1. © ABB AB, Corporate Research - 1 9/20/2015 Master Thesis Presentation André C. Bittencourt Friction Change Detection in Industrial Robot arms

2. © ABB AB, Corporate Research - 2 Situation: tools available to maintenance based on periodical inspection, no information of its real condition The project: assist robot maintenance and diagnosis with a condition monitoring system. The task: develop methods for friction change detection in robot systems for condition monitoring. Challenges: Define everything from experiment to the final detection method Deal with the several restrictions that appear in industrial robot applications Implement the method and demonstrate its validity Project definitions The Task

3. © ABB AB, Corporate Research - 3 Task 1: Choosing the approach Review on fault detection methods Review on friction phenomena Task 2: Understanding the system Review on robotics Robot modeling & identification Task 3: Understanding the phenomena Disturbances effects Fault effects Task 4: Performing the detection Parameters & method Task 5: Method evaluation Approach The Task

4. © ABB AB, Corporate Research - 4 Basic: masses moving around motor driven axes Main phenomena Flexibilities Friction Backlash Torque ripple Measurement inaccuracies Arm Joint Robots T2 understanding the system

5. © ABB AB, Corporate Research - 5 Point to point trajectories with RAPID System in closed loop Unknown controller Memory storage limitation Limited sampling rate Limited workspace Limited experiment time Restrictions T2 understanding the system Available measurements Motor position Motor velocity Motor applied torque

6. © ABB AB, Corporate Research - 6 In steady-state Taking movements in both directions at steady-state, the friction torque can be estimated at each steady-state velocity Result Friction torque curve through velocity Modeling & Identification – friction curve T2 understanding the system

7. © ABB AB, Corporate Research - 7 Simple Coulomb + Viscous friction Solution as linear regression More complete model Solution as linear regression combined with extensive search Results Modeling & Identification – friction curve parametrization T2 understanding the system

8. © ABB AB, Corporate Research - 8 Several conditions may influence the friction behavior Operational point Joints configuration Presence of tool/load Gearbox oil Temperature Behavior under disturbances T3 understanding the phenomena

9. © ABB AB, Corporate Research - 9 Wear Increase of wear debris  Changes in contact surfaces Transient behavior Significant increase in low velocities range More tips to select change detection parameters Behavior under faults T3 understanding the phenomena

10. © ABB AB, Corporate Research - 10 Method definition Parameters used Fc Relates changes in the low velocities range Fv High velocities range Integral approx Robust parameter, useful to detect general changes Integral approx low velocities Robust parameter usefuld to detect changes in low velocities T4 performing the detection Estimation Distance measure Stopping rule Hypothesis test Estimation Distance measure Moving average Difference between Averaging

11. © ABB AB, Corporate Research - 11 Method definition Stopping rule CUSUM (cumulative sum) compensates variance of the parameters Kv set size of the fault T4 performing the detection Hypothesis NF: No fault H0: increased friction H1: high increased friction Estimation Distance measure Stopping rule Hypothesis test Estimation Distance measure Averaging Hypothesis test

12. © ABB AB, Corporate Research - 12 Scenario 1: Normal Operation No fault in the system Real case Same temperatures T5 Method evaluation

13. © ABB AB, Corporate Research - 13 Scenario 2: Gearbox breakdown Gearbox breakdown process Real case Same temperatures T5 Method evaluation

14. © ABB AB, Corporate Research - 14 The methods and experiments for robot and friction identification The friction behavior of robot joints under several different conditions The fault diagnosis framework based on friction estimated parameters Method will be included as part of a new diagnosis system Main contributions Conclusions