A Systematic Literature Review of Predictive Maintenance Strategies Using RUL and GRU for Industrial Packaging Machines - Dendi Permana; Muhammad Ridwan Andi Purnomo (Jurnal Kalibrasi, 2026)
Predictive Maintenance is a key strategy in improving asset reliability and efficiency in the era of Industry 4.0. However, most existing approaches rely on sensor data, making them difficult to implement in companies that do not yet have sensor infrastructure. This study presents a systematic literature review (SLR) of predictive maintenance approaches based on Remaining Useful Life (RUL) and Gated Recurrent Unit (GRU), focusing on their potential application in vertical liquid packaging machines in the food industry. The review was conducted on 25 articles from 2017 to 2024 using the PRISMA 2020 guidelines. The results show that the majority of studies are still sensor-based and focus on heavy industry, while studies utilizing non-sensor historical data are still very limited. In addition, the RUL-GRU hybrid approach is only found in a small number of studies and has never been applied in the context of the food processing industry. This study contributes by mapping these research gaps and formulating a conceptual framework for the application of RUL-GRU-based predictive maintenance using non-sensor historical data. These findings provide a scientific basis for the development of data-based maintenance strategies in companies with limited sensor infrastructure.