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NASA NTRS1981

On-line structural parameter identification

Algorithms are presented for on-line parameter identification of structural dynamic systems. As an example, they are used to calculate the parameters of a modal model of a flexible beam. The algorithms are tested using hardware consisting of a 12 ft. beam with four voice coil actuators and nine noncontacting displacement sensors. They are programmed in a CDC Cyber 175 digital computer which provides input command signals for the actuators, reads the sensor data, and processes the algorithm to calculate consistent estimates of the modal parameters of the beam. Experimental results are compared with those of simulation analysis.

Thau, F. E.↗

NASA NTRS1984

Spacecraft structural system identification by modal test

A structural parameter estimation procedure using the measured natural frequencies and kinetic energy distribution as observers is proposed. The theoretical derivation of the estimation procedure is described and its constraints and limitations are explained. This procedure is applied to a large complex spacecraft structural system to identify the inertia matrix using modal test results. The inertia matrix is chosen after the stiffness matrix has been updated by the static test results.

Chen, J.-C.↗

NASA NTRS1969

Identification of structural systems by use of near-resonance testing

Identification of structural systems using near resonance testing

Howlett, J. T.↗

NASA NTRS1990

An AI-based approach to structural damage identification by modal analysis

Flexible-structure damage is presently addressed by a combined model- and parameter-identification approach which employs the AI methodologies of classification, heuristic search, and object-oriented model knowledge representation. The conditions for model-space search convergence to the best model are discussed in terms of search-tree organization and initial model parameter error. In the illustrative example of a truss structure presented, the use of both model and parameter identification is shown to lead to smaller parameter corrections than would be required by parameter identification alone.

Glass, B. J.↗

NASA NTRS1987

Identification of structural interface characteristics using component mode synthesis

The inability to adequately model connections has limited the ability to predict overall system dynamic response. Connections between structural components are often mechanically complex and difficult to accurately model analytically. Improved analytical models for connections are needed to improve system dynamic predictions. This study explores combining Component Mode synthesis methods for coupling structural components with Parameter Identification procedures for improving the analytical modeling of the connections. Improvements in the connection properties are computed in terms of physical parameters so the physical characteristics of the connections can be better understood, in addition to providing improved input for the system model. Two sample problems, one utilizing simulated data, the other using experimental data from a rotor dynamic test rig are presented.

Huckelbridge, A. A.↗

NASA NTRS1987

Identification of structural interface characteristics using component mode synthesis

Huckelbridge, A. A.↗

NASA NTRS1989

Identification of structural interface characteristics using component mode synthesis

Huckelbridge, A. A.↗

NASA NTRS2022

A Reinforcement Learning Hyper-Heuristic in Multi-Objective Optimization with Application to Structural Damage Identification

Multi-objective optimization allows satisfying multiple decision criteria concurrently, and generally yields multiple solutions. It has the potential to be applied to structural damage identification applications which are oftentimes under-determined. How to achieve high-quality solutions in terms of accuracy, diversity, and completeness is a challenging research subject. The solution techniques and parametric selections are believed to be problem specific. In this research, we formulate a reinforcement learning hyper-heuristic scheme to work coherently with the single-point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-seed). The four low-level heuristics proposed can meet various optimization requirements adaptively and autonomously using the domination amount, crowding distance, and hypervolume calculations. The new approach exhibits improved and more robust performance than AMOSA, NSGA-II, and MOEA/D when applied to benchmark test cases. It is then applied to an active damage interrogation scheme for structural damage identification where solution diversity/completeness and accuracy are critically important. Results show that this approach can successfully include the true damage scenario in the solution set identified. The outcome of this research can potentially be extended to a variety of applications.

Pei Cao↗

NASA NTRS2009

Dynamic Structural Fault Detection and Identification

Aircraft structures are designed to guarantee safety of flight in some required operational envelope. When the aircraft becomes structurally impaired, safety of flight may not be guaranteed within that previously safe operational envelope. In this case the safe operational envelope must be redefined in-flight and a means to prevent excursion from this new envelope must be implemented. A specific structural failure mode that may result in a reduced safe operating envelope, the exceedance of which could lead to catastrophic structural failure of the aircraft, will be addressed. The goal of the DFEAP program is the detection of this failure mode coupled with flight controls adaptation to limit critical loads in the damaged aircraft structure. The DFEAP program is working with an F/A-18 aircraft model. The composite wing skins are bonded to metallic spars in the wing substructure. Over time, it is possible that this bonding can deteriorate due to fatigue. In this case, the ability of the wing spar to transfer loading between the wing skins is reduced. This failure mode can translate to a reduced allowable compressive strain on the wing skin and could lead to catastrophic wing buckling if load limiting of the wing structure is not applied. The DFEAP program will make use of a simplified wing strain model for the healthy aircraft. The outputs of this model will be compared in real-time to onboard strain measurements at several locations on the aircraft wing. A damage condition is declared at a given location when the strain measurements differ sufficiently from the strain model. Parameter identification of the damaged structure wing strain parameters will be employed to provide load limiting control adaptation for the aircraft. This paper will discuss the simplified strain models used in the implementation and their interaction with the strain sensor measurements. Also discussed will be the damage detection and identification schemes employed and the means by which the damaged aircraft parameters will be used to provide load limiting that keeps the aircraft within the safe operational envelope.

Smith, Timothy↗

NASA NTRS1988

A Rayleigh-Ritz approach to structural parameter identification

This paper is concerned with the identification of parameter distributions in large space structures. The formulation is based on a Rayleigh-Ritz type approach working with the actual displacement at a given number of points in the structure. The parameter distributions are expanded in terms of known admissible functions multiplied by unknown coefficients, and the identification process reduces to the determination of these coefficients. The procedure uses a perturbation approach, beginning with a postulated set of parameters and iterating to the actual values in an incremental fashion.

Meirovitch, L.↗

NASA NTRS1990

Recent literature on structural modeling, identification, and analysis

The literature on the mathematical modeling of large space structures is first reviewed, with attention given to continuum models, model order reduction, substructuring, and computational techniques. System identification and mode verification are then discussed with reference to the verification of mathematical models of large space structures. In connection with analysis, the paper surveys recent research on eigensolvers and dynamic response solvers for large-order finite-element-based models.

Craig, Roy R., Jr.↗

NASA NTRS1983

Identification of structural systems using naturally induced vibration data in the presence of measurement noise

The method of instrument variables for the identification of the parameters of structural systems excited by white noise in the presence of white measurement noise is applied. The requisite equations and the resulting consistent estimator are derived and simulation results for least squares and instrumental variables methods, for light and heavy damping, are compared. The present method gave better results than least squares when measurement noise was presented, and verified the fact that least squares yields an inconsistent estimator unless the measurement noise is zero.

Huan, S.-L.↗

NASA NTRS1980

Adaptive/learning control of large space structures - System identification techniques

Techniques developed for the control of aircraft under changing operating conditions are used to develop a learning control system structure for a multi-configuration, flexible space vehicle. A configuration identification subsystem that is to be used with a learning algorithm and a memory and control process subsystem is developed. Adaptive gain adjustments can be achieved by this learning approach without prestoring of large blocks of parameter data and without dither signal inputs which will be suppressed during operations for which they are not compatible. The Space Shuttle Solar Electric Propulsion (SEP) experiment is used as a sample problem for the testing of adaptive/learning control system algorithms.

Thau, F. E.↗

NASA NTRS1993

The application of the Wigner Distribution to the identification of structure-borne noise components

In this paper it is shown that time-frequency analysis of a transient structural response may be used to identify the wave types carrying significant energy through a structure. The identification of various wave types is possible since each is characterized by its own dispersion relation, with the result that each wave type may be associated with characteristic features in the time-frequency domain representation of a structural response. In the work described here, the Wigner Distribution has been used to obtain time-frequency representations of structural transient responses. Practical aspects of the calculation of the Wigner Distribution are discussed briefly, and examples of its application to the analysis of transient structural responses are given. Additionally, time-frequency filtering techniques are described that can be used both to detect the presence of and to quantify the energy carried by both non-dispersive and dispersive waves.

Wahl, T. J.↗

NASA NTRS1990

Identification of structural-borne noise components by the use of time-frequency filtering

In this paper it is shown that a time-frequency analysis of a structural impulse response may be used to identify the wave types carrying significant energy through a structure. The identification of various wave types is possible since each is characterized by its own dispersion relation, with the result that each wave type may be associated with characteristic features in the time-frequency domain representation of an impulse response. In the work described here, the Wigner Distribution has been used to obtain appropriate time-frequency representations of impulse responses. Practical aspects of the calculation of the Wigner Distribution are discussed briefly and examples of its application to the analysis of structural impulse responses are given. Additionally, filtering techniques are described that can be used both to detect the presence of and to quantify the energy carried by both nondispersive and dispersive waves.

Wahl, T. J.↗

NASA NTRS1996

A Benchmark Problem for Development of Autonomous Structural Modal Identification

This paper summarizes modal identification results obtained using an autonomous version of the Eigensystem Realization Algorithm on a dynamically complex, laboratory structure. The benchmark problem uses 48 of 768 free-decay responses measured in a complete modal survey test. The true modal parameters of the structure are well known from two previous, independent investigations. Without user involvement, the autonomous data analysis identified 24 to 33 structural modes with good to excellent accuracy in 62 seconds of CPU time (on a DEC Alpha 4000 computer). The modal identification technique described in the paper is the baseline algorithm for NASA's Autonomous Dynamics Determination (ADD) experiment scheduled to fly on International Space Station assembly flights in 1997-1999.

Pappa, Richard S.↗

NASA NTRS1985

Structural parameter identification of distributed systems using finite element approximation

A system identification technique is developed for classes of distributed systems using finite element approximations. Vibrating systems represented by partial differential equations have physical parameters associated with mass, stiffness, and damping distributions which need to be known in order to properly control and design mathematical models of the system. In order to identify these parameters a weighted least-squares algorithm and modified Newton-Raphson method is used for the identification process. The theory and technique is demonstrated by estimating the system parameters of a vibrating cantilever beam made up of several different structural properties.

Lee, K. Y.↗

NASA NTRS2012

Sensor-Only System Identification for Structural Health Monitoring of Advanced Aircraft

Environmental conditions, cyclic loading, and aging contribute to structural wear and degradation, and thus potentially catastrophic events. The challenge of health monitoring technology is to determine incipient changes accurately and efficiently. This project addresses this challenge by developing health monitoring techniques that depend only on sensor measurements. Since actively controlled excitation is not needed, sensor-to-sensor identification (S2SID) provides an in-flight diagnostic tool that exploits ambient excitation to provide advance warning of significant changes. S2SID can subsequently be followed up by ground testing to localize and quantify structural changes. The conceptual foundation of S2SID is the notion of a pseudo-transfer function, where one sensor is viewed as the pseudo-input and another is viewed as the pseudo-output, is approach is less restrictive than transmissibility identification and operational modal analysis since no assumption is made about the locations of the sensors relative to the excitation.

Kukreja, Sunil L.↗