Industrial robots are a key component for several industrial applications. Like all mechanical tools, they do not last forever. The solution to extend the life of the machine is to perform maintenance on the degraded components. The optimal approach is called predictive maintenance, which aims to forecast the best moment for performing maintenance on the robot. This minimizes maintenance costs as well as prevents mechanical failure that can lead to unplanned production stops. There already exist methods to perform predictive maintenance on industrial robots, but these methods require additional sensors. This research aims to predict the anomalies by only using data from the sensors that already are used to control the robot. A machine learning approach is proposed for implementing predictive maintenance of industrial robots, using the torque profiles as input data. The algorithms selected are tested on simulated data created using wear and temperature models. The torque profiles from the simulator are used to extract a health index for each joint, which in turn are used to detect anomalous states of the robot. The health index has a fast exponential growth trend which is difficult to predict in advance. A Gaussian process regressor, an Exponentron, and hybrid algorithms are applied for the prediction of the time series of the health state to implement the predictive maintenance. The predictions are evaluated considering the accuracy of the time series prediction and the precision of anomaly forecasting. The investigated methods are shown to be able to predict the development of the wear and to detect the anomalies in advance. The results reveal that the hybrid approach obtained by combining predictions from different algorithms outperforms the other solutions. Eventually, the analysis of the results shows that the algorithms are sensitive to the quality of the data and do not perform well when the data present a low sampling rate or missing samples. ; Industrirobotar är en nyckelkomponent för flera industriella ...


    Access

    Download


    Export, share and cite



    Title :

    Machine learning in predictive maintenance of industrial robots


    Contributors:

    Publication date :

    2021-01-01


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    On-Board Predictive Maintenance with Machine Learning

    Zhang, Tianyu / Sun, Yong / Xu, Zhentao | SAE Technical Papers | 2019




    Thermographic Applications For Industrial/Utilities Predictive Maintenance

    Clark, John C. / Fitzgerald, Mark D. | SPIE | 1986


    Machine Learning Methods to Improve the Accuracy of Industrial Robots

    Murphy, Adrian / Higgins, Colm / Butterfield, Joe et al. | SAE Technical Papers | 2023