Automation & Robotics Research Institute (ARRI) The ... Automation & Robotics Research Institute (ARRI)

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  • F.L. Lewis, Assoc. Director for Research Moncrief-O’Donnell Endowed Chair

    Head, Controls, Sensors, MEMS Group Automation & Robotics Research Institute (ARRI)

    The University of Texas at Arlington

  • Automation & Robotics Research Institute (ARRI) The University of Texas at Arlington

    F.L. Lewis, Assoc. Director for Research Moncrief-O’Donnell Endowed Chair

    Head, Controls, Sensors, MEMS Group

    http://ARRI.uta.edu/acs

    CBM II - Diagnostics

  • Objectives Extend equipment lifetime Reduce down time Keep throughput and due dates on track Use minimum of maintenance personnel Maximum uptime for minimum effective maintenance costs CBM should be transparent to the user

    No extra maintenance for the CBM network! Determine the best time to do maintenance

    Efficiently use maintenance & repair resources Do not interfere with machine usage requirements

    Allow planning for maintenance costs No unexpected last-minute costs!

    Condition-Based Maintenance (CBM) Prognostics & Health Management (PHM)

  • The CBM/PHM Cycle

    Machine Sensors

    Pre- Processing

    Feature Extraction

    Fault Classification

    Prediction of Fault

    Evolution Data

    Schedule Required

    Maintenance

    Systems & Signal processing

    Diagnostics Prognostics MaintenanceScheduling

    Identify important features

    Fault Mode Analysis

    Machine legacy failure data

    Available resources RUL Mission due dates

    Required Background Studies

    Contact George Vachtsevanos gjv@ece.gatech.edu http://icsl.marc.gatech.edu

    PHMCBM

  • Off Line- Background Studies, Fault Mode Analysis On Line- Perform real-time Fault Monitoring & Diagnosis

    Two Phases of CBM Diagnostics

    Three Stages of CBM/PHM

    Diagnostics Prognostics Maintenance Scheduling

  • CBM – Fault Diagnosis Background Studies

    • Fault Mode Analysis (FMA) - Identify Failure and Fault Modes

    • Identify the best Features to track for effective diagnosis

    • Identify measured sensor outputs needed to compute the features

    • Build Fault Pattern Library

    Deal with FAULTS Need to identify Faults before they become Failures

    Phase I- Preliminary Off Line Studies

  • Compressor Pre-rotation Vane

    Condenser

    Evaporator

    •Compressor Stall & Surge •Shaft Seal Leakage •Oil Level High/Low •Aux. Pump Fail •Oil Cooler Fail •PRV/VGD Mechanical Failure

    •Condenser Tube Fouling •Condenser Water Control Valve Failure •Tube Leakage •Decreased Sea Water Flow

    •Target Flow Meter Failure •Decreased Chilled Water Flow •Evaporator Tube Freezing

    •Non Condensable Gas in Refrigerant •Contaminated Refrigerant •Refrigerant Charge High •Refrigerant Charge Low

    •SW in/out temp. •SW flow •Cond. press. •Cond. PD press. •Cond. liquid out temp.

    •Comp. suct. press./temp. •Comp. disch. press./temp. •Comp. oil press./flow (at required points) •Comp. bearing oil temp •Comp. suct. super-heat •Shaft seal interface temp. •PRV Position

    •Liquid line temp. •(Refrigerant weight)

    •CW in/out temp./flow •Eva. temp./press. •Eva. PD press.

    Ex. Ex. -- Navy Centrifugal Chiller Failure ModesNavy Centrifugal Chiller Failure Modes

    Fault Mode Analysis Contact George Vachtsevanosgjv@ece.gatech.edu http://icsl.marc.gatech.edu

  • Fault Modes of an Electro-Hydraulic Flight Actuator

    V. Skormin, 1994 SUNY Binghamton

    bearing control surface

    hydraulic cylinder

    pump

    power amplifier

    Fault Modes

    Control surface loss Excessive bearing friction

    Hydraulic system leakage Air in hydraulic system Excessive cylinder friction Malfunction of pump control valve

    Rotor mechanical damage Motor magnetism loss

    motor

    Fault Mode Analysis

  • Use Physics of Failure and Failure Models to select failure features to include in feature vectors

    Select Fault ID Feature Vector

    Method 1- Dynamical System Diagnostic Models

    The Fault Feature Vector is a sufficient statistic for identifying existing fault modes and conditions

    BJssT s

    + =

    1 )( )(ωmotor dynamics

    sBsMsF sX

    pp )( 1

    )( )(

    + =pump/piston dynamics

    LsK AsR

    sP

    + =

    )(

    1 )( )(

    2 actuator system dynamics

    Physical parameters are J, B, Mp, Bp, K, L

    V. Skormin, 1994 SUNY Binghamton

  • Select Feature Vector Relate physical parameters J, B, Mp, Bp, K, L to fault modes

    Get expert opinion (from manufacturer or from user group) Get actual fault/failure legacy data from recorded machine histories Or run system testbed under induced faults

    Result -

    Etc.Etc.

    THEN (fault is air in hydraulic system)IF (actuator stiffness K is small) AND (piston damping coeff. Bp is small)

    THEN (fault is excess cylinder friction)IF (motor damping coeff. B is large) AND (piston damping coeff. Bp is large)

    THEN (fault is hydraulic system leakage)IF (leakage coeff. L is large) Fault ModeCondition

    Therefore, select the physical parameters as the feature vector T

    pp LKBMBJt ][)( =φ

    V. Skormin, 1994 SUNY Binghamton

  • Select Sensors for the Best Outputs to Measure

    V. Skormin, 1994 SUNY Binghamton

    T pp LKBMBJt ][)( =φ

    Cannot directly measure the feature vector

    Can measure the inputs and outputs of the dynamical blocks, e.g.

    BJssT s

    + =

    1 )( )(ω

    )( 2

    )()( tPDtCItT π

    −= ω(t) motor speed

    armature current I(t)

    pressure difference P(t)

    Therefore, use system identification techniques to estimate the features

    Virtual Sensors = physical sensors + signal processing se

    ns or

    s DSP

    signals from machine

    Fault ID features

  • Method 2- Non-Model-Based Techniques

    Select Fault ID Feature Vector

    Etc.Etc.

    THEN (fault is worn outer ball bearing)IF (third harmonic of shaft speed is present) AND (kurtosis of load vibration is large)

    THEN (fault is gear tooth wear)IF (shaft vibration second mode is large) AND (motor vibration RMS value is large)

    THEN (fault is unbalance)IF (base mount vibration energy is large) Fault ModeCondition

    Therefore, include vibration moments and frequencies in the feature vector

    =)(tφ [ time signals … frequency signals ]T

    Get expert opinion (from manufacturer or from user group) Get actual fault/failure legacy data from recorded machine histories Or run system testbed under induced faults

  • Method 3- Statistical Regression Techniques

    Select Fault ID Feature Vector

    Vibration magnitude

    D riv

    e tra

    in g

    ea r t

    oo th

    w ea

    r

    Pearson’s correlation Nonlinear correlation techniques Multivariable regression

    Clustering techniques Neural networks Statistical

    Fault 1

    Fault 2

    Fault 3

    outliers

  • Etc.Etc.

    THEN (fault is air in hydraulic system)IF (actuator stiffness K is small) AND (piston damping coeff. Bp is small)

    THEN (fault is excess cylinder friction)IF (motor damping coeff. B is large) AND (piston damping coeff. Bp is large)

    THEN (fault is hydraulic system leakage)IF (leakage coeff. L is large) Fault ModeCondition

    Fault Pattern Library

    Etc.Etc.

    THEN (fault is worn outer ball bearing)IF (third harmonic of shaft speed is present) AND (kurtosis of load vibration is large)

    THEN (fault is gear tooth wear)IF (shaft vibration second mode is large) AND (motor vibration RMS value is large)

    THEN (fault is unbalance)IF (base mount vibration energy is large) Fault ModeCondition

    Fuzzy Logic Rulebase !

  • www.MIMOSA.org Machine User Group- CBM Data

  • Condition Monitoring and Diagnostics of Machines

  • CBM Fault DIAGNOSTICS Procedure

    machines

    Math models

    ),,( ),,(

    π π

    uxhy uxfx

    = =&

    System Identification- Kalman filter NN system ID

    RLS, LSE

    Dig. Signal Processing

    Physical Parameter estimates & Aero. coeff. estimates

    π̂

    Sensor outputs

    Vibration Moments, FFT

    Feature Vectors-

    Sufficient statistics

    )(tφ Fault Classification Feature patterns for faults Decision fusion could use:

    Fuzzy Logic Expert Systems NN classifier

    Stored Legacy Failure data Statistics analysis

    Feature extraction - determine inputs for Fault Classification

    Physics of failure System dynamics Physical params.

    Identify Faults/ Failures

    Set Decision Thresholds Manuf. variability data Usage variability Mission history Minimize Pr{false alarm} Baseline perf. requirements

    More info needed?

    Inject probe test signals for refined diagnosis Inform pilotyes

    π

    Serious?

    Inform pilot

    yes

    Sensing Fault Feature Extraction

    Reasoning & Diagnosis

    Systems, DSP & Data Fusion

    Sensor Fusion

    Feature vectors

    Feature fusion

    Stored Fault Pattern

    Library

    Model-Based Diagnosis

    Phase II- On Line Fault Monitoring and Diagnostics