IA Para el Mantenimiento Predictivo en Canteras: Modelado
Dependence on raw materials, especially in the mining sector, is a key part of today's economy. Aggregates are vital, being the second most used raw material after water. Digitally transforming this sector is key to optimizing operations. However, supervision and maintenance (predictive and corrective) are challenges little explored in this sector, due to the particularities of the sector, machinery and environmental conditions. All this, despite the successes achieved in other scenarios in monitoring with acoustic and contact sensors. We present an unsupervised learning scheme that trains a variational autoencoder model on a set of sound records. This is the first such dataset collected during processing plant operations, containing information from different points of the processing line. Our results demonstrate the model's ability to reconstruct and represent in latent space the recorded sounds, the differences in operating conditions and between different equipment. In the future, this should facilitate the classification of sounds, as well as the detection of anomalies and degradation patterns in the operation of the machinery.
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