Abstract
It has been established that corrosion is one of the most important factors causing deterioration and decreased performance and reliability in critical aerospace and industrial systems. Corrosion monitoring, detection, and quantification are recognized as key enabling technologies to reduce the impact of corrosion on the integrity of aircraft and industrial assets. Accurate and reliable detection of corrosion initiation and propagation, with specified false alarm rates, requires novel tools and methods, including verifiable simulation and modeling methods. This paper reports an experimental investigation of the detection and quantification of pitting corrosion on aluminum alloy panels using 3D surface metrology methods and image processing techniques. Panel surfaces were evaluated by laser microscopy and stylus-based profilometry to characterize global and local surface features. Promising imaging and texture features were extracted and compared between coated and uncoated aluminum panels at different exposure times under accelerated corrosion conditions. Image processing, information processing, and data mining techniques were utilized to evaluate the presence and extent of pitting corrosion. A new modeling framework for corrosion stages is introduced that emphasizes the representation of pitting corrosion and ultimately the crack formation process. Detection and prediction of the evolution of corrosion stages relies on data, a particle filtering method, and the corrosion propagation model. Results from these experimental studies demonstrate the efficacy of this proposed methodology.
Highlights
Corrosion is known to cause the loss of billions of dollars every year in structural integrity deterioration, leading to decreased performance and reliability of military and civil engineering assets
The remainder of this paper is organized by the following: Section 2 outlines the PHM architecture followed in this study; Section 3 identifies the corrosion modeling-based / application specific features and feature extraction routines used in the development of the prognostic/diagnostic models; Section 4 outlines the experimental materials and methods followed in this study; Section 5 presents the modeling results; and Section 6 summarizes the findings of the study, lessons learned, and future work
The Fault Detection and Identification (FDI) procedure may be interpreted as the fusion and utilization of the information present in a feature vector with the objective of determining the operational condition of a system and the causes for deviations from desired behavioral patterns (McAdam, Newman, McKenzie, Davis, & Hinton, 2005)
Summary
Corrosion is known to cause the loss of billions of dollars every year in structural integrity deterioration, leading to decreased performance and reliability of military and civil engineering assets. Corrosion starts in the form of pitting, due to the presence of a surface contaminant or material heterogeneity Facilitation of this process occurs by the interaction of the corrosive environment and cyclic loading, resulting in fatigue crack initiation across pitted areas that further grows and leads to accelerated structural failure (Pidaparti, 2007). Facilitated by understanding and modeling of corrosion growth and associated processes, continuous monitoring, detection, localization, and quantification of corrosion, and further, prediction of corrosion damage growth in complex structures over large, partially accessible areas are of growing interest in the aerospace industries. Scanning may become impractical when inspection area is inaccessible To address this a number of research techniques have been developed, including guided wave tomography to screen large areas of complex structure for corrosion detection, localization (Clark, 2009) and defect depth mapping (Belanger, Cawley, & Simonetti, 2010). The remainder of this paper is organized by the following: Section 2 outlines the PHM architecture followed in this study; Section 3 identifies the corrosion modeling-based / application specific features and feature extraction routines used in the development of the prognostic/diagnostic models; Section 4 outlines the experimental materials and methods followed in this study; Section 5 presents the modeling results; and Section 6 summarizes the findings of the study, lessons learned, and future work
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