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All Journal Journal of Moeslim Research Technik
Ardi Azhar Nampira
Institut Teknologi Sepuluk November
Aerospace Engineering Automotive Engineering Control & Systems Engineering Electrical & Electronics Engineering Engineering

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AI-DRIVEN PREDICTIVE MAINTENANCE FOR SMART MANUFACTURING SYSTEMS: A CASE STUDY USING DEEP LEARNING ON SENSOR DATA Ardi Azhar Nampira; Nova Pangastuti; Wiwit Wiwit; Taufik Taufik
Journal of Moeslim Research Technik Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i3.2345

Abstract

The rapid advancement of Industry 4.0 has catalyzed the integration of artificial intelligence (AI) into smart manufacturing, with predictive maintenance emerging as a crucial application to reduce downtime and optimize operational efficiency. This study aims to develop and evaluate a deep learning-based predictive maintenance model by leveraging real-time sensor data from a smart factory environment. A convolutional neural network (CNN) architecture was implemented to detect anomalies and predict machinery failures in advance. The dataset, consisting of multivariate time-series signals from industrial sensors, was preprocessed and used to train, validate, and test the model’s predictive performance. Results indicate that the proposed deep learning model achieved a prediction accuracy of 94.6%, outperforming traditional statistical and machine learning methods in both precision and recall. The implementation of this AI-driven system enables proactive maintenance strategies, minimizing production losses and extending equipment lifespan. In conclusion, the research demonstrates the feasibility and effectiveness of deep learning in predictive maintenance applications for smart manufacturing systems and offers a scalable solution adaptable to diverse industrial settings.  
HYBRID SOLAR-BIOMASS SYSTEMS FOR OFF-GRID RURAL ELECTRIFICATION: TECHNO-ECONOMIC AND ENVIRONMENTAL ASSESSMENT Ardi Azhar Nampira; Felipe Souza; Rafaela Lima
Journal of Moeslim Research Technik Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i3.2354

Abstract

This study investigates the potential of a hybrid solar-biomass system to provide reliable and sustainable electricity to off-grid rural communities. The research background highlights the critical energy poverty prevalent in many rural areas, which lacks access to a stable power grid. While solar energy is a promising solution, its intermittent nature often limits its reliability. The primary objective is to conduct a comprehensive techno-economic and environmental assessment of a hybrid solar-biomass system. The study aims to design an optimized system configuration that can meet the energy demand of a typical rural village while minimizing the levelized cost of energy (LCOE) and reducing the system’s overall carbon footprint. The research seeks to demonstrate a viable and sustainable alternative to conventional fossil fuel-based generation. The research methodology involves creating a detailed energy model of a hybrid system using specialized software. The model integrates solar photovoltaic (PV) panels, a biomass gasifier, and a battery storage system.. The research findings demonstrate that the hybrid system is a technically and economically feasible solution for rural electrification. The optimized configuration achieved a low LCOE of $0.25/kWh, which is competitive with diesel-based generators. The environmental assessment projected a 75% reduction in GHG emissions. The conclusion is that hybrid solar-biomass systems provide a highly effective, cost-efficient, and environmentally sound approach to off-grid rural electrification. to both economic development and climate change mitigation goals.
Co-Authors Felipe Souza Nova Pangastuti Rafaela Lima Taufik Taufik Wiwit Wiwit