A predictive maintenance model for health assessment of an assembly robot based on machine learning in the context of smart plant

This paper introduces a predictive maintenance model based on Machine Learning (ML) in the context of a smart factory. It addresses a critical aspect within factories which is the health assessment of vital machinery. This case study specifically focuses on two brass accessories assembly robots and...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2024-12, Vol.35 (8), p.3995-4013
Main Authors: Chakroun, Ayoub, Hani, Yasmina, Elmhamedi, Abderrahmane, Masmoudi, Faouzi
Format: Article
Language:English
Subjects:
Artificial intelligence
Assembly
Business and Management
Context
Control
Engineering Sciences
Factories
Industrial plants
Machine learning
Machines
Manufacturing
Mechatronics
Plant layout
Prediction models
Predictive maintenance
Processes
Production
Robotics
Robots
Transmitters
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Summary:This paper introduces a predictive maintenance model based on Machine Learning (ML) in the context of a smart factory. It addresses a critical aspect within factories which is the health assessment of vital machinery. This case study specifically focuses on two brass accessories assembly robots and predicts the degradation of their power transmitters, which operate under severe mechanical and thermal conditions. The paper presents a predictive model based on ML and Artificial Intelligence (the Discrete Bayes Filter) to estimate and foresee the gradual deterioration of robots’ power transmitters. It aims at empowering operators to make informed decisions regarding maintenance interventions. The model is based on a Discrete Bayesian Filter (DBF) in comparison to a model based on Naïve Bayes Filter (NBF). The findings indicate that the DBF model demonstrates superior predictive performance compared to the NBF model. The predictive model’s investigation results were validated during testing on robots. This model enables the company to establish an informed and efficient schedule for maintenance interventions.
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-023-02281-3