Data analytics for predictive maintenance of industrial robots

The predictive maintenance of industrial machines is one of the challenging applications in the new era of Industry 4.0. Thanks to the predictive capabilities offered by the emerging smart data analytics, data-driven approaches for condition monitoring are becoming widely used for early detection of anomalies on production machines. The aim of this paper is to provide insights on the predictive maintenance of industrial robots and the possibility of building a condition-monitoring system based on the data analysis of robot's power measurements. A predictive modeling approach is proposed to detect robot manipulator accuracy errors based on robot's current data analysis for predictive maintenance purposes. An experimental procedure is also carried out to oversee the correlation between the robot accuracy error and a set of extracted features from current time-series, and to evaluate the proposed predictive modeling. The obtained results are satisfactory and prove the feasibility of building a data-driven condition monitoring of robot manipulators using the electrical power time-series data analysis.

[1]  Ying Peng,et al.  Current status of machine prognostics in condition-based maintenance: a review , 2010 .

[2]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[3]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[4]  Antonio Visioli,et al.  On the fault detection and isolation of industrial robot manipulators , 2003 .

[5]  Mikko Kolehmainen,et al.  Feature-Based Clustering for Electricity Use Time Series Data , 2009, ICANNGA.

[6]  André Carvalho Bittencourt,et al.  A Data-driven Method for Monitoring Systems that Operate Repetitively -Applications to Wear Monitoring in an Industrial Robot Joint1 , 2012 .

[7]  Dr. Raimund Loser.  Laser Tracker and 6 DoF measurement strategies in industrial robot applications , 2011 .

[8]  Jay Lee,et al.  Recent advances and trends in predictive manufacturing systems in big data environment , 2013 .

[9]  Zhiwei Gao,et al.  From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis , 2013, IEEE Transactions on Industrial Informatics.

[10]  Francisco Herrera,et al.  Data Preprocessing in Data Mining , 2014, Intelligent Systems Reference Library.

[11]  Santiago Garrido,et al.  Accuracy and Calibration Issues of Industrial Manipulators , 2006 .

[12]  Sahin Yildirim,et al.  Fault detection on robot manipulators using artificial neural networks , 2011 .

[13]  S. Ramaswamy,et al.  Training data selection criteria for detecting failures in industrial robots , 2016 .