Fault Detection of IoT Time-Series Data using Roboflow and Multi-Channel Gramian Angular Difference Fields

IoT sensor deployments generate extensive time series data, often challenging traditional analysis methods, but this blog post by Elliot Willis demonstrates how Gramian Angular Difference Fields (GADF) can transform such data into visual patterns for accurate classification using deep learning models. By converting sensor readings from a home HVAC system into images and training a ResNet18 model via Roboflow, the author achieved effective fault detection for various HVAC failure modes. GADF, proposed by Wang and Oates in 2015, turns one-dimensional time series data into two-dimensional images, maintaining temporal dependencies and allowing convolutional neural networks to interpret them effectively. This technique is useful in monitoring scenarios, such as rolling bearing fault diagnosis and photovoltaic system anomaly detection, and can handle multiple related time series by stacking GADFs into color channels. The post details the data collection, fault simulation, and the process of converting time series to GADF images using Home Assistant and a Raspberry Pi, demonstrating the approach's potential for real-time fault detection and scalability across IoT applications. By leveraging Roboflow's platform for training classification models, the workflow streamlines the deployment of sophisticated monitoring solutions, highlighting the practical benefits of integrating GADF with modern computer vision platforms in extracting actionable insights from complex temporal patterns.