Journal of Smart Computing ISSN 2979-8752 · Biannual
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Research Article · Vol. 2, No. 1 (2026)

Explainable AI for Predictive Maintenance in Industry 4.0

Dinesh Bhagwan Hanchate*

Computer Science and Engineering, Dnyanshree Institute Engineering and Technology, Satara, Maharashtra, 415013, India

* Corresponding author: dinesh.hanchate@dnyanshree.edu.in

Abstract Predictive maintenance has become a cornerstone of Industry 4.0, where machine learning models analyze sensor data to anticipate equipment failures and reduce unplanned downtime. The most accurate models, however, are often opaque, and maintenance engineers are reluctant to act on predictions they cannot interpret. This tension between accuracy and interpretability limits industrial adoption of data-driven maintenance. This paper presents an explainable predictive maintenance framework that couples high-performing ensemble classifiers with post-hoc explanation methods—SHAP for global and local feature attribution and LIME for instance-level explanations—within a layered Industry 4.0 pipeline. The framework is specified formally and as pseudocode, covering data acquisition, preprocessing with class-imbalance handling, model training, and an explanation layer that exposes the drivers of each failure prediction to the engineer. Using an illustrative synthetic dataset modeled after industrial milling-machine telemetry, five classifiers are compared, and the selected gradient-boosting model is interpreted via global and local explanations. The results indicate strong predictive performance together with human-readable attributions, supporting explainable AI as a practical route to trustworthy predictive maintenance.

Keywords: Explainable AI, Predictive maintenance, Industry 4.0, SHAP, Machine learning

DOI
10.5281/zenodo.23257920
Pages
22-32
Published
20 Feb 2026
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To cite this article: Hanchate, D.B. (2026). Explainable AI for Predictive Maintenance in Industry 4.0. Journal of Smart Computing , 2(1) , 22-32. https://doi.org/10.5281/zenodo.23257920

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