A Scalable Big Data Framework for Real-Time Predictive Maintenance in Industrial IoT

The rapid growth of Industrial IoT (IIoT) has led to vast volumes of real-time sensor data, necessitating intelligent and scalable maintenance solutions. This paper presents a big data–driven framework for real-time predictive maintenance, integrating Apache Kafka for data ingestion, Apache Spark Streaming for distributed analytics, and an LSTM-based deep learning model for fault prediction. The NASA CMAPSS dataset is replayed through a simulated streaming pipeline to emulate realistic industrial workloads, achieving processing rates of over one million events per minute. Experimental results demonstrate significant improvements over baseline methods, including a 90.4\% prediction accuracy, a reduction in false alarms by 30\%, and an average latency below 600 ms under high-throughput conditions. The proposed framework effectively addresses core big data challenges such as volume, velocity, and scalability, offering a robust and extensible solution for predictive maintenance in Industry 4.0 environments.