Servo Motor Diagnostics using Anomaly Detectionisystems.unist.ac.kr/wp-content/uploads/sites/209/2017/05/Bumsoo_… · Servo Motor Diagnostics using Anomaly Detection Bumsoo Park,

Servo Motor Diagnostics using Anomaly Detection

Bumsoo Park, Haedong Jeong and Seungchul Lee*

UNIST

Contents

• Monitoring Systems for Robot Diagnosis

• Prognostics and Health Management (PHM) for robot

• Simulation Study

–Model-based Fault Detection and Isolation (FDI)

• Servo Motor Demonstration and Comparison

–Model-based FDI

– Unsupervised Learning

• Conclusion

2

Contents






–Model-based FDI


• Conclusion

3

Industrial Robots

• Increased robot usage on factory

• Breakdown of robot – 2015 GM Russia Operation Breakdown for 2 Months

• Losses over $100 million

• Approximately $1.6 million a day

4

Contents






–Model-based FDI


• Conclusion

5

• Key component

– Servo motor

• Dynamic movement (6-axis)

Prognostics and Health Management (PHM)

6

PHM

Hardware Redundancy

Analytic Redundancy

Output Estimation

Parity Space Parameter Estimation

Observer

Unsupervised Learning

Supervised Learning

Data-driven Model-based

Residual Generation

Prognostics and Health Management (PHM)

• Difficult to attach sensors due to dynamic movement of the arm – Mostly existing instrumentation are used (Encoder, Torque)

• In may cases, failure data is not available

7

Hardware Redundancy

Analytic Redundancy

Output Estimation

Parity Space Parameter Estimation

Observer


Supervised Learning


Residual Generation

PHM

Contents






–Model-based FDI


• Conclusion

8

DC Motor Position: System Modeling

• System representation in state space

9

http://ctms.engin.umich.edu/CTMS/index.php?example=MotorPosition&section=SystemModeling

0 1 0 0

0 0

0 1

1 0 0

db J K J V Ff

dti K L R L i L

y

i

: angle

: armature current

: volatage

: moment of intertia of the rotor

: motor viscous friction constant

: motor torque constant

: electric resistance

: electric inductance

: fault matrix

: fault

i

V

J

b

K

R

L

F

f

Fault Modeling and Residual Design

• Fault modeling

– Physically a load torque that acts on the inertia of the motor

• Residual Design

– Parameter estimation from output error (Luenberger observer)

10

Fault Observer Residual

ˆ ˆ ˆ( )

ˆ ˆ

x Ax Bu L y Cx

y Cx

ˆy y C 0 1 0

( ) : General Fault

TF

f t

y

y

: fault transition matrix

Contents






–Model-based FDI


• Conclusion

11

Image Specifications

Arduino

UNO

ATmega328 Microcontroller, 32KB Flash Memory, 16MHz Clock Speed, 14

Digital 1/O Pins

HS-311 Servo Motor

Cored motor type, 4 slider potentiometer drive, 24 tooth spline

style output shaft

Demo Specifications

• System configuration – Arduino UNO

– Servo motor

• Load generation – Anomaly is induced through manual press

12

Servo Motor Testbed

Operations Repetitive movement (0 - 180 degree)

Sensor Internal encoder

Sample Rate 50 Hz

Demo for Model-based FDI

• Residual = sum of magnitude of parameter

13

• Unsupervised Learning – Selected Feature:

– Normal state training: Gaussian distribution

– Decision Making: Mahalanobis distance

Demo for Unsupervised Data-driven Method

14

[ ] [ ]u k y k

Th

reshold

Data-driven vs. Model-based

15

Fault: Physical Defect

Data-driven

Feature

Anomaly Detection

Classification

Training Process

Model-based

Residual

Fault Detection

Fault Isolation

Modeling Process

System

Data-Driven


Training Process

Decision Making

Anomaly Detection

Observer based

System & Fault Modeling

Decision Making

Fault Detection

Model-based

Fault: Physical Defect

System


16

Data-driven

Feature

Anomaly Detection

Classification

Training Process

Model-based

Residual

Fault Detection

Fault Isolation

Modeling Process

Data-Driven


Training Process

Decision Making

Anomaly Detection

Observer based


Decision Making

Fault Detection

Model-based

• Data-driven methods

– Normal states based on feature

• Model-based methods

– Normal states based on residual


17

Training Process System & Fault Modeling

• Acquire normal data • Feature extraction

• Represents normal state • Normal state cluster in feature space

• System representation

• Fault Modeling

• Residual Design

[ 1] [ ] [ ]

[ ] [ ]

x k Ax k Bu k

y k Cx k

0 if no fault residual =

0 if fault exists

Feature3

0 100 200 300 400 500 600 700 800 900 1000-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Data Number

Am

plit

ud

e

Time Signal

Data Feature

( ) ( )[ ] n nj jj n k j n k

n n

n

f k R e e R e e


• Classify the state – based on the similarity of the predefined normal state

• Similarity can be represented differently

18

Data-Driven


Training Process

Decision Making

Anomaly Detection

Observer based


Decision Making

Fault Detection

Model-based

• Classify the state – based on the similarity of the predefined normal state

• Similarity can be represented differently


19

Decision Making (Classification)

• Mahalanobis Distance • Statistical distance of two points

• Residual-based threshold

1( ) ( )TD x x Anomaly

0 if no fault residual =

0 if fault exists

Conclusion

• Importance of robot diagnosis has increased

– More robots are being adopted

– Servo motors are the key component of robots

• Anomaly detection and fault detection

– Unsupervised learning (Mahalanobis distance)

– Model-based fault detection (Observer-based residual)

20


Training/ Modeling

• Define normal state

Feature/ Residual

Decision Making

[ 1] [ ] [ ] [ ]

[ ] [ ]

x k Ax k Bu k Ff k

y k Cx k

ˆ[ 1] [ 1]y k y k

Feature3

0 100 200 300 400 500 600 700 800 900 1000-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Data Number

Am

plit

ud

e

Time Signal

[ ] [ ]u k y k

1( ) ( )TD x x 0 if no fault

residual0 if fault exists

Documents

Servo Motor Diagnostics using Anomaly Detectionisystems.unist.ac.kr/wp-content/uploads/sites/209/2017/05/Bumsoo_… · Servo Motor Diagnostics using Anomaly Detection Bumsoo Park,