Optimized Big Data Analytics Pipeline for Predictive Maintenance in Smart Manufacturing Systems

Abstract

Predictive maintenance (PdM) plays a vital role in modern smart manufacturing systems, where the emphasis is on minimizing unplanned equipment failures and maximizing operational efficiency. As a key enabler of Industry 4.0, PdM leverages real-time sensor data and advanced analytics to forecast potential breakdowns before they occur. However, implementing a reliable and scalable PdM framework remains a significant challenge due to the volume, velocity, and variety of data generated by industrial machines, alongside the need for low-latency decision-making. This study proposes an optimized big data analytics pipeline specifically tailored for predictive maintenance in smart manufacturing environments.The pipeline follows a modular framework comprising real-time data ingestion (Apache Kafka), scalable distributed storage (HDFS and Cassandra), and high-performance data processing (Apache Spark). Feature engineering and machine learning models—including Long Short-Term Memory (LSTM) networks for Remaining Useful Life (RUL) prediction and Random Forest classifiers for anomaly detection—are integrated to ensure predictive accuracy and rapid fault recognition. Visualization is achieved through Grafana dashboards with real-time alerts delivered via MQTT, enabling maintenance teams to make timely, data-driven decisions.Experimental validation using both the NASA CMAPSS turbofan engine degradation dataset and six months of real-world CNC machine data demonstrates the system’s robustness and accuracy. The proposed framework achieves over 94% prediction accuracy, significantly reduces unplanned downtime, and improves technician scheduling and component usage. Evaluation metrics such as RMSE, F1-score, latency, and throughput confirm the pipeline’s readiness for industrial deployment.Importantly, the modular and technology-agnostic architecture makes the proposed pipeline adaptable to a wide range of industries—including aerospace, automotive, and chemical manufacturing—offering scalable, interpretable, and real-time PdM solutions beyond the boundaries of smart factories.