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arxiv: 2605.02507 · v1 · submitted 2026-05-04 · 💻 cs.LG · cs.AI

A Novel Preprocessing-Driven Approach to Remaining Useful Life (RUL) Prediction Using Temporal Convolutional Networks (TCN)

Florent Imbert , Tosin Adewumi , Hui Han This is my paper

Pith reviewed 2026-05-08 19:11 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords remaining useful lifeRUL predictionpreprocessing pipelinetemporal convolutional networksaero-engineC-MAPSS datasetpredictive maintenanceneural networks
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The pith

A novel preprocessing pipeline using complete temporal sequences improves accuracy and robustness of aero-engine remaining useful life predictions with temporal convolutional networks.

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

The paper argues that most prior work on remaining useful life prediction has emphasized neural network architectures while treating data preprocessing as secondary, and that a dedicated preprocessing step can lift performance by improving data quality and temporal structure. The proposed pipeline processes full temporal sequences and produces a remaining useful life label at every timestep rather than only at the end of a run. This lets the model learn fine-grained degradation patterns across an engine's entire operational history. Experiments on the NASA C-MAPSS aero-engine dataset show the resulting temporal convolutional network outperforming a range of existing models including CNN, RNN, LSTM, DCNN, and other TCN variants in both accuracy and robustness.

Core claim

The central claim is that applying a preprocessing pipeline which preserves complete temporal sequences and generates remaining useful life targets at each individual timestep, before feeding the data into a temporal convolutional network, produces higher accuracy and more robust predictions of aero-engine remaining useful life than standard approaches that focus mainly on architecture design.

What carries the argument

The novel preprocessing pipeline that converts raw sensor sequences into full-length temporal inputs with per-timestep RUL targets to improve data quality and temporal representation before model training.

If this is right

  • The model learns fine-grained degradation dynamics across the full operational life of each engine.
  • Remaining useful life estimates become available continuously at every timestep instead of only at run end.
  • The approach yields higher accuracy and robustness than the compared neural baselines on the C-MAPSS dataset.
  • Predictive maintenance decisions for aero-engines can be informed by higher-quality continuous prognostics.

Where Pith is reading between the lines

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

  • Preprocessing decisions may contribute as much or more to performance than the choice of neural architecture in sensor-based prognosis tasks.
  • The same pipeline could be tested on other industrial sensor datasets to check whether the benefit generalizes beyond aero-engines.
  • Controlled ablation studies that isolate each preprocessing component would clarify which elements drive the reported gains.

Load-bearing premise

The observed gains in prediction accuracy are caused mainly by the new preprocessing steps rather than by differences in hyperparameter choices, data splits, or implementation details of the baseline models.

What would settle it

An independent re-run of the listed baseline models (CNN, RNN, LSTM, DCNN, TCN, BiGRU-TSAM, AGCNN, ATCN) that applies the identical preprocessing pipeline, data splits, and training protocol and achieves equal or higher accuracy on the same C-MAPSS test sets would show the gains are not attributable to the preprocessing.

Figures

Figures reproduced from arXiv: 2605.02507 by Florent Imbert, Hui Han, Tosin Adewumi.

Figure 2
Figure 2. Figure 2: TCN concept dilated convolutions to extract temporal view at source ↗
Figure 4
Figure 4. Figure 4: TCN sliding window with zero padding on edges view at source ↗
Figure 3
Figure 3. Figure 3: Preprocessing pipeline for RUL. First column raw data, second column trimed times series, third column clip RUL view at source ↗
Figure 5
Figure 5. Figure 5: RUL/cycle prediction (red) vs actual RUL (blue) results on test engine units for FD001 to FD004. view at source ↗
read the original abstract

Accurate prediction of Remaining Useful Life (RUL) in aero-engines is vital for predictive maintenance, improved operational reliability, and reduced lifecycle costs. While deep learning approaches have demonstrated strong potential in this area, most existing methods focus primarily on model architecture design and treat input features uniformly, often neglecting the influence of data preprocessing. In this work, we propose a novel preprocessing pipeline that enhances RUL prediction by improving data quality and temporal representation before model training. Our approach leverages complete temporal sequences and generates RUL estimates at each timestep, enabling the model to capture fine-grained degradation dynamics and deliver continuous prognostic insights throughout the engine's operational life. To validate the effectiveness of the proposed pipeline, we conduct experiments on the NASA C-MAPSS dataset. Comparative evaluations against a suite of state-of-the-art neural models including CNN, RNN, LSTM, DCNN, TCN, BiGRU-TSAM, AGCNN, and ATCN, demonstrate that our approach consistently achieves superior accuracy and robustness in aero-engine RUL prediction. These results highlight the critical role of preprocessing in maximizing the effectiveness of neural prognostic models.

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 / 2 minor

Summary. The paper proposes a novel preprocessing pipeline for RUL prediction in aero-engines that processes complete temporal sequences and produces per-timestep RUL estimates, then feeds these into a TCN. Experiments on the NASA C-MAPSS dataset are claimed to show that this approach yields superior accuracy and robustness relative to CNN, RNN, LSTM, DCNN, TCN, BiGRU-TSAM, AGCNN, and ATCN.

Significance. If the performance gains can be shown to stem from the preprocessing pipeline under controlled conditions, the work would usefully draw attention to the impact of input preparation on neural prognostic models, an aspect that is frequently under-emphasized in the RUL literature.

major comments (2)
  1. [Experimental Results] The central claim attributes consistent outperformance to the novel preprocessing pipeline, yet the manuscript contains no ablation that applies the proposed preprocessing steps to the baseline architectures (including the authors' own TCN) while holding hyperparameters, data splits, and training protocol fixed. Without this control, the reported gains cannot be securely assigned to preprocessing rather than implementation differences.
  2. [Abstract and Results] The abstract asserts superior accuracy and robustness but supplies no quantitative metrics (RMSE, MAE, etc.), error bars, or details on baseline re-implementations and hyperparameter search. The full manuscript likewise lacks these controls, leaving the empirical comparison unverifiable.
minor comments (2)
  1. [Methodology] The description of the preprocessing pipeline would benefit from explicit pseudocode or a step-by-step algorithmic listing to allow exact reproduction.
  2. [Problem Formulation] Notation for the per-timestep RUL target and the temporal sequence construction should be defined once and used consistently throughout.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive report. The comments highlight important aspects of experimental rigor that we will address to strengthen the manuscript. We respond to each major comment below and indicate the planned revisions.

read point-by-point responses

  1. Referee: The central claim attributes consistent outperformance to the novel preprocessing pipeline, yet the manuscript contains no ablation that applies the proposed preprocessing steps to the baseline architectures (including the authors' own TCN) while holding hyperparameters, data splits, and training protocol fixed. Without this control, the reported gains cannot be securely assigned to preprocessing rather than implementation differences.

    Authors: We agree that an ablation study applying the preprocessing pipeline to the baseline architectures under fixed conditions would provide stronger causal evidence for the source of the performance gains. In the revised version, we will add these controlled experiments, applying the proposed preprocessing steps to CNN, RNN, LSTM, DCNN, and the authors' TCN baseline while keeping hyperparameters, data splits, and training protocols identical. This will allow clearer attribution of improvements to the preprocessing pipeline. revision: yes

  2. Referee: The abstract asserts superior accuracy and robustness but supplies no quantitative metrics (RMSE, MAE, etc.), error bars, or details on baseline re-implementations and hyperparameter search. The full manuscript likewise lacks these controls, leaving the empirical comparison unverifiable.

    Authors: We acknowledge that the abstract would be more informative with explicit quantitative support. We will revise the abstract to include key metrics (e.g., RMSE and MAE values on the C-MAPSS subsets) and direct comparisons to the listed baselines. In the full manuscript, we will expand the experimental details to document baseline re-implementations, the hyperparameter search procedure, and include error bars or standard deviations in the results tables to improve verifiability. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical validation

full rationale

The manuscript proposes a preprocessing pipeline for TCN-based RUL prediction and validates it via direct experimental comparison on the public C-MAPSS dataset against listed external baselines. No equations, fitted parameters presented as predictions, or derivation steps appear that reduce to the inputs by construction. Claims rest on falsifiable empirical results rather than self-definition, self-citation load-bearing premises, or imported uniqueness theorems. The noted lack of ablations concerns experimental controls, not circularity in any claimed derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the work relies on standard supervised training of a TCN on a public benchmark dataset.

pith-pipeline@v0.9.0 · 5503 in / 1151 out tokens · 67566 ms · 2026-05-08T19:11:31.716644+00:00 · methodology

discussion (0)

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Reference graph

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