Predictive Maintenance in the Food Industry: A Case Study Using Vibration Sensors and Machine Learning Techniques

Abstract

Predictive maintenance has emerged as a crucial strategy in industrial settings to enhance operational efficiency and minimize downtime. This study focused on implementing predictive maintenance in the context of the Monoblock machine, utilizing data from vibration sensors integrated with production stoppage records. The objective was to forecast future stoppages in the production line with a narrow time window to enable proactive maintenance interventions. Xtreme Gradient Boosting with oversampling techniques was employed to address the challenges of imbalanced data classes. Through extensive experimentation and analysis, promising results were achieved, demonstrating the ability to predict future stoppages with high accuracy within a 3-minute time window. The findings underscore the effectiveness of machine learning approaches in predictive maintenance applications, mainly when dealing with real-time sensor data and complex operational environments.