The primary goal of Army Prognostics & Diagnostics is to develop real-time state awareness technologies for primary
structural components. In fatigue-critical structural maintenance, the probabilistic structural risk assessment (PSRA)
methodology for fatigue life management using conventional nondestructive investigation (NDI) has been developed
based on the assumption of independent inspection outcomes. When using the emerging structural health monitoring
(SHM) systems with in situ sensors, however, the independent assumption no longer holds, and the existing PSRA
methodology must be modified. The major issues currently under investigation are how to properly address the
correlated inspection outcomes from the same sensors on the same components and how to quantify its effect in the
SHM-based PSRA framework. This paper describes a new SHM-based PSRA framework with a proper modeling of
correlations among multiple inspection outcomes of the same structural component. The framework and the associated
probabilistic algorithms are based on the principles of fatigue damage progression, NDI reliability assessment and
structural reliability methods. The core of this framework is an innovative, computationally efficient, probabilistic
method RPI (Recursive Probability Integration) for damage tolerance and risk-based maintenance planning. RPI can
incorporate a wide range of uncertainties including material properties, repair quality, crack growth related parameters,
loads, and probability of detection. The RPI algorithm for SHM application is derived in detail. The effects of
correlation strength and inspection frequency on the overall probability of missing all detections are also studied and
discussed.
There are emerging sensor technologies that will be deployed in future rotorcraft or retrofitted to existing rotorcraft and
aircraft for structural diagnostics and prognostics. The vehicle health management system is likely to contain heterogeneous sensor
arrays. Thus the structural state awareness may require information data fusion from dissimilar sensor (heterogeneous) system. This
paper reviews the state of the art commercial of the shelf (COTS) and emerging sensor technologies for structural damage monitoring
of rotorcraft and aircraft health.
The paper presents a method of quantifying the mode conversion of Lamb waves within a 1D structure from a
notch-like asymmetric damage using both in-plane and out-of-plane velocity/displacement measurements. The
method is applied to data recorded from a Scanning Laser Doppler Vibrometer, and likewise to numerical studies
from a plane strain finite element model. A filtering procedure is implemented, and the reflected, converted,
and transmitted waves are separated in the frequency/wavenumber domain, and then integrated spatially in the
space/frequency domain. An accurate experimental technique for capturing the multiple components from a
single laser head is verified. Based on an initial in-plane excitation, the spatially-integrated multiple component
mode conversion coefficients are shown to mitigate experimental noise compared to single component mode
conversion coefficients.
Structural health monitoring (SHM) systems often rely on propagating elastic waves through complex structures, which can
result in the formation of diffuse-fields. Diffuse fields fill the whole structure with energy and are characterized by energy
equi-partition among all propagation modes. Due to their apparent complexity, diffuse-fields are not commonly used by
conventional SHM systems. However, recent theoretical and experimental studies have demonstrated that the local Green's
functions (GF) can be estimated from the cross-correlation of diffuse wavefields recorded between points of a sensor grid and
generated by sources located remotely from the monitoring area. The Diffuse Field Interferometry (DFI) concept yields the
GF between all measured points (e.g. nominal response of the structure), effectively transforming each measurement point
into a virtual source. The resulting local GFs provide detailed information on the dynamic behavior of the material/structure
under investigation. In this work, Green's functions are estimated experimentally from DFI using full-field measurements
obtained with a scanning laser vibrometer.
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