Predictive Maintenance in Production Equipment-Machine Learning–Based Failure Prediction for Improved Reliability in a Latex centrifuge. Mechanical Engineering
This work presents a novel approach to constructing a predictive model using machine learning techniques for the early identification of machine faults for industrial maintenance purposes. The topic is justified by the importance of predictive maintenance in increasing operational efficiency, reducing costs, and improv...
This study proposes a machine learning-based predictive maintenance framework for machine failure prediction and fault diagnosis using the AI4I 2020 Predictive Maintenance Dataset, and identified torque, torque–speed ratio, and tool wear as the most influential predictors of machine failure.
Abhishek Sharma, Sujesh Kumar, Ramkrishna Mohan Kambli et al.· Journal of Intelligent Decis...· 0 citations
The article presents the results of the pilot implementation of the predictive equipment maintenance system at the SaryarkaAvtoProm LLP automotive industry enterprise. The system is based on an ensemble of machine learning algorithms trained on multichannel sensor data (vibration, temperature, pressure, flow rate of pr...
D. Aldasheva, O. Salykova, K. Maulenov et al.· Bulletin of Manash Kozybayev...· 0 citations
This study investigates the effectiveness of machine learning techniques in predictive maintenance for reducing unexpected machine faults and optimizing maintenance strategies. A synthetic dataset was utilized to model Faults based on key operational parameters such as temperature, torque, rotational speed, and product...
Feyyaz Alpsalaz, Y. Özüpak, H. Uzel et al.· Automation, Control, and Inf...· 0 citations
The findings of this study demonstrate the potential of lubrication data to forecast machine health and improve equipment reliability, providing valuable insights that enable researchers to enhance lubrication management strategies.
Q. Ahmed, Hasanur J. Molla, Ziad M. Aloqifi et al.· Journal of Trends and Challe...· 0 citations
Industrial production systems require intelligent maintenance solutions to minimize unplanned downtime, improve reliability, and support data-driven decision making. This study presents a machine learning framework for condition monitoring and fault classification in industrial production systems. The framework is eval...
Paraskevi Zacharia, Konstantinos Botsis, K. Moustris et al.· Machines· 0 citations
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