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.

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.

Progress in development of the physics model based diagnostic and prognostic system for solid rocket motors (SRMs) of the new generation of the crew exploration vehicles is reported. The performance model (PM) of the internal ballistics of large segmented SRMs in the regime of steady burning in the presence of the case breach fault is presented. This model takes into account propellant regression, erosive burning, surface friction, nozzle ablation, and also processes describing specific faults.

This paper presents a methodology to quantify the uncertainty in fatigue damage prognosis, applied to structures with complicated geometry and subjected to variable amplitude multi-axial loading. The crack growth analysis uses the concept of equivalent initial flaw size to replace small crack growth calculations and make use of a long crack growth model. A Gaussian process surrogate model, trained by a few finite element runs, is used to calculate the stress intensity factor used in crack growth calculation, as a function of crack size and loading.

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.

Actuator systems are employed widely in aerospace, transportation and industrial processes to provide power to critical loads, such as aircraft control surfaces. They must operate reliably and accurately in order for the vehicle / process to complete successfully its designated mission. Incipient actuator failure conditions may severely endanger the operational integrity of the vehicle / process and compromise its mission.

In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. Various uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data.

Core aspects of diagnosis and prognosis are based on the knowledge of the actual state-of-damage. Every mechanical system damage increases due to applied stresses. This contribution focuses on systems being affected by mechanical loads, leading to failure if a certain damage level is exceeded. According to the literature, mathematical models are known that describe qualitatively the damage progression based on experimental data. Hence, those models are valid for certain systems under certain operating conditions and depend on the underlying experimental data.