Big Data Driven Machine Learning Models For Predictive Maintenance In Smart Industries

Predictive maintenance is an important aspect of Industry 4.0 for reducing equipment downtimes and increasing operational efficiency. This work proposes a methodology in which Industrial Internet of Things (IIoT) sensors are integrated with machine learning models for predicting equipment failures in near real-time. IIoT sensors monitor critical parameters such as vibration and temperature, collect data using protocols such as MQTT and OPC-UA, with some real-time data streams from Apache Kafka and processing by Apache Flink. Preprocessing operations such as noise filtering and outlier detection are performed, and feature engineering is applied to derive key insights. A hybrid machine learning approach of CNNs and LSTMs is then subsequently used to capture spatially and temporally evolving patterns. Deployed to edge devices through TensorFlow Lite, these models allow for immediate failure detection. A Grafana dashboard shows real-time health of the equipment and predictive information to stakeholders. The intended outcome of this method is to provide maintenance decisions in a timely manner, reducing the risk of unplanned downtime and associated expenses.