Data-Driven Gearbox Failure Detection in Industrial Robots

Abstract

Gearbox failures cost thousands of lost production hours in plants that use industrial robots. In this context, an automated monitoring system that can warn the user of an impending failure can save precious resources. This problem has been addressed in many other domains through the use of machine learning approaches. However, standard machine learning algorithms are limited in their ability to detect gearbox failures, mainly due to task variability arises from robot-specific data. To improve detection performance of machine learning approaches, in this paper we propose techniques to curate the data prior to building a classification model. In a systematic hypothesis-driven study exploring the effect of different preprocessing techniques, we evaluate training data augmentation with estimated measurements, data differencing to suppress task dependence, inclusion of local variation, and selection of principal components on data collected from 26 industrial robots from the field. Our results show that preprocessing techniques improve the failure detection performance.