Explainable AI to Improve Machine Learning Reliability for Industrial Cyber-Physical Systems

Annemarie Jutte; Uraz Odyurt

arxiv: 2601.16074 · v2 · submitted 2026-01-22 · 💻 cs.LG

Explainable AI to Improve Machine Learning Reliability for Industrial Cyber-Physical Systems

Annemarie Jutte , Uraz Odyurt This is my paper

Pith reviewed 2026-05-16 11:50 UTC · model grok-4.3

classification 💻 cs.LG

keywords explainable AISHAP valuesmachine learningcyber-physical systemstime-series decompositionmodel reliabilityinput window sizepredictive performance

0 comments

The pith

SHAP analysis of time-series decomposition reveals insufficient context, so increasing input window size improves ML performance for industrial CPS.

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

The paper applies explainable AI to deep learning models deployed in industrial cyber-physical systems, where reliability matters for safety and economics. Using SHAP values on decomposed time-series components, the authors find evidence that models receive too little surrounding context during training. This diagnosis leads directly to a practical change: enlarging the window size of each data instance. The result is measurable improvement in predictive performance on unseen future data. A reader cares because these systems control sensitive infrastructure, so any method that turns black-box weaknesses into concrete model fixes reduces the risk of unexpected failures.

Core claim

By applying SHAP values to the effects of time-series data decomposition components on model predictions, the authors observe evidence on the lack of sufficient contextual information during model training. By increasing the window size of data instances, informed by the XAI findings for this use-case, they are able to improve model performance.

What carries the argument

SHAP values computed on components from time-series data decomposition, used to diagnose insufficient contextual information and guide enlargement of the input window.

If this is right

Higher reliability for ML components in safety-critical industrial infrastructure.
Improved generalization of predictions to data arriving after the training period.
A repeatable workflow in which XAI findings directly dictate changes to input representation.
Fewer instances of unexpected model behavior on new operating conditions in CPS environments.

Where Pith is reading between the lines

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

The same XAI-guided window adjustment might apply to other sensor-driven time-series tasks where context spans longer periods than initially assumed.
Larger windows could raise real-time inference latency or memory use, requiring trade-off analysis in deployed CPS.
Re-running the SHAP analysis after the change would confirm whether the original diagnosis was complete or if new patterns emerge.

Load-bearing premise

The SHAP patterns correctly identify insufficient contextual information as the root cause, and simply enlarging the input window will improve generalization on future data without introducing overfitting or latency problems.

What would settle it

Retraining the model on the same CPS data but with the larger window size and measuring accuracy on a truly future held-out test set that was never seen during the original XAI analysis.

Figures

Figures reproduced from arXiv: 2601.16074 by Annemarie Jutte, Uraz Odyurt.

**Figure 2.** Figure 2: C-SHAP workflow, including concept construction from signal data, mask [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 4.** Figure 4: Change in SHAP values for the test data when increasing the window size from 100 to 400. The change is shown as the mean and standard deviation of the difference across windows. of 200 and 400 data points, improved test accuracies to 87.9% and 92.3% respectively. Comparing SHAP values between the models with window sizes 100 and 400 ( [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: The predictions and SHAP values for two selected example segments [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗

**Figure 6.** Figure 6: Value histograms for component ‘Levels’, per class label. The arrows in [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗

read the original abstract

Industrial Cyber-Physical Systems (CPS) are sensitive infrastructure from both safety and economics perspectives, making their reliability critically important. Machine Learning (ML), specifically deep learning, is increasingly integrated in industrial CPS, but the inherent complexity of ML models results in non-transparent operation. Rigorous evaluation is needed to prevent models from exhibiting unexpected behaviour on future, unseen data. Explainable AI (XAI) can be used to uncover model reasoning, allowing a more extensive analysis of behaviour. We apply XAI to improve predictive performance of ML models intended for an industrial CPS use-case. We analyse the effects of components from time-series data decomposition on model predictions using SHAP values. Through this method, we observe evidence on the lack of sufficient contextual information during model training. By increasing the window size of data instances, informed by the XAI findings for this use-case, we are able to improve model performance.

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.

Desk Editor's Note private letter to a colleague

The paper applies SHAP to time-series decomposition components in an industrial CPS to diagnose insufficient context and then enlarges the input window to improve performance, but the abstract supplies no metrics or ablations to verify the gain.

read the letter

Colleague, the core of this paper is a straightforward case study: they take a deep learning model for some CPS prediction task, decompose the time series, compute SHAP values on the components, notice that the model seems to ignore longer-range patterns, and respond by increasing the data window size. They report that this change improves results. It is an applied workflow rather than a new technique or theorem. What works here is the direct link from explanation to a concrete preprocessing adjustment in a domain where reliability matters. For engineers already running time-series models on industrial data, seeing SHAP used this way on decomposed signals can give a practical template for spotting when context is missing. The soft spots are exactly where the stress-test note points. The abstract states an improvement but shows no numbers, no baseline comparisons, no error bars, and no test for whether the larger window introduces overfitting or latency problems on future data. SHAP attributions on autocorrelated series can highlight correlations without proving that adding history will generalize better, so the causal step from explanation to fix needs more support. If the full paper includes ablations across window sizes and checks on unseen data, that would address the main gap. This is for practitioners who deploy ML in CPS and want a worked example of XAI-guided tuning. It does not introduce new methods, so it is unlikely to change how most researchers think about XAI. I would send it to peer review because the domain is important and the workflow is clear enough to be worth referee scrutiny, even though the current evidence is thin.

Referee Report

3 major / 1 minor

Summary. The paper claims that applying SHAP-based XAI analysis to time-series decomposition components in ML models for industrial CPS reveals insufficient contextual information in the training data. The authors then increase the input window size based on these XAI findings and report improved model performance on the use-case.

Significance. If supported by rigorous quantitative evidence, the approach of using XAI to diagnose and correct input-window deficiencies could aid reliability in safety-critical CPS applications. The idea of interpreting SHAP attributions on decomposed components to guide hyperparameter choices is potentially useful, but the manuscript currently provides no metrics, baselines, or validation to substantiate the performance gain.

major comments (3)

[Abstract] Abstract: the claim that 'increasing the window size of data instances, informed by the XAI findings... we are able to improve model performance' supplies no quantitative metrics, error bars, baseline comparisons, model architecture details, or dataset description, leaving the central empirical result without verifiable support.
[Results] Results section: no ablation is reported that compares the XAI-selected window size against other enlargements, nor are statistical significance tests, overfitting checks, or latency measurements on future unseen data provided.
[Methodology] Methodology: the interpretation of SHAP patterns on decomposition components as direct causal evidence of 'lack of sufficient contextual information' is not accompanied by a test distinguishing correlation from causation in the presence of temporal autocorrelations; enlarging the window may introduce noise rather than improve generalization.

minor comments (1)

Add explicit definitions for all time-series decomposition components and SHAP computation parameters to allow reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important gaps in empirical validation and methodological clarity that we will address through targeted revisions. Below we respond point-by-point to the major comments.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that 'increasing the window size of data instances, informed by the XAI findings... we are able to improve model performance' supplies no quantitative metrics, error bars, baseline comparisons, model architecture details, or dataset description, leaving the central empirical result without verifiable support.

Authors: We agree that the abstract currently lacks the quantitative details needed to substantiate the performance claim. In the revised manuscript we will expand the abstract to report specific metrics (e.g., F1-score improvement with standard deviation across runs), a brief baseline comparison, model architecture summary, and dataset characteristics, ensuring the central empirical result is verifiable from the abstract alone. revision: yes
Referee: [Results] Results section: no ablation is reported that compares the XAI-selected window size against other enlargements, nor are statistical significance tests, overfitting checks, or latency measurements on future unseen data provided.

Authors: We accept that additional quantitative controls are required. The revised Results section will include an ablation comparing the XAI-selected window size against multiple alternative enlargements, paired statistical significance tests (e.g., t-tests with p-values), learning-curve analysis to assess overfitting, and inference latency measurements on held-out future data to confirm generalization. revision: yes
Referee: [Methodology] Methodology: the interpretation of SHAP patterns on decomposition components as direct causal evidence of 'lack of sufficient contextual information' is not accompanied by a test distinguishing correlation from causation in the presence of temporal autocorrelations; enlarging the window may introduce noise rather than improve generalization.

Authors: The SHAP attributions on decomposed components provide consistent correlational patterns indicating that certain components receive low attribution under the original window. While we do not claim strict causation, the decomposition itself isolates trend, seasonal, and residual effects, reducing the impact of raw autocorrelation. We will revise the Methodology section to explicitly distinguish correlation from causation, add a limitations paragraph on this point, and include a sensitivity check that monitors validation performance across window sizes to detect potential noise introduction. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical outcome stands on reported experiment

full rationale

The paper reports an empirical sequence: SHAP analysis on time-series decomposition components is used to observe evidence of insufficient contextual information, after which the input window size is enlarged and a performance improvement is measured. No equations define the improvement in terms of the SHAP values themselves, no fitted parameter is renamed as a prediction, and no self-citation chain is invoked to justify the result by construction. The central claim therefore remains an independent experimental finding rather than a tautology or statistical artifact forced by the paper's own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides insufficient technical detail to enumerate specific free parameters, axioms, or invented entities; the work rests on standard assumptions that SHAP values faithfully reflect model reasoning and that window size directly controls contextual information.

pith-pipeline@v0.9.0 · 5452 in / 1039 out tokens · 30749 ms · 2026-05-16T11:50:41.863768+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

20 extracted references · 20 canonical work pages · 1 internal anchor

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Jutte and U

Adadi, A., Berrada, M.: Peeking Inside the Black-Box: A Survey on Explain- ableArtificialIntelligence(XAI).IEEEAccess(2018).https://doi.org/10.1109/ ACCESS.2018.2870052 12 A. Jutte and U. Odyurt

work page arXiv 2018

[2] [2]

In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (2021).https://doi.org/10.1145/3447548.3467166

Bento, J.a., Saleiro, P., Cruz, A.F., Figueiredo, M.A., Bizarro, P.: TimeSHAP: Explaining Recurrent Models through Sequence Perturbations. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (2021).https://doi.org/10.1145/3447548.3467166

work page doi:10.1145/3447548.3467166 2021

[3] [3]

In: Pattern Recog- nition, Computer Vision, and Image Processing

Dardouillet, P., Benoit, A., Amri, E., Bolon, P., Dubucq, D., Credoz, A.: Explain- ability of Image Semantic Segmentation Through SHAP Values. In: Pattern Recog- nition, Computer Vision, and Image Processing. ICPR 2022 International Work- shops and Challenges (2023).https://doi.org/10.1007/978-3-031-37731-0_19

work page doi:10.1007/978-3-031-37731-0_19 2022

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Artificial Intelligence Review (2023).https://doi.org/10.1007/s10462-022-10354-7

Ferraro, A., Galli, A., Moscato, V., Sperlì, G.: Evaluating eXplainable artificial intelligence tools for hard disk drive predictive maintenance. Artificial Intelligence Review (2023).https://doi.org/10.1007/s10462-022-10354-7

work page doi:10.1007/s10462-022-10354-7 2023

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Goyal, Y., Feder, A., Shalit, U., Kim, B.: Explaining Classifiers with Causal Con- cept Effect (CaCE) (2020).https://doi.org/10.48550/arXiv.1907.07165

work page doi:10.48550/arxiv.1907.07165 2020

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Griffin, D., Lim, J.: Signal estimation from modified short-time Fourier transform. IEEE Transactions on Acoustics, Speech, and Signal Processing (1984).https: //doi.org/10.1109/TASSP.1984.1164317

work page doi:10.1109/tassp.1984.1164317 1984

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Heil, C.E., Walnut, D.F.: Continuous and Discrete Wavelet Transforms. SIAM Review (1989).https://doi.org/10.1137/1031129

work page doi:10.1137/1031129 1989

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Hgctnet: Handcrafted feature-guided cnn and transformer network for wearable cuffless blood pressure measurement,

Hoenig, A., Roy, K., Acquaah, Y.T., Yi, S., Desai, S.S.: Explainable AI for Cyber- Physical Systems: Issues and Challenges. IEEE Access (2024).https://doi.org/ 10.1109/ACCESS.2024.3395444

work page doi:10.1109/access.2024.3395444 2024

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E., Shen, Z., Long, S

Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences (1998). https://doi.org/10.1098/rspa.1998.0193

work page doi:10.1098/rspa.1998.0193 1998

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Jutte, A., Ahmed, F., Linssen, J., van Keulen, M.: C-SHAP for time series: An approach to high-level temporal explanations (2025).https://doi.org/10.48550/ arXiv.2504.11159, Under review

work page internal anchor Pith review Pith/arXiv arXiv 2025

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Kawauchi, H., Fuse, T.: SHAP-Based Interpretable Object Detection Method for Satellite Imagery. Remote Sensing (2022).https://doi.org/10.3390/rs14091970

work page doi:10.3390/rs14091970 2022

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In: 2022 27th International Conference on Automation and Computing (ICAC) (2022)

Khan, T., Ahmad, K., Khan, J., Khan, I., Ahmad, N.: An Explainable Re- gression Framework for Predicting Remaining Useful Life of Machines. In: 2022 27th International Conference on Automation and Computing (ICAC) (2022). https://doi.org/10.1109/ICAC55051.2022.9911162

work page doi:10.1109/icac55051.2022.9911162 2022

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Journal of the American Statistical Association , author =

Killick, R., Fearnhead, P., Eckley, I.A.: Optimal Detection of Changepoints With a Linear Computational Cost. Journal of the American Statistical Association (2012).https://doi.org/10.1080/01621459.2012.737745

work page doi:10.1080/01621459.2012.737745 2012

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In: Proceedings of the 35th International Conference on Machine Learning (2018)

Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., sayres, R.: Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). In: Proceedings of the 35th International Conference on Machine Learning (2018)

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Lundberg, S.M., Lee, S.I.: A Unified Approach to Interpreting Model Predictions. In: Advances in Neural Information Processing Systems (2017)

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Journal of Biomedical Informatics (2023).https://doi.org/10.1016/j.jbi

Nayebi, A., Tipirneni, S., Reddy, C.K., Foreman, B., Subbian, V.: WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley val- ues. Journal of Biomedical Informatics (2023).https://doi.org/10.1016/j.jbi. 2023.104438

work page doi:10.1016/j.jbi 2023

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Odyurt, U., Roeder, J., Pimentel, A.D., Alonso, I.G., de Laat, C.: Power passports for fault tolerance: Anomaly detection in industrial cps using electrical efb. In: 2021 XAI to Improve ML Reliability for Industrial CPS 13 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) (2021).https://doi.org/10.1109/ICPS49255.2021.9468262

work page doi:10.1109/icps49255.2021.9468262 2021

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Why Should I Trust You?

Ribeiro, M.T., Singh, S., Guestrin, C.: "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016).https: //doi.org/10.1145/2939672.2939778

work page doi:10.1145/2939672.2939778 2016

[19] [19]

Advances in Neural Information Processing Systems (2023)

Sun, A., Ma, P., Yuan, Y., Wang, S.: Explain Any Concept: Segment Anything Meets Concept-Based Explanation. Advances in Neural Information Processing Systems (2023)

work page 2023

[20] [20]

Jour- nal of Big Data (2024).https://doi.org/10.1186/s40537-024-00905-w

Wang, H., Liang, Q., Hancock, J.T., Khoshgoftaar, T.M.: Feature selection strate- gies: a comparative analysis of SHAP-value and importance-based methods. Jour- nal of Big Data (2024).https://doi.org/10.1186/s40537-024-00905-w

work page doi:10.1186/s40537-024-00905-w 2024