Southwest Research Institute (SwRI) investigated the feasibility of integrating remote sensing technology with probability of failure analyses into a monitoring system capable of assessing the structural integrity of critical airframe components. The project demonstrated the viability of remote sensing to discern structural flaw growth along with the integration of sensor data with crack growth analyses in order to assess the health and integrity of a critical structural component.

A variety of rule-based, model-based and datadriven techniques have been proposed for detection and isolation of faults in physical systems. However, there have been few efforts to comparatively analyze the performance of these approaches on the same system under identical conditions. One reason for this was the lack of a standard framework to perform this comparison.

Next generation technology of integrated health management systems for air-transportation structures will combine different single SHM methods to an overall system with multiple abilities considering different stages of damage initiation and propagation. The fundamental configuration of the proposed SHM technique will involve the idea of an integrated passive/active monitoring and diagnostic system extended by numerical modules for lifetime prediction.

Structural health monitoring provides sensor data that monitor fatigue-induced damage growth in service. This information may in turn be used to improve the characterization of the material properties that govern damage propagation for the structure being monitored. These properties are often widely distributed between nominally identical structures because of differences in manufacturing processes and aging effects. The improved accuracy in damage growth characteristics allows more accurate prediction of the remaining useful life (RUL) of the structural component.

Situational awareness and decision-making are necessary to identify and select the optimal set of mutually non-exclusive hypothesis in order to maximize mission performance and adapt system behavior accordingly. This paper presents a hierarchical and decentralized approach for integrated damage assessment and trajectory planning in aircraft with uncertain navigational decision-making. Aircraft navigation can be safely accomplished by properly addressing the following: decision-making, obstacle perception, aircraft state estimation, and aircraft control.

Integrated System Health Management (ISHM) methodologies seek to enhance traditional component-level health management techniques to assess, diagnose, maintain, and prolong the health of complex target systems with large numbers of physical components that interact with each other in increasingly complex ways. Model-based reasoning solutions have the potential and demonstrate the promise to address some key challenges for ISHM.

This paper demonstrates the ability to design health monitoring systems from a systematic perspective and, with proper sensor and actuator placement, to detect and track damage occurring in a structure. The results from the first of three separate tests were previously presented showing the daily progression of damage until ultimate failure of the part under test. The tests were performed and the data were collected to emulate on-ground health monitoring scenarios. The data indicate the precursors to total structural failure significantly before the failure occurs.

Aircraft engine bearing prognosis not only requires early detection of a bearing defect, but also the ability to predict bearing health conditions given certain operational scenarios. This paper summarizes a physics-based remaining useful life prediction method developed in the DARPA engine system prognosis (ESP) program. This investigation focuses on a typical roller bearing fault (or defect) on the outer raceway. Spall detection is based on the fusion of vibration and online oil debris sensors.

Fatigue crack initiation and growth during the service of aging aircraft are important life-limiting phenomena. In a previous study, a risk prediction and reliability model for naval aircraft has been developed based on fracture mechanics and inspection field data. Despite significant achievements in the study of fatigue cracks using fracture mechanics, it is still of great interest to find practical techniques for monitoring the crack growth using non-destructive inspection and to integrate the inspection results with the fracture mechanics models to improve the predictions.

The integration of structural health monitoring (SHM) systems into in-service applications has been hindered by the implied infrastructure, specifically wires for power and data from each sensor an acquisition and/or processing unit. Prior research by the present investigators has demonstrated a patented method of point-of-measurement datalogging for SHM, thereby greatly reducing the required quantity of cable by locally converting analog signals into digital data that can be placed on a common sensor-bus.