Integrating servopress acoustic monitoring with Machine Learning for enhanced predictive maintenance: opportunities and limitations

This paper explores the innovative integration of acoustic monitoring of servo presses with machine learning algorithms to improve predictive maintenance strategies in industrial environments. Our research focuses on the application of advanced acoustic sensors to capture and analyze servo press acoustic data, which is critical in forming and pressing operations. This acoustic data serves as a key indicator of machine health, potentially revealing signs of wear or failure before they occur. We present the implementation of machine learning models trained on historical acoustic data, highlighting how these models can identify patterns indicative of machine degradation. The goal of this predictive approach is to optimize maintenance schedules, reduce downtime and extend the lifetime of industrial equipment. It also addresses the inherent challenges and limitations of applying artificial intelligence in this field. These include issues related to the quality and quantity of data required for accurate modelling, the complexity of real industrial environments, the ongoing need for AI system maintenance, and the risks associated with over-reliance on automated systems. Our findings suggest that while integrating servo press acoustic monitoring with machine learning offers significant potential for improving predictive maintenance, it requires a balanced approach that combines technological innovation with human expertise. The paper concludes with recommendations for future research and practical implementation strategies in industrial settings.