From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0

Riccardo Rosati (), Luca Romeo, Gianalberto Cecchini, Flavio Tonetto, Paolo Viti, Adriano Mancini and Emanuele Frontoni
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Riccardo Rosati: Marche Polytechnic University
Luca Romeo: Marche Polytechnic University
Gianalberto Cecchini: Sinergia Consulenze Srl
Flavio Tonetto: Sinergia Consulenze Srl
Paolo Viti: Benelli Armi Spa
Adriano Mancini: Marche Polytechnic University
Emanuele Frontoni: Marche Polytechnic University

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 1, No 5, 107-121

Abstract: Abstract The Internet of Things (IoT), Big Data and Machine Learning (ML) may represent the foundations for implementing the concept of intelligent production, smart products, services, and predictive maintenance (PdM). The majority of the state-of-the-art ML approaches for PdM use different condition monitoring data (e.g. vibrations, currents, temperature, etc.) and run to failure data for predicting the Remaining Useful Lifetime of components. However, the annotation of the component wear is not always easily identifiable, thus leading to the open issue of obtaining quality labeled data and interpreting it. This paper aims to introduce and test a Decision Support System (DSS) for solving a PdM task by overcoming the above-mentioned challenge while focusing on a real industrial use case, which includes advanced processing and measuring machines. In particular, the proposed DSS is comprised of the following cornerstones: data collection, feature extraction, predictive model, cloud storage, and data analysis. Differently from the related literature, our novel approach is based on a feature extraction strategy and ML prediction model powered by specific topics collected on the lower and upper levels of the production system. Compared with respect to other state-of-the-art ML models, the experimental results demonstrated how our approach is the best trade-off between predictive performance (MAE: 0.089, MSE: 0.018, $$R^{2}: 0.868$$ R 2 : 0.868 ), computation effort (average latency of 2.353 s for learning from 400 new samples), and interpretability for the prediction of processing quality. These peculiarities, together with the integration of our ML approach into the proposed cloud-based architecture, allow the optimization of the machining quality processes by directly supporting the maintainer/operator. These advantages may impact to the optimization of maintenance schedules and to get real-time warnings about operational risks by enabling manufacturers to reduce service costs by maximizing uptime and improving productivity.

Keywords: IoT; Decision support system; Predictive maintenance; Machine learning; Random Forest (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10845-022-01960-x

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