UNCLASSIFIED Structural Health Monitoring Systems Presented by: Thomas C. Null, PhD U.S. Army Aviation and Missile Research, Development, and Engineering Center Presented to: RAM 8 Distribution A: Approved for public release; distribution unlimited

UNCLASSIFIED 2 FileName.pptx UNCLASSIFIED SHM Technology Apache Tail Boom FEM Design Implementation Testing Detection Localization Future Work Overview

UNCLASSIFIED 3 FileName.pptx UNCLASSIFIED Structural Health Monitoring (SHM) promises to do the following: 1.Reduce Unnecessary Inspections – –By monitoring the structure maintainers can move away from usage based inspection and only perform them when damage in suspected. This removes a potential source of damage since a disassembly can often result in damage the structure (dents, scratches that break the corrosion barrier, etc.) 2.Increased asset availability –with less scheduled maintenance an asset is available for duty. 3.Reduced burden on the warfighter – An automated inspection process frees up a serviceman for other more important tasks 4.Increases safety –automated inspections reduces the risk of missing faults 5.Reduces costs –An automated SHM enables the prediction of when a component will fail. Maintainers with this knowledge can anticipate maintenance actions and reduces the amount of spares needed thus shortening the logistics chain. Another factor is the unscheduled maintenance is by far the most costly type in the Army. Just by reducing that will allow for a large savings. Structural Health Monitoring

UNCLASSIFIED 4 FileName.pptx UNCLASSIFIED Design a repeatable process to optimally perform 1.Detection –Is there a problem? 2.Localization –Where is the problem? 3.Classification (Characterization) –How bad is the problem? 4.Prognostication –How long before I need a repair? Our Goals

UNCLASSIFIED 5 FileName.pptx UNCLASSIFIED Framework MODELS & CONSTRAINTS SENSORS & ACTUATORS OPTIMIZATION SIMULATE SIGNAL PROCESSING METHOD ANALYZE

UNCLASSIFIED 6 FileName.pptx UNCLASSIFIED Design Process

UNCLASSIFIED 7 FileName.pptx UNCLASSIFIED Theoretical Development Physics-based model of the dynamical behavior of the structure(e.g., FEM, FDM; elastic, thermal) State-Space Representation for Dynamic Systems First order State-Space Representation with Accelerometers x = state vector u = excitation signals y = sensor measurements A = system matrix (dynamics) B = excitation influence C = observations (sensor locations)

UNCLASSIFIED 8 FileName.pptx UNCLASSIFIED FEM (Finite Element Model) –Data Drawing Package 3D Optometric Scanning Caliper/Micrometer –Tool Nastran V&V –Modal Analysis Output –Mass Matrix (M) –Stiffness Matrix (K) –And? S280 Physics Model

UNCLASSIFIED 9 FileName.pptx UNCLASSIFIED Design Process

UNCLASSIFIED 10 FileName.pptx UNCLASSIFIED Measurement: Output Power Covariance Sensitivity Steady State Response of the System: –Controlability Gramian Objective function for known damage location Lyapunov Optimal Sensor and Actuator Design

UNCLASSIFIED 11 FileName.pptx UNCLASSIFIED Unknown Damage Location –Problem: Too many objective functions –Solution: Global Sensitivity Controlability Gramian Observability Gramian Hankle –More computationally efficient Lyapunov Optimal Sensor and Actuator Design

UNCLASSIFIED 12 FileName.pptx UNCLASSIFIED Objective 1 –Maximize the minimum singular value of Hankle Objective 2 –Maximize sensitivity to mass change at known damage locations Objective 3 –Maximize sensitivity to stiffness change at known damage locations Objective 4 –Minimize sensitivity to manufacturing variations. Objective 5 –Minimize number of sensors Objective 6 –Minimize number of actuators Optimal Sensor and Actuator Design

UNCLASSIFIED 13 FileName.pptx UNCLASSIFIED Optimization –Obviously a variety of methods can be implemented –Genetic Algorithm was chosen Combinatorial problem Solutions scattered throughout design space Pareto Optimization Multi-objective Optimal Sensor and Actuator Design

UNCLASSIFIED 14 FileName.pptx UNCLASSIFIED Model / Constraints –FEM Sensors –Accelerometers Actuators –Piezoelectric Signal Processing –Detection- Change in covariance –Maximize Observability and Controlability –Maximize Sensitivity to “Hot Spots” –Minimize Sensitivity to Manufacturing Variation –Minimize Number of Sensor –Minimize Number of Actuators Use GA to Find Designs Apache Tail Boom

UNCLASSIFIED 15 FileName.pptx UNCLASSIFIED Design Process

UNCLASSIFIED 16 FileName.pptx UNCLASSIFIED Global Sensitivity (S280) –1 objective function, 40 designs –Clear shifts in objective function for increasing number of actuators –Hankle objective balances weighting in controllability and observability Evaluate Solutions

UNCLASSIFIED 17 FileName.pptx UNCLASSIFIED Stiffness Comparison –3 Designs, 20 Objective Functions –Normalized for comparison –Location 19 is the least sensitive for all three designs Evaluate Solutions

UNCLASSIFIED 18 FileName.pptx UNCLASSIFIED Mass Comparison –3 Designs (same as previous), 20 Objective Functions –Normalized for comparison –Design tradeoffs Design 1 is best for crack detection at location 19 but worst for corrosion detection at 19 Design 3 has the best overall corrosion detection, but the worst crack detection at location 19 Evaluate Solutions

UNCLASSIFIED 19 FileName.pptx UNCLASSIFIED Design Process

UNCLASSIFIED 20 FileName.pptx UNCLASSIFIED Designs Chosen –1 Actuator - 2 Sensor –2 Actuator - 4 Sensor Translate GA genes to FEM node IDs –Verify you can actually put a sensor/actuator at those locations. Implementation

UNCLASSIFIED 21 FileName.pptx UNCLASSIFIED Attach sensors/actuators Connect Data Acquisition Unit Implementation

UNCLASSIFIED 22 FileName.pptx UNCLASSIFIED Design Process

UNCLASSIFIED 23 FileName.pptx UNCLASSIFIED Damage Multiple sites –Stabilizer mount holes –Skin –Frame Ring Tool –Exacto Precision Razor Sizes (inches) –.02,.03,.04,.06,.08 –.16,.32,.64

UNCLASSIFIED 24 FileName.pptx UNCLASSIFIED Damage Detection Location 3 Design –1 Actuator –2 Sensors Key Takeaways –Metric increases as damage increases –Metric uses noise compensation –Detection for a 0.02 inch crack

UNCLASSIFIED 25 FileName.pptx UNCLASSIFIED Damage Localization Theory Dynamic Damage Localizing Vector (DDLV) –“Damage Localization from the Null Space of Changes in the Transfer Matrix” Bernal 2007 General Concepts –Change in transfer function can be found –Loads in the Null Space can be calculated –Applying those load to a model will produce no strain in regions of change –Approach is robust to model error because the excitation that locates the damage is a data driven feature Finds Where Damage is Not

UNCLASSIFIED 26 FileName.pptx UNCLASSIFIED Damage Localization Location 3 Design –2 Actuators –4 Sensors Key takeaways –99.9% of Structure does not require inspection

UNCLASSIFIED 27 FileName.pptx UNCLASSIFIED Location 10 Design –2 Actuators –4 Sensors Key takeaways –99.2% of Structure does not require inspection Damage Localization

UNCLASSIFIED 28 FileName.pptx UNCLASSIFIED Damage Localization Location 20 Design –2 Actuators –4 Sensors Key takeaways –96.4% of Structure does not require inspection

UNCLASSIFIED 29 FileName.pptx UNCLASSIFIED Characterization –Actively working on this with the AMCOM Corrosion Lab Prognostics (RUL) –Near future Future Work