-efficient and robust solution for diverse industrial PHM tasks.

Principles

• PHM data fragmentation hinders supervised learning.
• TFMs generalize across tasks with in-context learning.
• Context diversity is critical for TFM performance.

Method

A unified pipeline transforms raw PHM time-series into aligned feature-target sequences, then into flattened tabular samples (\u00a7\u00a7\u00a7X_m\u00a7\u00a7\u00a7) for TFM inference, ensuring consistent evaluation across model types.

In practice

• Convert time-series to tabular rows for TFM input.
• Use in-context learning with small, representative data.
• Integrate validation data into TFM context for richer inference.

Topics

• Tabular Foundation Models
• Prognostics and Health Management
• In-Context Learning
• Time Series Analysis
• Data Efficiency
• Industrial AI

Code references

• facebookresearch/hydra

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Related on AIssential

• Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models — Tabular Foundation Models offer a data-efficient, unified approach for diverse industrial PHM tasks via in-context learning.
• Pocket Foundation Models: Distilling TFMs into CPU-Ready Gradient-Boosted Trees — Distilling large tabular foundation models into CPU-native gradient-boosted trees significantly reduces inference latency.
• TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data — TabPFN-MT extends Prior-Data Fitted Networks to natively support efficient multitask in-context learning for tabular data.
• What do you think about Tabular Foundation Models [D] — Tabular Foundation Models offer high performance on small datasets but face significant resource, explainability, and licensing challenges.
• Staying Alive: Uncensored Survival Analysis with Tabular Foundation Models — Tabular Foundation Models can perform uncensored survival analysis without dataset-specific training.
• Distilling Tabular Foundation Models for Structured Health Data — Knowledge distillation effectively transfers TFM performance to lightweight models, significantly reducing inference costs for healthcare applications.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.