Abstract
The article presents a case study applying industrial artificial intelligence to Condition-Based Maintenance in a wooden piece manufacturing company. The study focuses on the extraction system that transports wood residue to a warehouse, supplying a biomass plant for cold and heat generation in the factory. The objective is to predict the temperature of the ten induction motors in the extraction system using an Extreme Learning Machines-based methodology, enabling dynamic model prediction. Data from IoT sensors measuring the motors’ intensity, temperature, and humidity are collected every minute, pre-processed, and stored in a database. The pre-processing includes a single novel algorithm to detect and eliminate data containing possible sensor blockages. The results demonstrate an implementable methodology utilizing single-layer feedforward neural networks, prioritizing fast training while maintaining sufficient accuracy for detecting deviations in motor behaviour. The research offers valuable insights for preventive maintenance applications in similar industrial settings.
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