Real-time equipment monitoring and predictive maintenance using IoT and cloud analytics
This research paper, published in Springer Nature's Advances in Science, Technology & Innovation and presented at IICT 2020 (Innovations in Information and Communication Technologies), presents a comprehensive approach to predictive maintenance and fault detection in smart manufacturing systems using IoT sensor networks and cloud-based data analytics.
The system was designed to collect real-time sensor data from industrial machinery or home appliances and analyze it for signs of performance degradation or failure. The study demonstrates how temperature and humidity sensors, integrated with Raspberry Pi microcontrollers, can be used to track machine health, store data in the cloud (Google Cloud Storage & BigQuery), and enable intelligent fault detection.
The methodology paves the way for scalable, cost-effective predictive maintenance solutions that reduce downtime, lower servicing costs, and extend the lifespan of equipment.
DHT11 temperature and humidity sensor for real-time environmental data. Raspberry Pi 3B+ used as a microcontroller and gateway for data acquisition and local storage.
Developed Python scripts to control sensor readings, store CSV files locally, and automate cloud uploads. Integrated directory monitoring to push sensor data to Google Cloud Storage.
Leveraged Google Cloud Platform (GCP) for scalable data storage (GCS buckets), query-based fault detection (BigQuery), and visual analytics for temperature/humidity trends.
Data collected from a real-world appliance (20-year-old LG refrigerator). Time-series trends revealed normal operation, fault conditions, and partial overload states.
The implemented system successfully captured temperature and humidity data from a 20-year-old LG refrigerator using a DHT11 sensor and Raspberry Pi. Data was stored locally and uploaded to Google Cloud Storage, then analyzed in BigQuery to detect anomalies.
Visualizations revealed clear distinctions between normal cooling patterns, faulty conditions (e.g., sustained temperatures around 10 °C), and partial faults such as overloading or frequent door openings. The results validated the system's ability to detect performance issues in real time, supporting its utility for predictive maintenance applications in both industrial and domestic settings.
This system highlights the transformative role of IoT, cloud computing, and data analytics in Industry 4.0. By enabling real-time monitoring and predictive maintenance, the methodology supports safer, smarter, and more efficient operations in both industrial and consumer settings.