The need for guidance on integrating structural health monitoring within military aircraft systems

Gaussian mixture model–based structural health monitoring methods have been studied in recent years to improve the reliability of damage monitoring under environmental and operational conditions. However, most of these methods only use the ordinary expectation maximization algorithm to construct the Gaussian mixture model but the expectation maximization algorithm can easily lead to a local optimal solution and a singular solution, which also results in unreliable and unstable damage monitoring especially for complex structures. This article proposes an enhanced dynamic Gaussian mixture model–based damage monitoring method. First, an enhanced Gaussian mixture model constructing algorithm based on a Gaussian mixture model merge-split operation and a singularity inhibition mechanism is developed to keep the stability of the Gaussian mixture model and to obtain a unique optimal solution. Then, a probability similarity–based damage detection index is proposed to realize a normalized and general damage detection. The method combined with guided wave structural health monitoring technique is validated by the hole-edge cracks monitoring of an aluminum plate and a real aircraft wing spar. The results indicate that the method is efficient to improve the reliability and the stability of damage detection under fatigue load and varying structural boundary conditions. The method is simple and reliable regarding aviation application. It is a data-driven statistical method which is model-independent and less experience-dependent. It can be used by combining with different kinds of structural health monitoring techniques.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An enhanced dynamic Gaussian mixture model–based damage monitoring method of aircraft structures under environmental and operational conditions

Qiu

Fang

Yuan

et al. 2018

“…Structural health monitoring (SHM) technology has gradually developed from the research in laboratory to aerospace engineering validations and applications, [1][2][3] in which the problem of reliable damage evaluation under time-varying conditions has become one of the main obstacles for applying SHM technology to real aircraft structures. 1,4 These time-varying conditions include environmental, operational, and structural boundary conditions and possibly others.…”

Section: Introductionmentioning

confidence: 99%

An adaptive guided wave-Gaussian mixture model for damage monitoring under time-varying conditions: Validation in a full-scale aircraft fatigue test

Qiu

Boller

2017

Structural health monitoring technology has gradually developed from the research in laboratory to engineering validations and applications. However, the problem of reliable damage evaluation under time-varying conditions is a main obstacle for applying structural health monitoring to real aircraft structures. Among the existing structural health monitoring methods, the guided wave-based structural health monitoring method is popular but the time-varying problem needs to be addressed. Several methods have been proposed to deal with this problem but limitations remain. In this article, an adaptive guided wave-Gaussian mixture model-based damage monitoring method is proposed. It can be used online without any structural mechanical model and a priori knowledge of damage under time-varying conditions. With this method, a baseline guided wave-Gaussian mixture model is constructed first based on the guided wave features obtained under time-varying conditions when the structure is in healthy state. When a new guided wave feature is obtained during an online damage monitoring process, the guided wave-Gaussian mixture model is updated by an adaptive updating mechanism including dynamic learning and Gaussian components split-merge. The mixture probability structure of the guided wave-Gaussian mixture model and the number of Gaussian components can be optimized adaptively. Finally, a probability damage index is proposed to measure the degree of variation between the baseline guided wave-Gaussian mixture model and the online guided wave-Gaussian mixture model to reveal the damage-induced weak cumulative variation trend of the guided wave-Gaussian mixture model so as to increase the damage evaluation reliability. The method is validated in a full-scale aircraft fatigue test, and the results indicate that the reliable crack growth monitoring of the right landing gear spar and the left wing panel under the fatigue load condition is achieved.

“…To date, there is no theoretical predictive or simulation model to reliably describe the mechanics of cumulative fatigue damage in composites. 7,8 Hence, structural state awareness, diagnostics, and life predictions are primarily data-driven, mainly carried out using non-destructive inspections (NDIs) and structural health monitoring (SHM) techniques such as ultrasonic pitch-catch, pulse-echo, optical Fiber Bragg Grating (FBG), and acoustic emission (AE).…”

Section: Introductionmentioning

confidence: 99%

Distributed acoustic emission sensing for large complex air structures

Haile

Bordick

Riddick

2017

The vast majority of existing work on acoustic emission–based structural health monitoring is for geometrically simple structures with uninterrupted propagation path and constant wave speed. Realistic systems such as a full-scale fuselage, however, are built from interconnected pieces of acoustically mismatched parts such as sandwich core panels, stringer stiffened skin, and fastener holes. The geometric complexity and dynamic operating environment of realistic systems mean that the acoustic emission wave undergoes multiple reflections, refractions, and mode changes resulting in overlapped transducer outputs with no clear beginning and end. The objective of this paper is to outline the fundamental limitations of acoustic emission as applied to complex systems and present a new distributed data-centric acoustic emission sensing network for durability health monitoring and damage tolerance applications in large and complex systems. The study considers the case of a full-scale composite rotorcraft fuselage to introduce several new concepts on acoustic emission data acquisition time control for alleviating effects of wave distortion as well as methods for improving event location analysis in large quasi-isotropic materials. Methods for adaptive front-end signal processing and data volume control are presented. Despite the size and complexity of realistic full-scale systems and the acoustic emission data, we show that it is possible to locate damage with acceptable accuracy.