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arxiv: 2606.05481 · v1 · pith:WZKRFBM7new · submitted 2026-06-03 · 💻 cs.LG · cs.AI· eess.SP

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

Raffael Theiler , Lev Telyatnikov , Leandro Von Krannichfeldt , Olga Fink This is my paper

Pith reviewed 2026-06-28 06:40 UTC · model grok-4.3

classification 💻 cs.LG cs.AIeess.SP
keywords tabular foundation modelsprognostics and health managementin-context learningdata efficiencycondition monitoringremaining useful lifePHM tasks
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The pith

Converting unit-level signals to tabular rows lets foundation models handle multiple PHM tasks with top average ranks and strong low-data performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that tabular foundation models can address fragmented and sparsely labeled industrial PHM data by turning time-varying condition-monitoring signals into tabular rows suitable for in-context learning. This representation supports both diagnostic classification and prognostic remaining-useful-life estimation within a single framework. The models are compared against sequence models, transformer baselines, and gradient-boosted trees on shared benchmarks, where they record the best average ranks across tasks. PFN-based variants remain competitive when training data are scarce. The results indicate that the tabular format can retain enough temporal context for effective learning while providing a unified interface for heterogeneous PHM problems.

Core claim

By converting raw unit-level signals into tabular rows, tabular foundation models perform well across multiple PHM tasks—including prognostics and diagnostics—and achieve the best average ranks; PFN-based models are competitive in low-data regimes.

What carries the argument

The conversion of time-varying condition-monitoring signals into tabular rows that supports in-context learning with tabular foundation models.

If this is right

  • A single tabular foundation model can serve as a reusable interface for mixed diagnostic and prognostic problems without task-specific retraining.
  • PFN-based tabular models reduce the labeled data volume required for acceptable accuracy on industrial assets.
  • Performance hinges on constructing representative context examples during subsampling of the tabular rows.
  • Temporal ordering information can be retained sufficiently in the row format to support both classification and regression PHM objectives.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same tabular conversion step could be tested on other fragmented time-series domains such as predictive maintenance in energy or transportation networks.
  • An ablation that varies the number of context rows per query would quantify exactly how much historical context the models need to match sequence-model accuracy.
  • If tabular foundation models continue to improve, they might allow maintenance planners to deploy one pretrained system across fleets with different sensor suites and failure modes.

Load-bearing premise

Turning time-varying signals into static tabular rows still keeps the temporal information needed for accurate diagnosis and remaining-life prediction.

What would settle it

A controlled test in which the same signals are presented with temporal order deliberately shuffled in the tabular rows, and performance on prognostic tasks drops sharply while diagnostic tasks remain stable, would show the representation fails to preserve required timing.

Figures

Figures reproduced from arXiv: 2606.05481 by Leandro von Krannichfeldt, Lev Telyatnikov, Olga Fink, Raffael Theiler.

Figure 1
Figure 1. Figure 1: Overview of the unified evaluation pipeline. Unit-level PHM signals are transformed into aligned feature–target windows and evaluated either by trained sequence models or by in-context tabular foundation models after tabularization. Validation data are used for model selection or tabular-shape selection before final test evaluation. converts continuous time-series into supervised samples that can be interp… view at source ↗
Figure 2
Figure 2. Figure 2: Our tabularization scheme. Time-series features are aggregated into their statistics on a per-window basis, along with the corresponding labels. These are then composed into a row in our table, providing richer context. 5. Practical Implementation Details 5.1. Unified Evaluation Pipeline Details All baselines are trained from scratch and evaluated alongside all foundation models using the evaluation infras… view at source ↗
Figure 3
Figure 3. Figure 3: reports the effect of missing data on normalized MAE for PHME20. TabPFN remains the strongest model under this missing-data setting, followed by TabDPT, indicating that the tabular foundation models retain their advantage when incomplete inputs are present. Unexpectedly, however, the explicit TabPFN NaN-token variant underperforms the imputed TabPFN configuration, indicating that TabPFN’s proposed missing-… view at source ↗
Figure 4
Figure 4. Figure 4: Predictive quantile distribution heatmaps for PHME20 and Unibo. RUL distribution with and without tabularization of time. R. Theiler, L. Telyatnikov et al. Page 19 of 43 [PITH_FULL_IMAGE:figures/full_fig_p019_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Scaling behavior for PHME20, Unibo, and MZVAV. The left column shows uniformly subsampled scaling laws; the right column shows blockwise subsampled scaling laws. We show the top 5 models for each dataset, which include TabPFN and TabDPT in all cases. A complete version with all models is included in the Appendix as [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Class balance in the MZVAV subset-ratio experiment, comparing uniformly subsampled and blockwise subsampling (Seed: 72). 0 20 40 60 80 100 Sequence Length 0.02 0.03 0.04 0.05 0.06 MAE (normalized) TabDPT TabPFN (a) PHME20 0 20 40 60 80 100 Sequence Length 0.04 0.05 0.06 0.07 0.08 MAE (normalized) TabDPT TabPFN (b) Unibo 0 20 40 60 80 100 Sequence Length 0.052 0.054 0.056 0.058 0.060 0.062 0.064 0.066 0.068… view at source ↗
Figure 7
Figure 7. Figure 7: Effect of sequence length on normalized MAE for selected prognostics datasets. R. Theiler, L. Telyatnikov et al. Page 21 of 43 [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Data-efficiency scaling under aggregate and blockwise context subsampling for PHME20, Unibo, and MZVAV. Each row compares aggregate random subsampling with contiguous blockwise subsampling, highlighting when small contexts preserve or lose coverage of trajectories or diagnostic classes. R. Theiler, L. Telyatnikov et al. Page 39 of 43 [PITH_FULL_IMAGE:figures/full_fig_p039_8.png] view at source ↗
read the original abstract

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. These tasks are central to maintenance planning, but industrial PHM data are often fragmented, partially observed, and poorly labeled, which hinders supervised learning. Foundation models offer a route toward reusable predictive systems, yet most time-series foundation models are designed for forecasting and assume long, coherent, regularly sampled sequences. To address this gap, we propose a framework for applying Tabular Foundation Models to industrial time series using in-context learning, and we evaluate them on a variety of PHM tasks. By converting raw unit-level signals into tabular rows, we show that these models perform well across multiple tasks - including prognostics, and diagnostics - and are highly data efficient. We compare them directly with sequence models, transformer baselines, and gradient-boosted trees under a common evaluation protocol. The results indicate that tabular foundation models achieve the best average ranks across prognostic and diagnostic tasks. Our findings further show that PFN-based models are competitive in low-data regimes, that temporal context can be preserved in the tabular representation, and that performance depends on representative context construction under subsampling. These results demonstrate that tabular foundation models provide a practical and general interface for heterogeneous PHM problems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes converting unit-level time-varying condition-monitoring signals from PHM applications into tabular rows to enable in-context learning with Tabular Foundation Models (TFMs), particularly PFN-based ones. It claims these models achieve the best average ranks across prognostic and diagnostic tasks compared to sequence models, transformers, and gradient-boosted trees, while being highly data-efficient in low-data regimes. The work asserts that temporal context can be preserved in the tabular representation and that performance depends on representative context construction under subsampling, offering a unified interface for heterogeneous, fragmented PHM data.

Significance. If the empirical claims hold under rigorous verification, the result would be significant for PHM by demonstrating a practical route to reusable, data-efficient predictive systems that bypass the need for large labeled datasets or task-specific retraining. It would also extend the applicability of tabular foundation models beyond static tabular data to time-series condition monitoring, with potential impact on industrial maintenance where data fragmentation is common. The direct comparison under a common protocol and emphasis on low-data performance are strengths.

major comments (2)
  1. [Abstract] Abstract: The central performance claim (best average ranks across tasks and data efficiency) cannot be evaluated because the text supplies no dataset descriptions, metric definitions (e.g., how RUL error or diagnostic accuracy is computed), statistical tests for rank differences, or exclusion criteria for tasks/models. This information is load-bearing for any assertion of superiority.
  2. [Abstract] Abstract (framework and results paragraph): The assertion that 'temporal context can be preserved in the tabular representation' and that results 'depend on representative context construction under subsampling' is not supported by any description of the encoding (row ordering, explicit time deltas, cumulative statistics, or windowing) or by an ablation that isolates the contribution of temporal order versus marginal feature distributions. Without this, it is unclear whether the approach retains the sequential degradation dynamics required for prognostics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We agree that it should be more self-contained to support the central claims and will revise it accordingly while ensuring the full manuscript details remain clear. Below we respond to each major comment.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central performance claim (best average ranks across tasks and data efficiency) cannot be evaluated because the text supplies no dataset descriptions, metric definitions (e.g., how RUL error or diagnostic accuracy is computed), statistical tests for rank differences, or exclusion criteria for tasks/models. This information is load-bearing for any assertion of superiority.

    Authors: We agree the abstract should reference key evaluation elements for self-containment. The full manuscript (Sections 3 and 4) details the datasets (e.g., C-MAPSS variants and other PHM benchmarks), metrics (RMSE for RUL estimation, accuracy/F1 for diagnostics), the common protocol across models, and task inclusion criteria. Statistical significance tests on rank differences are not currently reported. We will revise the abstract to briefly note the evaluation setup, datasets, and metrics while keeping length constraints in mind; we will also explore adding rank significance tests if they can be computed without new experiments. revision: partial

  2. Referee: [Abstract] Abstract (framework and results paragraph): The assertion that 'temporal context can be preserved in the tabular representation' and that results 'depend on representative context construction under subsampling' is not supported by any description of the encoding (row ordering, explicit time deltas, cumulative statistics, or windowing) or by an ablation that isolates the contribution of temporal order versus marginal feature distributions. Without this, it is unclear whether the approach retains the sequential degradation dynamics required for prognostics.

    Authors: The abstract summarizes findings whose supporting details appear in the method (Section 2) and results (Section 5). Section 2 describes the signal-to-table conversion, including feature extraction, windowing, and how rows are ordered to retain temporal structure via cumulative statistics and time-aware features. Section 5 reports performance under varying subsampling regimes, showing sensitivity to context construction. An explicit ablation separating temporal ordering from marginal distributions is not present. We will revise the abstract to reference the encoding approach in Section 2 and clarify the subsampling results; we can expand the method description if needed but note that space in the abstract is limited. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical benchmarking with no derivations or self-referential predictions

full rationale

The paper is an empirical evaluation of tabular foundation models on PHM tasks via conversion of time-series signals to tabular rows and in-context learning. It reports average ranks, data-efficiency comparisons, and observations about temporal context preservation, all grounded in experimental results under a shared protocol against baselines. No equations, fitted parameters renamed as predictions, self-citation load-bearing uniqueness theorems, or ansatzes appear in the derivation chain. The central claims rest on observable performance metrics rather than any reduction to inputs by construction. This is the standard case of a self-contained empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract introduces no free parameters, axioms, or invented entities; the approach rests on the pre-existing capabilities of tabular foundation models (e.g., PFNs) and the unstated premise that tabular conversion is information-preserving.

pith-pipeline@v0.9.1-grok · 5777 in / 1115 out tokens · 31995 ms · 2026-06-28T06:40:38.389337+00:00 · methodology

discussion (0)

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