Macroeconomics

Big data

Random forest

Process (computing)

Engineering

Reliability engineering

Ensemble learning

Embedded system

Real-time computing

Operating system

Economics

Production line

Data mining

Computer science

Manufacturing engineering

Boosting (machine learning)

Mechanical engineering

Internet of Things

Preventive maintenance

Industrial engineering

Artificial intelligence

Production system (computer science)

Factory (object-oriented programming)

Programming language

Predictive maintenance

Machine learning

Production (economics)

Expert Systems with Applications

In this study, a data driven predictive maintenance system was developed for production lines in manufacturing. By utilizing the data generated from IoT sensors in real-time, the system aims to detect signals for potential failures before they occur by using machine learning methods. Consequently, it helps address the issues by notifying operators early such that preventive actions can be taken prior to a production stop. In current study, the effectiveness of the system was also assessed using real-world manufacturing system IoT data. The evaluation results indicated that the predictive maintenance system was successful in identifying the indicators of potential failures and it can help prevent some production stops from happening. The findings of comparative evaluations of machine learning algorithms indicated that models of Random Forest, a bagging ensemble algorithm, and XGBoost, a boosting method, appeared to outperform the individual algorithms in the assessment. The best performing machine learning models in this study have been integrated into the production system in the factory.

Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time