Fault Diagnosis based on Particle Filter

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www.cesos.ntnu.no Bo Zhao – Centre for Ships and Ocean Structures

Fault Diagnosis based on Particle Filter- with applications to marine crafts

Bo ZhaoCeSOS / Department of Marine Technology

Norwegian University of Science and Technology

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Danger and harmDanger and harm

PollutionPollution

Property lossProperty loss

Faults

SafetySafety

Environmental friendlyEnvironmental friendly

EconomyEconomy Purpose

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Data from: The Software Problem ++, Marine Cybernetics, 2004.

0

5

10

15

20

25

30

35

40

45

Incide

nt in

%

Year

Incident trends 1990 – 2001: Primary causes

Computer Reference Thruster

Operator Electrical Generator

Environment Other

Computer

Reference

Thruster

Operator

Electrical

Generator

Environment Other

Average incidents in percentage on primary causes

DP incident analysis

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• Two divers on the sea bed, were testing within a subsea structure.

22:10• A series of alarms activated in the vessels in relation to the DP system.

• Vessel started drifting away.

• The Divers started to leave the structure and head back to the diving bell.

• Diver 2 noticed his umbilical had been snagged.

22:12• Communications and video was lost to Diver 2.

• The DP operator were trying to control the vessel by manual operation.

22:17• Regained control of the vessel having drifted off approx. 240m.

• The vessel was driven manually by the master back to the structure.

22:40 ‐ 22:46• The vessel was back in a position close to the drilling template.

• Both Divers were on the bell stage. Diver 2 was recovered into the bell.

• Vessel:• Date:• Wind:• Sea State: • Drifted:

Bibby Topaz18/09/1230 knots, 316°5 240m in 7min

DP incident report:

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Principle:• System with faults• Particle filter• Diagnosis of faults

Applications: • Diagnosis of DP

position reference system• Underwater robot navigation

Content

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Principle

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time

• Hidden Markov model• State observer• Switching-mode HMM• Mode/state observer

• Kalman filter• Extended KF• Unscented KF• Particle filter

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…

time


projectile rebounds on ground

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Principle • Hidden Markov model• State observer• Switching-mode HMM• Mode/state observer

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…

time

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…

time

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…

…

time

• Kalman filter• Extended KF• Unscented KF• Particle filter


projectile rebounds on ground

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How do we diagnose a fault?

PredictedFault free behavior

PredictedFaulty behavior

Prediction

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How do we diagnose a fault?

PredictedFault free behavior

PredictedFaulty behavior

Prediction ObservationTake the measurement

Correction

H0

H1

Obs.

Compare

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Application toDiagnosis of DP position reference system

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Challenge: Complex external disturbance

Wave‐frequency motion Wind and current force

Nonlinear system behaviorModel uncertaintyMultiple failure modes

GPS• Drifting• Bias• Outliers

HPR• Random excursion• Outliers

GPS drifting

wave

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GPS driftingHPR in function

wave

Results: Alarm when faults happen Diagnosis faults Acceptable positioning during

failure

Pros: Avoids catastrophic consequences

by giving DPO time to handle faults Assists the DPO diagnosing faults

Cons: Relatively poor position estimation

comparing with other DP observer Time consuming

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Application toRobust Navigation of Underwater Robot

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x

y

z

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Tunnel thruster

2×Main thrusters

2×Vertical thrustersVertical: 1.2 knot

Yaw rate: 60°/s

x

y

z

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DVL (Dopple Velocity Log)depth sensor

HPR (Hydroacousticposition reference)

compassYaw rate gyro

x

y

z

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Kinetics&

Kinematics

Propulsion‐ reduction

DVL‐ dropout‐ bias

HPR‐ dropout‐ outliers

Current

Robust Navigation of Underwater RobotROV Model

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HPR – Hydro acoustic position reference

Faults: 1. Dropout – when no signal received2. Outlier – Measurement has

significant difference from the true position

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DVL – Doppler velocity log

Faults: 1. Dropout – when no signal received2. Bias – small‐size constant difference

between the measurement and the true velocity

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Kinetics&

Kinematics




Current

Robust Navigation of Underwater RobotROV Model

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Kinetics&

Kinematics




Current

SystemModel

MeasurementModels

Robust Navigation of Underwater RobotInformation flow

Predictions

Estimation and

Diagnosis

Estimationfrom last

sampling time

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Kinetics&

Kinematics

Propulsion‐ reduction DVL

‐

ut‐ bias

DVL‐dropout‐ bias


Current

Model Measurement

Robust Navigation of Underwater RobotExperiment

Estimationfrom time k‐1Estimation

from time k‐1

Prediction

Estimation and

Diagnosis

Full

Faults

Full‐scale test, ROV MinervaOctober 17‐18, 2012, Trondheimsfjord.

Real disturbance.Real measurement.

In the real‐time control loopFaults were triggered.

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Kinetics&

Kinematics


‐

ut‐ bias



Current

Model Measurement



from time k‐1

Prediction

Estimation and

Diagnosis

‐16m ‐2m

‐2m

2m

Full

Faults




measurmentestimationcommandfaultymeasurementKF estimation


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Kinetics&

Kinematics


‐

ut‐ bias



Current

Model Measurement



from time k‐1

Prediction

Estimation and

Diagnosis

‐16m ‐2m

‐2m

2m

NormalFull

Faults






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Kinetics&

Kinematics


‐

ut‐ bias



Current

Model Measurement



from time k‐1

Prediction

Estimation and

Diagnosis

‐16m ‐2m

‐2m

2m

HPR OutliersFull

Faults






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Kinetics&

Kinematics


‐

ut‐ bias



Current

Model Measurement



from time k‐1

Prediction

Estimation and

Diagnosis

‐16m ‐2m

‐2m

2mHPR drop

Full

Faults






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Experiment

HPR OutliersHPR DropoutDVL Dropout

HPR + DVL

Fault Free

DVL BiasThruster Loss

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Experiment


HPR + DVL

Fault Free




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Experiment


HPR + DVL

Fault Free




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Principle:• System with faults: SM‐HMM• Particle filter for fault diagnosis

Applications: • Diagnosis of DP position reference system• Underwater robot robust navigation

Future work:• Efficiency issue for particle filter in SM‐HMM• Formal design process for the mode transition• Transient after mode switching

Summary

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Reference:

Particle filter:

• Zhao, B.; Skjetne, R. & Blanke, M., Particle Filter and for Fault and Diagnosis and Robust Navigation of Underwater Robot, IEEE Transactions on control systems technology (Submitted), IEEE, 2013

Fault diagnosis (regarding marine crafts):

• Blanke, M.; Kinnaert, M.; Lunze, J. & Staroswiecki, M., Diagnosis and Fault‐Tolerant Control, Springer Berlin Heidelberg, 2006

• Blanke, M., Diagnosis and Fault‐Tolerant Control for Ship Station Keeping, Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, MediterreanConference on Control and Automation, 2005, 1379 ‐1384

Others

• The Software Problem ++, Marine Cybernetics, 2004.

Documents

Fault Diagnosis based on Particle Filter