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Active Interrogation of Helicopter Rotor Faults Using Trailing Edge Flap Actuation Patricia Stevens Doctoral Candidate Mechanical Engineering Penn State University Doctoral Dissertation Defense April 2, 2001

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2 Outline Background & Motivation Objectives of Work Modeling Approach Damage Identification Conclusions

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3 Documented Rotor System Problems CH-46 Sea Knight - Prior to Upgrade Inspections as often as every 8 hours of flight time for some rotor components AH-64 Apache - Early Blade Problems Original aluminum blades pitted by sand and disabled by hail Composite blades suffered from delamination Civil 1990-1996: 35 civil rotorcraft accidents were caused by rotor system failures

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4 What makes helicopter rotor damage detection so difficult? Centrifugal Stiffening Gyroscopic System Aerodynamic Loads Complex Components Inaccessible Locations Noisy Environment

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5 Previous work: Localized fault detection Acoustic Emission Schoess et al. (1997) –Passive Approach –Acoustic Emission sensor “listens” for crack propagation Wave Mechanics Lakshamanan & Pines (1997) & Purekar et al. (1998) –Active approach –Scattering of structural waves due to impedance changes Limitation: –Requires a priori knowledge of fault location Acoustic Emission Schoess et al. (1997) –Passive Approach –Acoustic Emission sensor “listens” for crack propagation Wave Mechanics Lakshamanan & Pines (1997) & Purekar et al. (1998) –Active approach –Scattering of structural waves due to impedance changes Limitation: –Requires a priori knowledge of fault location Ultrasonic sensor crack stress waves Acoustic Emission PZT actuator / sensor flaw scattered waves Wave Mechanics

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6 Previous work: Rotor Diagnostics using Fuselage Measurements Azzam & Andrew (1992, 1995) Ganguli, Chopra & Haas (1995-98) Passive generation of fixed frame loads Measurements relative blade position fuselage vibration Measurements in hover and forward flight Limitations: Limited detectability of small faults Neural net required to classify faults Forward flight condition measurements required Azzam & Andrew (1992, 1995) Ganguli, Chopra & Haas (1995-98) Passive generation of fixed frame loads Measurements relative blade position fuselage vibration Measurements in hover and forward flight Limitations: Limited detectability of small faults Neural net required to classify faults Forward flight condition measurements required Dissimilar blade model Seed fault Simulate response Measure tip displacement hub loads (vibs) Next flight condition Next fault Fault profile at each flight condition Train Neural Net

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7 Previous work: Application of Structural Damage Detection Kiddy & Pines (1997 - 1999) Applied Modal Based SDD Technique to rotor blade environment Modified Eigenstructure Assignment Technique to accommodate –Centrifugal Stiffening –Aerodynamic Damping Limitations –Sensitive to noise –Limited fault coverage –Measurability & actuation not assessed Kiddy & Pines (1997 - 1999) Applied Modal Based SDD Technique to rotor blade environment Modified Eigenstructure Assignment Technique to accommodate –Centrifugal Stiffening –Aerodynamic Damping Limitations –Sensitive to noise –Limited fault coverage –Measurability & actuation not assessed Will an active interrogation structural damage detection approach yield improved results?

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8 Next Generation Rotorcraft… Active Trailing Edge Flaps Installed for vibration and noise control Potential actuator for damage interrogation Composite Blade Assembly Bearingless Hub Tab Actuator Flap Actuator Active Control Flap, Noise and Vibration Trim Tab, In-Flight Tracking HH10 Airfoil Section BLADE CROSS-SECTION Tab Actuator Flap Actuator MD 900 blade with trailing edge flap

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9 Goal: Design and Evaluate the Active Interrogation Concept Interrogation signal Damage Evaluation Algorithms Blade Response Measured trailing-edge flap sensors

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10 Objectives Determine if active interrogation of rotor faults using trailing edge flap actuators is a viable concept. Develop active interrogation techniques appropriate for the rotor blade environment. Demonstrate effective damage evaluation in hover. Demonstrate damage evaluation in the presence of noise and modeling errors Evaluate limitations of the approach. Determine if active interrogation of rotor faults using trailing edge flap actuators is a viable concept. Develop active interrogation techniques appropriate for the rotor blade environment. Demonstrate effective damage evaluation in hover. Demonstrate damage evaluation in the presence of noise and modeling errors Evaluate limitations of the approach.

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11 Outline Background & Motivation Objectives of Work Modeling Approach Rotor Trailing Edge Flap Damage Damage Identification Conclusions

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12 Rotor Model - Bearingless Main Rotor Cantilever boundary condition N el = 10 Nodal Degrees of Freedom Flexbeam Pitch Link Stiffness Finite Element Approach –Flap, torsion –10 beam elements Hingeless rotor - cantilever boundary condition Dissimilar blades Aeroelastic rotor in hover Response via time integration Response measured at each node

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13 Trailing-Edge Flap Model Physical Description Size10% of rotor radius Location80-90% rotor radius Frequency0 - 50 Hz. Amplitudeup to +/- 5 deg (using +/- 2.5 deg) Lift120 lb/deg at 0 Hz 70 lb/deg at 50 Hz Moment25 ft-lb/deg Physical Description Size10% of rotor radius Location80-90% rotor radius Frequency0 - 50 Hz. Amplitudeup to +/- 5 deg (using +/- 2.5 deg) Lift120 lb/deg at 0 Hz 70 lb/deg at 50 Hz Moment25 ft-lb/deg Aerodynamic Environment Mach No. 0.45 - 0.6 in hover Reduced Frequency up to 0.5 (k= c/2V) Requires subsonic compressible flow unsteady aerodynamic model (Leishman, et al) Aerodynamic Environment Mach No. 0.45 - 0.6 in hover Reduced Frequency up to 0.5 (k= c/2V) Requires subsonic compressible flow unsteady aerodynamic model (Leishman, et al)

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14 Damage Models Flexbeam Degradation –Bending Stiffness –Torsional Stiffness Control System Stiffness Flexbeam Crack Outboard Stiffness Defect –Bending –Torsional Outboard Crack Ballistic Damage Trim Mass Flexbeam Degradation –Bending Stiffness –Torsional Stiffness Control System Stiffness Flexbeam Crack Outboard Stiffness Defect –Bending –Torsional Outboard Crack Ballistic Damage Trim Mass

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15 Flexbeam Degradation Distributed stiffness fault Change in EI or GJ over flexbeam element 5% reduction in EI or GJ for 0.0-0.1R (flexbeam element)

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16 Control System Stiffness Crack in pitch rod or fatigue failure in connecting hardware 5% reduction in axial stiffness of pitch rod 5% effective reduction in torsional spring at end of flexbeam

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17 Outboard Stiffness Defect Adopted from Ganguli, Chopra and Haas (1995-98) Manufacturing Defect Delamination 5% reduction in EI or GJ for 0.6-0.7R

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18 Ballistic Damage Experimental study of effects of ballistic damage (Robinson & Leishman, 97-98) Ballistic damage affects: –C l , C lmax, C d –aerodynamic center location –mass “In some cases significant damage produced surprisingly mild effect on the aerodynamics” “Mild decreases in lift, but major increases in drag” Ballistic Damage = 5% decrease in mass from 0.6-0.7R

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19 Loss of Trim Mass Trim Mass mass nominal mass feather axis x/L el L el Discrete change in mass of 0.6 lb at 95% radius

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20 Crack Model - a new finite element Krawczuk et al. (2000) Boundary Conditions a H A A CRACK c b =1/k b III I w 1 (x) 1 (x) w 2 (x) 2 (x) LBLB L x=0 x=L B x=L q2q2 q1q1 q4q4 q3q3 From moment equilibrium

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21 Crack Model - a new finite element Converges to standard beam element as K 0 Only bending slope terms are affected Krawczuk et al. (2000) L B =L/2

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22 Elastic Crack Model - Relating Crack Depth to Crack Constant Correction function, F(a/H), takes into account crack and body geometry (from stress intensity factor): Correction function governs flexibility (elastic crack) Flexibility determines constant, K Effect of depth on crack constant K/H a/H

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23 Crack Model - Validation [reproduced from Krawczuk et al. (2000)]

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24 Outline Background & Motivation Objectives of Work Modeling Approach Damage Identification Theory & Results Effect of Modeling Errors Noise & Noise Mitigation Alternate Extent Quantification Approach Measurability Conclusions

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25 Structural Damage Detection Background Four Levels of Damage Identification –Level 1: Detection –Level 2a: Level 1 + Location –Level 2b: Level 1 + Characterization –Level 3:Level 2 + Quantification of Severity –Level 4: Level 3 + Prediction of Remaining Life (Modified from Rytter, 1993) Can I safely complete my mission?

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26 Structural Damage Detection Background Four Levels of Damage Identification –Level 1: Detection –Level 2a: Level 1 + Location –Level 2b: Level 1 + Characterization –Level 3:Level 2 + Quantification of Severity –Level 4: Level 3 + Prediction of Remaining Life There’s a problem!

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27 Structural Damage Detection Background Four Levels of Damage Identification –Level 1: Detection –Level 2a: Level 1 + Location –Level 2b: Level 1 + Characterization –Level 3:Level 2 + Quantification of Severity –Level 4: Level 3 + Prediction of Remaining Life...in the pitch link!

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28 Structural Damage Detection Background Four Levels of Damage Identification –Level 1: Detection –Level 2a: Level 1 + Location –Level 2b: Level 1 + Characterization –Level 3:Level 2 + Quantification of Severity –Level 4: Level 3 + Prediction of Remaining Life It’s a crack!

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29 Structural Damage Detection Background Four Levels of Damage Identification –Level 1: Detection –Level 2a: Level 1 + Location –Level 2b: Level 1 + Characterization –Level 3:Level 2 + Quantification of Severity –Level 4: Level 3 + Prediction of Remaining Life It’s a small crack.

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30 Structural Damage Detection Background Four Levels of Damage Identification –Level 1: Detection –Level 2a: Level 1 + Location –Level 2b: Level 1 + Characterization –Level 3:Level 2 + Quantification of Severity –Level 4: Level 3 + Prediction of Remaining Life Safe to complete the mission!

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31 Damage Detection, Location & Characterization The "DAMAGE VECTOR" EOM of damaged system: Rearranging results in two equivalent vector expressions -- d(jw) = the Residual Force or “Damage Vector” (1) (2) d(jw) has non-zero elements only at DOFs associated with damage d(jw) can be calculated from known parameters Damage is perturbation matrix:

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32 Interpretation of the Damage Vector Physical interpretation: The harmonic amplitude of nodal forces required to force the healthy system model to respond as if damage were present 1,23,45,67,89,10 f int 1,23,45,67,89,10 f int healthy damaged degrees of freedom: measurements: d3d3 d4d4 d5d5 d6d6 Ojalvo & Pilon (1988)

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33 Results for... Flexbeam Degradation –Torsional Stiffness Control System Stiffness Outboard Stiffness Defect –Bending Stiffness Outboard Crack Ballistic Damage Flexbeam Degradation –Torsional Stiffness Control System Stiffness Outboard Stiffness Defect –Bending Stiffness Outboard Crack Ballistic Damage Need to detect & locate differentiate between similar faults Does interrogation frequency affect the results?

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Damage Vector for Flexbeam Torsional Stiffness measurement location displacement w bending slope w' mid-node twist M end-node twist A Damage is 5% decrease in GJ of element 1 50 Hz 10 Hz Torsional stiffness damage manifests as damage vector DOFs connected to damaged element

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Damage Vector for Pitch Link Stiffness measurement location Damage is 5% decrease in torsional spring representing pitch link 50 Hz 10 Hz displacement w bending slope w' mid-node twist M end-node twist A Pitch link stiffness damage manifests as damage vector DOF connected to damaged element -- a single DOF

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Damage Vector for Outboard Bending Stiffness measurement location Damage is 5% decrease in EI of element 7 50 Hz 10 Hz displacement w bending slope w' mid-node twist M end-node twist A Outboard bending stiffness damage manifests as damage vector w & w’ DOFs connected to damaged element

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Damage Vector for Outboard Bending Crack measurement location Damage is crack of depth a/H=0.05 at midpoint of element #7 50 Hz 10 Hz displacement w bending slope w' mid-node twist M end-node twist A Crack damage manifests as damage vector w' DOFs connected to damaged element

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Damage Vector for Ballistic Damage measurement location Damage is 5% decrease in mass of element 7 50 Hz 10 Hz displacement w bending slope w' mid-node twist M end-node twist A Ballisitic damage manifests as damage vector w, w’, and DOFs connected to damaged element

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Damage Vector for Ballistic Damage measurement location Damage is 5% decrease in mass of element 7 50 Hz 10 Hz displacement w bending slope w' mid-node twist M end-node twist A Why is damage vector contaminated? Centrifugal Stiffening

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Damage Vector for Compound Damage measurement location Damage is –Root bending stiffness –Pitch link stiffness –Ballistic damage Results show –Each damage type is identified –Combined damage vector is equal to sum of individual damage vectors 50 Hz 10 Hz displacement w bending slope w' mid-node twist M end-node twist A

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41 Damage Detection, Location & Characterization Summary Residual force vector (a.k.a. damage vector) requires –refined model of healthy system –measured response of damaged system –model or measurement of external force All fault types studied were detected and located via residual force vector Similar faults are distinguishable Compound fault damage vector = sum of individual damage vectors No clear frequency recommendation Requires a single interrogation frequency Residual force vector (a.k.a. damage vector) requires –refined model of healthy system –measured response of damaged system –model or measurement of external force All fault types studied were detected and located via residual force vector Similar faults are distinguishable Compound fault damage vector = sum of individual damage vectors No clear frequency recommendation Requires a single interrogation frequency

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42 Why are rotor system damage extent calculations difficult? Aerodynamic Loads –Non-symmetric aerodynamic matrices –Damping Centrifugal Stiffening –large CF stiffening –mass / stiffness coupling Coriolis Forces –Skew symmetric matrices

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43 Damage Extent for Gyroscopic Systems Yap and Zimmerman (1999) solved the gyroscopic problem via the “Asymmetric Minimum Rank Perturbation Theory” –Modal based model update –Find the perturbation matrix of minimum rank subject to constraint of null symmetry This modal analysis based approach was extended to a FRF based approach as part of the current work Yap and Zimmerman (1999) solved the gyroscopic problem via the “Asymmetric Minimum Rank Perturbation Theory” –Modal based model update –Find the perturbation matrix of minimum rank subject to constraint of null symmetry This modal analysis based approach was extended to a FRF based approach as part of the current work

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44 Damage Extent (step 2) FRF -"Asymmetric Minimum Rank Perturbation Theory” Stiffness damage: Damping damage: Mass damage: Where [ B ]=matrix collection of damage vectors (step 1) = [ d 1, d 2, …, d p ] [ j int ]= diagonal matrix of interrogation frequencies [ X ]= matrix collection of damaged system response = [ {X(j 1 )}, {X(j 2 )}, …{X(j p )} ] The number of independent columns of [ B ] and [ X ] is equal to the rank of the perturbation matrix (e.g. flap only: mass=4, stiffness=2) BUT! Must know nature (mass, damping, stiffness) a priori.

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45 Calculation of Parameter Change AMRPT results in perturbation matrix of full dimension Non-zero terms describe change in elemental matrix For damage located in a single element, change in physical parameter is calculated using structure of elemental matrix e.g. AMRPT results in perturbation matrix of full dimension Non-zero terms describe change in elemental matrix For damage located in a single element, change in physical parameter is calculated using structure of elemental matrix e.g. 5 10 15 20 25 30 35 40 5 10 15 20 25 30 35 40 Exact K DOF x 10 4 1 0 -2

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46 Mass Damage in Rotating Structure Observations: Off diagonal terms in mass and CF stiffness matrices depend on c.g. offset - typically small CF affects inboard elements in flapwise motion only Neglecting off-diagonal terms, problem is now (3 x 3) in twist Solve problem using twist DOFs only - still coupled in mass & stiffness Solution: Iterate on coupled twist problem

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47 Damage Extent Summary AMRPT Damage Extent Quantification Error AMRPT results show improvement using higher interrogation frequencies where x is damaged parameter (EI, GJ, A ) Errors stem from small errors in damage vector

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48 Outline Background & Motivation Objectives of Work Modeling Approach Damage Identification Theory & Results Effect of Modeling Errors Noise & Noise Mitigation Alternate Extent Quantification Approach Measurability Conclusions

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49 Effect of Modeling Errors 10%modeling error no error Model Error:10% stiffness error in baseline model Damage: 5% outboard bending stiffness Damage Detection Destroyed!!

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50 Correction of Modeling Errors no correction corrected d no error Model Error:10% stiffness error in baseline model Damage: 5% outboard bending stiffness Interrogation:+/- 2.5 deg., = 40 Hz Use damage vector correction: d=d d -d h

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51 Effect of Modeling Errors on Damage Extent Calculations Damage: 5% outboard bending stiffness Interrogation:+/- 2.5 deg., = 32 & 40 Hz Case 10% Increase in Baseline Stiffness 10% Decrease in Baseline Mass % Error 45.4 -17.22 AMRPT Extent Quantification Error Extent quantification error for perfect model = 0.04% Damage vector correction d=d d -d h is utilized Small errors in damage vector result in large errors in frequency domain AMRPT

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52 Outline Background & Motivation Objectives of Work Modeling Approach Damage Identification Theory & Results Effect of Modeling Errors Noise & Noise Mitigation Alternate Extent Quantification Approach Measurability Conclusions

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53 Noise How does measurement noise affect the results? How can noise effects be reduced? How does measurement noise affect the results? How can noise effects be reduced? Uniform Random Noise in Harmonic Signal 5% 2% 10%

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54 Effect of Noise on Damage Vector Noise:uniform random noise Damage: 5% outboard bending stiffness Interrogation:+/- 2.5 deg., = 40 Hz Damage Vector Displacement DOFs Damage Vector Rotation DOFs Damage Vector Magnitude 5% noise 2% noise no error

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55 Noise Mitigation Procedure Number of Cycles in Average 10% uniform random noise 5% uniform random noise 2% uniform random noise 1% uniform random noise % RMS Noise Cycle Averaging of Harmonic Signal with Noise

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56 Benefits of Cycle Averaging Damage Vector Displacement DOFs Damage Vector Rotation DOFs Noise:5% uniform random noise Damage: 5% outboard bending stiffness Interrogation:+/- 2.5 deg., = 40 Hz Damage Vector Magnitude Threshold = 1.8 Threshold = 2.5 5% noise 40 cycle average no error

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57 Effect of Noise on Damage Extent Calculations Damage: 5% outboard bending stiffness Interrogation:+/- 2.5 deg., = 32 & 40 Hz Case 2% noise 20 cycles 5% noise 40 cycles % Error 114% 130% AMRPT Extent Quantification Error Extent quantification error for perfect model = 0.04% Small errors in damage vector result in large errors in frequency domain AMRPT

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58 Outline Background & Motivation Objectives of Work Modeling Approach Damage Identification Theory & Results Effect of Modeling Errors Noise & Noise Mitigation Alternate Extent Quantification Approach Measurability Conclusions

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59 Damage Vector Sensitivity Approach Motivation –Damage vector is clean & reliable –AMRPT very susceptible small errors in damage vector –AMRPT only applicable for null symmetric systems –Aerodynamic damage is non-symmetric Does magnitude of damage vector indicate damage severity?

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60 Damage Vector Sensitivity Approach Damage Vector Magnitude vs. Damage Extent Outboard Bending Stiffness Fault Damage Vector Magnitude Simple relationship relates damage severity to damage vector magnitude Nearly linear for small damage

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61 Outline Background & Motivation Objectives of Work Modeling Approach Damage Identification Theory & Results Effect of Modeling Errors Noise & Noise Mitigation Alternate Extent Quantification Approach Measurability Conclusions

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62 Measurability Sensitivity –How large is the response of the healthy system at sensor locations? Resolution –How does damage change the magnitude of response? Sensitivity –How large is the response of the healthy system at sensor locations? Resolution –How does damage change the magnitude of response?

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63 Measurability: Sensitivity Direct Measurements –Displacement < 0.25" –Rotation < 0.25 deg. –Twist < 1 deg. Strain Measurements –Bending Strain < 250 -strain –Shear Strain < 60 -strain Direct Measurements –Displacement < 0.25" –Rotation < 0.25 deg. –Twist < 1 deg. Strain Measurements –Bending Strain < 250 -strain –Shear Strain < 60 -strain Frequency averaged 10-50 Hz, 2 Hz step. Peak-peak harmonic response amplitudes

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64 Measurability: Change in Direct Measurement Response root bending stiffness root torsional stiffness root crack a/H=0.05 root crack a/H=0.2 pitch link outboard bending stiffness outboard torsional stiffness outboard crack a/H=0.05 outboard crack a/H=0.2 ballistic damage trim mass * Results averaged over frequency and blade length

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65 Outline Background & Motivation Objectives of Work Modeling Approach Damage Identification Conclusions

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66 Recommendations & Conclusions Summary –A unique active-interrogation damage evaluation approach for helicopter rotor systems using trailing-edge flap actuation has been designed and implemented in a numerical rotor code. –Residual force vector and AMRPT adapted for active interrogation approach –Residual force vector sensitivity approach formulated as alternative extent quantification approach –Detection & Extent demonstrated in hover using trailing edge flap actuation within the bounds set by vibration control requirements (< 50 Hz, +/- 2.5 deg. deflection) –Effects of noise & modeling errors assessed and mitigated –Preliminary measurability study Summary –A unique active-interrogation damage evaluation approach for helicopter rotor systems using trailing-edge flap actuation has been designed and implemented in a numerical rotor code. –Residual force vector and AMRPT adapted for active interrogation approach –Residual force vector sensitivity approach formulated as alternative extent quantification approach –Detection & Extent demonstrated in hover using trailing edge flap actuation within the bounds set by vibration control requirements (< 50 Hz, +/- 2.5 deg. deflection) –Effects of noise & modeling errors assessed and mitigated –Preliminary measurability study

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67 Recommendations & Conclusions Successes –Damage detection very clean for mass and stiffness faults not sensitive to interrogation frequency faults detected & characterized in the presence of 5% noise with cycle averaging faults detected & characterized with 10% baseline model errors using damage vector correction –Damage extent measurement via AMRPT stiffness faults within 5% error (without noise or modeling errors) Successes –Damage detection very clean for mass and stiffness faults not sensitive to interrogation frequency faults detected & characterized in the presence of 5% noise with cycle averaging faults detected & characterized with 10% baseline model errors using damage vector correction –Damage extent measurement via AMRPT stiffness faults within 5% error (without noise or modeling errors)

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68 Recommendations & Conclusions Limitations –Damage extent very sensitive to noise & errors –AMRPT damage extent algorithm modified to account for CF stiffening effects BUT sensitivity to errors in damage vector is severe –AMRPT damage extent algorithm inappropriate for aerodynamic faults –Measurability Typical change in response = 1% Change in response << 1% for cracks, flexbeam torsional stiffness fault Limitations –Damage extent very sensitive to noise & errors –AMRPT damage extent algorithm modified to account for CF stiffening effects BUT sensitivity to errors in damage vector is severe –AMRPT damage extent algorithm inappropriate for aerodynamic faults –Measurability Typical change in response = 1% Change in response << 1% for cracks, flexbeam torsional stiffness fault

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69 Recommendations & Conclusions Remarks –Damage detection in helicopter main rotor using active interrogation with trailing edge flap is promising –Damage extent using frequency domain AMRPT is difficult due to sensitivity to errors in damage vectors Recommendations –Extent calculations using damage vector sensitivity –Optimize sensor placement –Optimize interrogation frequency –Implementation of strain-based approach –Investigate alternate detection and extent algorithms non-linear time series feature extraction (Todd et al, 2001) Remarks –Damage detection in helicopter main rotor using active interrogation with trailing edge flap is promising –Damage extent using frequency domain AMRPT is difficult due to sensitivity to errors in damage vectors Recommendations –Extent calculations using damage vector sensitivity –Optimize sensor placement –Optimize interrogation frequency –Implementation of strain-based approach –Investigate alternate detection and extent algorithms non-linear time series feature extraction (Todd et al, 2001)

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70 Acknowledgments This work was funded in part by –The ONR MURI in Integrated and Predictive Diagnostics through the Penn State Applied Research Lab –The ARO MURI in Active Noise and Vibration Control Technologies for Jet Smooth, Quiet Rotorcraft This work was funded in part by –The ONR MURI in Integrated and Predictive Diagnostics through the Penn State Applied Research Lab –The ARO MURI in Active Noise and Vibration Control Technologies for Jet Smooth, Quiet Rotorcraft

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