Predictive Maintenance in Production Robots in a Real World Industrial Setting

Abstract

With the exponential growth of data and advancements in AI technology, Predictive Maintenance (PdM) has emerged as a vital practice for optimizing equipment maintenance and minimizing unplanned downtime. This study was performed in collaboration with Sandvik Coromant, a company producing steel products and actively collecting data for evaluation purposes, to investigate how the collected data can be utilized for decision-making processes. Specifically, the study analyses vibrational data to address the issues related to unexpected tool malfunctions. Based on the available data, anomaly detection was identified as the most suitable approach to leverage the stored data based on its characteristics. A comparative study where various anomaly detection models were evaluated demonstrated that a reconstruction-based LSTM autoencoder yields the highest performance. The reconstruction approach exhibited its effectiveness in detecting and flagging potential abnormalities, capturing 71% of the malfunctions with an F1 score of 0.75 for the data used for the comparison. Extending the model to other tools displayed the challenges posed within time-series analysis, proving unique characteristics for each case. The findings from this study provide valuable insights into the implementation of anomaly detection techniques for leveraging collected data and enhancing decision-making processes in Sandvik Coromant and similar industrial settings.