A Robust Conformal Framework for IoT-Based Predictive Maintenance

Alberto Moccardi (), Claudia Conte, Rajib Chandra Ghosh and Francesco Moscato
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Alberto Moccardi: Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
Claudia Conte: Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
Rajib Chandra Ghosh: Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
Francesco Moscato: Department of Information Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy

Future Internet, 2025, vol. 17, issue 6, 1-27

Abstract: This study, set within the vast and varied research field of industrial Internet of Things (IoT) systems, proposes a methodology to address uncertainty quantification (UQ) issues in predictive maintenance (PdM) practices. At its core, this paper leverages the commercial modular aero-propulsion system simulation (CMAPSS) dataset to evaluate different artificial intelligence (AI) prognostic algorithms for remaining useful life (RUL) forecasting while supporting the estimation of a robust confidence interval (CI). The methodology primarily involves the comparison of statistical learning (SL), machine learning (ML), and deep learning (DL) techniques for each different scenario of the CMAPSS, evaluating the performances through a tailored metric, the S-score metric, and then benchmarking diverse conformal-based uncertainty estimation techniques, remarkably naive, weighted, and bootstrapping, offering a more suitable and reliable alternative to classical RUL prediction. The results obtained highlight the peculiarities and benefits of the conformal approach, despite probabilistic models favoring the adoption of complex models in cases where the operating conditions of the machine are multiple, and suggest the use of weighted conformal practices in non-exchangeability conditions while recommending bootstrapping alternatives for contexts with a more substantial presence of noise in the data.

Keywords: predictive maintenance; internet of things; artificial intelligence (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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