Multi-Source Data Fusion for Predictive Maintenance in Industrial Systems - Oluwapelumi Joseph Adebowale; Zamathula Sikhakhane Nwokediegwu (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
Predictive maintenance has become a cornerstone of modern industrial systems, enabling timely interventions that prevent equipment failures, reduce downtime, and extend asset lifespan. However, the reliability and accuracy of predictive maintenance models heavily depend on the quality and diversity of data sources. This study explores a multi-source data fusion framework that integrates heterogeneous data streams such as sensor readings, maintenance logs, operational records, and environmental conditions to enhance the performance of predictive maintenance in industrial settings. The proposed approach employs advanced data fusion techniques including feature-level integration, temporal alignment, and sensor calibration to harmonize structured and unstructured data. Machine learning algorithms such as random forests, long short-term memory (LSTM) networks, and support vector machines (SVM) are trained on the fused dataset to detect early signs of failure, estimate remaining useful life (RUL), and recommend optimal maintenance schedules. The model is validated through a case study conducted in a heavy machinery manufacturing plant, where multiple equipment types were monitored over a one-year period. Results show that multi-source data fusion significantly improves the accuracy of failure prediction models, with an observed increase in precision and recall of up to 20% compared to single-source models. Additionally, the fusion-based system enabled earlier detection of faults and more effective maintenance planning, resulting in a 30% reduction in unplanned downtime and a 15% improvement in overall equipment effectiveness (OEE). The study also discusses practical challenges such as data synchronization, noise handling, and the need for scalable infrastructure. This research demonstrates that leveraging multi-source data fusion enhances predictive maintenance capabilities and supports the development of more resilient, intelligent, and efficient industrial systems. It contributes to the evolving field of Industry 4.0 by providing a scalable and adaptable methodology that can be applied across various manufacturing sectors.