Fault detection in industrial battery production using naive Bayes networks
Kadda Mostefaoui,Sid Ahmed Mokhtar Mostefaoui,Said Mekroussi,Lazreg Hadji,Royal Madan
Abstract
This research presents a machine learning-based approach for monitoring an industrial battery production system using a Naive Bayesian Network, a probabilistic model widely recognized for its ability to handle uncertainty. The proposed framework infers system states from observed operational conditions and event data, providing predictive insights into machine behavior. Real-world production data were employed to train and validate the model, ensuring both accuracy and practical applicability. Through probabilistic inference, the model anticipates potential failures or abnormal behaviors, enabling timely maintenance interventions and minimizing downtime. Evaluation results demonstrate that the Naive Bayesian Network offers a robust and interpretable solution for industrial monitoring, with strong potential to enhance predictive maintenance strategies and improve the overall reliability and efficiency of battery manufacturing operations.
Key Words
Bayesian networks; failure analysis; machine learning; modelling; probability
Address
Kadda Mostefaoui, Sid Ahmed Mokhtar Mostefaoui — Artificial Intelligence and Systems Research Laboratory, Department of Computer Sciences, University of Tiaret, Tiaret, 14000, Algeria
Said Mekroussi — Research Laboratory of Industrial Technology, Department of Mechanical Engineering, University of Tiaret, Tiaret, 14000, Algeria
Lazreg Hadji — 3Department of Civil Engineering, University of Tiaret, Tiaret, 14000, Algeria
Royal Madan — Department of Mechanical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, Uttarakhand, India
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