A Bio-inspired Clustering Model for Anomaly Detection in the Mining Industry

Being able to detect anomalies for predicting machine breakdown is of critical importance in the mining industry. These anomalies are usually found in unlabelled sensor data, and therefore unsupervised models represent the preferred choice for the task. In this chapter, we propose the use of a bio-inspired clustering model based on the self-organizing map (SOM) to detect anomalies in real-world data provided by Joy Global, a manufacturer of high-productivity mining solutions. The proposed SOM is compared to two other well-known clustering models, namely k-means and fuzzy c-means. Simulation experiments using grease cycle data from the manufacturer show that the SOM is able to detect a more reasonable number of anomalies than k-means and fuzzy c-means. Based on real scenarios given by Joy Global, we devise a simple way to prevent machine failures by triggering alarms through the anomalies detected, and the SOM is again shown to be more capable of identifying incidents of potential machine breakdown compared to the other two clustering models.