Teleoperation and System Health Monitoring · 2003-10-07 · Teleoperation and System Health...

Teleoperation and System Health Monitoring

Mo-Yuen Chow, [email protected]

Advanced Diagnosis and Control (ADAC) LabDepartment of Electrical and Computer Engineering

North Carolina State UniversityRaleigh, NC 27695-7911

USA

Teleoperation and System Health Monitoring, Mo-Yuen Chow 2

Teleoperation Potential applications in NASA

Remote robotics manipulator control and teleoperation.E.g.: SPDM (Special Purpose Dexterous Manipulator).


Main challenge in teleoperation

Challenge Network delay during data transfer among sensors, controllers, and actuators

Challenge Network delay during data transfer among sensors, controllers, and actuators

What are the causes of network delaysBandwidth constraintMultiple packet transmissionsPacket lossPacket collisionEtc.

What are the causes of network delaysBandwidth constraintMultiple packet transmissionsPacket lossPacket collisionEtc.

Effect of network delaysSystem performance degradationSystem instability

Effect of network delaysSystem performance degradationSystem instability


Delays in teleoperation

Controller( )ku

Plant( )ty

( )ky T( )τsckkT −y

( )kr ( )τcakkT −u

Z.O.H

Network

τcak

τsck

τck

( )ty

( ) ( )k kTr r

( ) ( )k kTu u

( ) ( )k kTy y

Computational delayController-to-actuator delaySensor-to-controller delay

Time skew : k∆: Continuous system output: Discrete reference signal: Discrete control signal: Discrete system output


Gain Scheduler Middleware (GSM)

Gain Scheduler Middleware (GSM) is a novel methodology to utilize middleware to enable an existing non-network-based controller so they can be used for networked control.The proposed methodology applies middleware to modify the controller output with respect to the current network traffic condition in addition to providing appropriate network conditions.

Controller

Middleware gain scheduler

Networktraffic

estimator

Feedbackpreprocessor

Gainscheduler

NetworkRemotesystem

Probing

Controlsignal

Feedbacksignal


Optimal gain [K(i) surface] with respect to different curvature and time delay

( )A i ( )iτ

Opt

imal

K(i)

00.2

0.40.6

0.81

0

2

4

6

8

0

1

2

3

4

A

B

UV position X

UV position Y

path forthe UVto follow

vX

vY

J

v

τ

0 0 .2 0 .4 0 .6 0 .8 1 05

1 00

1 0

2 0

3 0

4 0

5 0

6 0

( )A i ( )iτ

( ) 0.1K i =

( )ˆ 1J i +

( ) 4K i =

( ) 1.4K i =

ε


Illustration: Tele-operation of an Unmanned Vehicle (UV)

The actual networked mobile robot is setup with the following configuration:

– The speed of both wheels are controlled by two PI controllers using a SK-515C microcontroller board.

– The path tracking controller and GSM are implemented on a notebook computer as RTLinuxprocesses.

– Data transfers between the tracking controller (or GSM) are delayed by an RTLinux process, using the internal hardware timer and delays from ADAC to KU.

– This is a scenario to focus specifically on delay effects and to avoid packet loss effects.


Experimental results in virtual reality world

-0.5 0 0.5 1 1.5 2 2.5-0.5

0

0.5

1

1.5

2

2.5

x(m)

y(m

)

Robot tracks

No delayWith delay, no GS MWith delay and GS M

0 10 20 30 40 50 60 700

0.02

0.04

0.06

0.08

0.1

0.12

Time(s)

D(m

)

No delayWith delay, no GSMWith delay and GSM

0 10 20 30 40 50 60 700

0.02

0.04

0.06

0.08

0.1

0.12

Time(s)

D(m

)

No delayWith delay, no GSMWith delay and GSM


Web-based remote access real-time Mechatronics Teleoperation Lab

PIDCONTROLLER

PROCESSor

PLANT

+-

COMMANDRESPONSE

PID controller wirelessunmanned vehicle

Use web browser to access the lab remotely.The lab server can communicate with the UV wirelesslyPID controller gains adjustment through wireless communication.

WEB browser LAB server

Unmannedvehicle

WEB cam

LAB server

WEB browser

Communicationnetwork

netmeeting


System health monitoring

Invasive vs. non-invasive methodsModel-Based Paradigms

(Less suitable for system with highnonlinearity)

– Parameter estimation– Kalman filter

Model-Free Paradigms(More suitable for many real

world power engineering application)

– DSP– Expert system– Artificial Intelligence– Neural Networks– Fuzzy Logic– Hybrid Neural/Fuzzy Fault

Detection System

Fault Management

Module

Knowledge Engineering

(e.g.: Neural Network and Fuzzy Logic Technologies)

+

Domain Expertise(e.g. Shuttle Experts)


Challenges

Millions and millions of dataData stream constantly flowing into the database from sensorsWorking environments are dynamics have many unexpected eventsNeed to make proper decision on Fault Detection, Diagnosis, Prognosis, {D,D,P} and Mitigation Control Actions in real timeNeed tools to:

– Provide meaningful information {D,D,P} by mining the million andmillion of data

– Able to react properly to unseen data of the dynamics operating environments


Hybrid neural/fuzzy fault detection system

Pure NN Approach (Disadvantages)

– "Black-box" characteristics– Does not give heuristic

reasoningPure FZ Approach (Disadvantages)

– Difficult to Give an Exact Solution to the Problems

Hybrid NN/FZ System (Synergy)– Give Exact Solution

by Training Data

– Provide Valuable Information of the Fault Detection in a Heuristic Manner

GOOD FAIR BAD

TachometerMOTOR

ωΙΙ

Current Sensor

ω

Module 2

Module 1


Immune system inspired {D,D,P} technologies

Natural immune system has distinguished features such as:

– Memory and learning;– Self/non-self discrimination;– Responding to unknown patterns.

These features form the bases of a new emerging research field, Artificial Immune Systems (AIS), and have been used in application areas such as:

– Anomaly/Fault Detection and Diagnosis;– Control;– Robotics.

What is our objective?Data miningIncorporate with NN/FZ system for {D,D,P}


Artificial immune systems for motor fault detection, diagnosis, and prognosis

What is our objective?– Many motor incipient fault detection schemes are insufficient to discover and

identify new faults.– Our objective is to investigate the feasibility of developing an Intelligent Fault

Detection, Diagnosis and Prognosis System using Artificial Immune Systems on top of a Neural Network – Fuzzy Logic (NN-FZ) structure to actively detect, diagnose motor incipient faults and make an estimation of the remaining lifetime.

AIS algorithm

Neural-FuzzyIncipient faultdetection and

diagnosis

Motor systemmeasurement,

waveformanalyses and

featureextraction

Estimatedmotor

condition

Actual motorcondition (verified

at a later time)

Environmentalinformation

Motor faultdatabase


Biologically inspired intelligent fault diagnosis for power distribution systems

Distribution fault database(knowledge base)

(including district, spatialinformation, etc.)

Web geographicinformation

woodedarea

mall

house

Web weather channelgeographic weather

conditions

including protection devicestatus (e.g. fuse) and/orfault recorder waveforms

on-line distribution systemfault alarms

data miningand featureextraction

featureextraction

featureextraction

waveformanalyses

and featureextraction

Neural / Fuzzyfault root

causeidentification

estimateddistributionfault causes

actual faultcause (found

and verified at alater time)

update databasewith new data

AIS algorithmcontinuously guide

the NN-FZ system tolearn and absorb

new information toimprove its

performance


ADAC lab and research projects

Mechatronics and Control– Distributed Network Based Control and Applications– Distributed Network-Based Mobile Robots

(Unmanned Vehicles) control with Network QoS(Quality of Service) Constraints

– Adaptive Fuzzy Modulation for Network-Based PI Control (Completed)

Distance LearningDelta Project: Web-Based Remote Access Real-Time Mechatronics Laboratory Development

MotorsIntelligent Fault Detection Diagnosis and Prognosis of Induction MotorsDC Motor Acoustic Noise Analysis using Signal Processing TechniquesFast Prototype Motor System Simulation (MotorSIMII)A Neural/Fuzzy Approach for Motor Incipient Fault Detection (Completed)Incipient Fault Detection of Rotating Machines Using Neural Networks (Completed)

http://www.adac.ncsu.edu/NB_QoS/NB_QoS.html




http://www.adac.ncsu.edu/Adap_Fuzz_Mod/naif.htm

http://www.adac.ncsu.edu/DELTA/delta.html

http://www.adac.ncsu.edu/IFD_IM/IFD_IM.html

http://www.adac.ncsu.edu/dcmotor_noise/dcmotor_noise.htm


ADAC Lab and research projects – cont.

Power Distribution SystemsBiologically Inspired Intelligent Fault Diagnosis for Power Distribution SystemsPower Distribution Fault Location and Diagnosis (Completed)Distribution System Load Management (Completed) Power Quality Assessment of PowerDistribution Systems (Completed) Intelligent Energy Control (Completed)

NetworkingProactive Intelligent Network Fault Management (Completed)A Novel Set Theoretic Based Neural/Fuzzy Network Traffic Feature Extraction and Modeling Methodology (Completed)Communication System Network Control Software Performance Modeling and Fault Detection (Completed)

http://www.adac.ncsu.edu/power_distr/FaultDiagnosisPrognosis_PowerDistributionSystem.htm

http://www.adac.ncsu.edu/power_distr/FaultDiagnosisPrognosis_PowerDistributionSystem.htm

http://www.adac.ncsu.edu/Nortel_PIFM/Nortel_PIFM.html

Teleoperation and System Health Monitoring · 2003-10-07 · Teleoperation and System Health...

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Transcript of Teleoperation and System Health Monitoring · 2003-10-07 · Teleoperation and System Health...