Data-driven detection and isolation of wear-induced faults in industrial robot

Abstract

Industrial robots are complex systems that require technical expertise for condition monitoring and diagnostics. In small and medium scale industries, adequate skilled resources may not be available to monitor the robots consistently. In such cases, domain experts can provide maintenance advisory based on data collected through remote monitoring. The goal of this thesis is to develop and validate data-driven approaches to detect and identify the source of mechanical degradation in industrial robots using data typically collected through remote monitoring. Performance of data-driven methods is influenced by both data and algorithms. The factors that influence the data include robot application, tasks, and environment. Algorithms differ in terms of the extent to which they rely on an underlying model, and how they weight bias versus variance to isolate anomalous situations. With a remote monitoring solution, knowledge about all the factors may not be available and thus create uncertainty about the data generation process. Therefore, we adopted a two-pronged approach for the study: a) evaluate data-driven methods on simulated data, b) apply the algorithms that worked well with simulated data on real data enhanced with preprocessing methods. For evaluating data-driven methods, we identified wear through significant changes in torque values from normal operating conditions using principal component analysis and studied the effect of source and type of training data on detecting failures in industrial robots. Towards application of these results on real data, we next formulated strategies to detect the occurrence of wear-induced fault using supervised learning algorithm in a systematic hypothesis-driven study. That study sought to identify effective combinations of four preprocessing techniques on data collected from twenty-six industrial robots. Our results show that preprocessing techniques improved the fault detection performance. Finally, we investigated the problem of isolating the axis of fault by inferring pair wise directional relationships between all axes using an information-theoretic approach called Transfer Entropy (TE). The approach was validated on simulated data generated with an in-house robotic simulation tool. The axis responsible for wear was always detected when the wear was 10% above nominal value. The results of these two studies form the basis for informed data-driven strategies for fault detection and isolation in industrial robots and sets the stage for advanced adaptive detection approaches.