When it comes to identifying model parameters such as damage growth parameters in Paris law for example, Bayesian inference is a popular method. However, it involves substantial computational cost, especially with increasing number of parameters. When the prior distribution for the parameters is not narrow, non-linear regression may provide almost all the benefits of Bayesian updating at a small fraction of the computational cost. In this paper we apply this approach to the identification of damage growth parameters.

Composites present additional challenges for inspection due to their heterogeneity and anisotropy, and since damage often occurs beneath their surface. Currently successful laboratory non-destructive methods, such as X-ray and C-scans, are impractical for inspection of large integrated structures. It is clear that new approaches for inspection of composites need to be developed. During the present research, multiple carbon nanotube (CNT) based NDE & SHM techniques were investigated to resolve these issues.

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.

The sensor signal of Lead Zirconate Titanate (PZT) piezoelectric sensors/actuator surface mounted to a structure with a thin adhesive layer
is known to be influenced by the bondline quality and integrity. Monitoring the bondline health of sensor/actuators integrated into structures is becoming a major concern to guarantee the success and reliability of Structural Health Management systems. The design of a carbon-nanotube-coated PZT (CPZT) sensor was

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.

Herein a general, multimechanism, physics-based viscoelastoplastic model is presented in the context of an integrated diagnosis and prognosis methodology which is proposed for structural health monitoring, with particular applicability to gas turbine engine structures. In this methodology, diagnostics and prognostics will be linked through state awareness variable(s).

Self-sealing or self-healing materials can be found in many applications, including automotive and aerospace, and remain a topic for current research. The nature of these materials allows damage to be repaired autonomously. This can improve safety and reliability, but also pose challenges for structural health monitoring and prognostics. One goal of structural health monitoring is to monitor the accumulation of minor damage and degradation in order to predict and prevent catastrophic failure.

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.