© A

BB

AB

, Cor

pora

te R

esea

rch

- 1

04/

19/2

3

Master Thesis Presentation

André C. Bittencourt

Friction Change Detection in Industrial Robot arms

© A

BB

AB

, Cor

pora

te R

esea

rch

- 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 definitionsThe Task

© A

BB

AB

, Cor

pora

te R

esea

rch

- 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

ApproachThe Task

© A

BB

AB

, Cor

pora

te R

esea

rch

- 4

Basic: masses moving around motor driven axes

Main phenomena Flexibilities

Friction

Backlash

Torque ripple

Measurement inaccuraciesArm Joint

RobotsT2 understanding

the system

© A

BB

AB

, Cor

pora

te R

esea

rch

- 5

Point to point trajectories with RAPID

System in closed loop

Unknown controller

Memory storage limitation

Limited sampling rate

Limited workspace

Limited experiment time

RestrictionsT2 understanding

the system

Available measurements Motor position Motor velocity Motor applied torque

© A

BB

AB

, Cor

pora

te R

esea

rch

- 6

In steady-state

Taking movements in both directions at steady-state,

the friction torque can be estimated at each steady-state velocity

)()( agmf

)()(

)()(

agBWDmfBWD

agFWDmfFWD

Result Friction torque curve through

velocity

Modeling & Identification – friction curveT2 understanding

the system

)0,0( ma

© A

BB

AB

, Cor

pora

te R

esea

rch

- 7

Simple Coulomb + Viscous friction

Solution as linear regression

More complete model

Solution as linear regression combined with extensive search

)()( mvmcf fsignf

)()cosh(

)1(mv

mcf ff

Results

Modeling & Identification – friction curve parametrization

T2 understanding the system

© A

BB

AB

, Cor

pora

te R

esea

rch

- 8

Several conditions may influence the friction behavior Operational point

Joints configuration

Presence of tool/load

Gearbox oil

Temperature

50º50º

0º0º

80º80º

-70º-70º

-170º

0º

Behavior under disturbancesT3 understanding the phenomena

© A

BB

AB

, Cor

pora

te R

esea

rch

- 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 faultsT3 understanding the phenomena

© A

BB

AB

, Cor

pora

te R

esea

rch

- 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

0 tts

ttt

tt

yy

yy

1

10

)1(

)1(5.0

AveragingAveraging

ty t ,0 ts tgdata

© A

BB

AB

, Cor

pora

te R

esea

rch

- 11

Method definition

Stopping rule CUSUM (cumulative sum)

compensates variance of the parameters

Kv set size of the fault

T4 performing the detection

alarmtholdg

vsgg

t

ttt

,

),0max( 1

)(0 tv skv vthold 3

Hypothesis

NF: No fault

H0: increased friction

H1: high increased friction

Estimation Distance measure

Stopping rule

Hypothesis test

Estimation Distance measure

AveragingAveraging

ty t ,0 ts tgdataHypothesis

test

)( ts

© A

BB

AB

, Cor

pora

te R

esea

rch

- 12

Scenario 1: Normal Operation

No fault in the system

Real case

Same temperatures

T5 Method evaluation

© A

BB

AB

, Cor

pora

te R

esea

rch

- 13

Scenario 2: Gearbox breakdown

Gearbox breakdown process

Real case

Same temperatures

T5 Method evaluation

© A

BB

AB

, Cor

pora

te R

esea

rch

- 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 contributionsConclusions