The work presented in this paper is focused on monitoring fatigue crack growth in metallic structures using acoustic emission (AE) technology. Three different methods are proposed to utilize the information obtained from in-situ monitoring for structural health management.

Steam generator tube integrity is critical for the safety and operability of pressurized water reactors. Any degradation and rupture of tubes can have catastrophic consequences, e.g., release of radioactivity into the atmosphere. Given the risk significance of steam generator tube ruptures, it is necessary to periodically inspect the tubes using nondestructive evaluation methods to detect and characterize unknown existing defects.

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.

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.

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.

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.

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.

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.