Industrial Age non-destructive evaluation to Information Age structural health monitoring

Abstract

This article was originally presented as a keynote address at the Ninth International Workshop on Structural Health Monitoring with the intent of provoking discussions relating to the transformation of aircraft maintenance practices by exploiting opportunities and benefits offered by Information Age technology and techniques. The US Air Force currently manages its aircraft using a schedule-based maintenance philosophy. This schedule-based approach works well for ensuring aircraft integrity; however, it is very costly, labor-intensive, and reduces aircraft availability. Structural health monitoring systems have the potential to analyze near-real-time and historical weapon systems data to provide a predictive maintenance capability. However, much aerospace structural health monitoring research has focused on in situ structural inspection techniques instead of structural monitoring. Structural inspections typically entail examining key locations of an airframe for material degradation or flaws. These examinations usually occur at predefined time intervals. As such, each inspection is considered an independent evaluation. Conversely, structural monitoring involves continuous condition surveillance of an airframe over an extended period of time. Structural monitoring uses past conditions and expected future conditions for producing a comprehensive understanding of the current health state. A new architecture, Cognitive Architecture for State Exploitation, is introduced as a monitoring technique that combines diagnostic or state (i.e. health) assessments, prognostic assessments, and mission objectives into a common framework to enable goal-based decision making. Results from a laboratory experiment are utilized to demonstrate the application of Cognitive Architecture for State Exploitation and to illustrate the potential to improve effectiveness and efficiency metrics compared to those of the current US Air Force maintenance procedures.

Keywords

Condition-based maintenance non-destructive evaluation structural health monitoring cognitive architecture

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References

Condition-Based Maintenance Plus DoD guidebook. Washington, DC: United States Department of Defense, 2008.

Air Force Times. Readiness declines in aging, overworked fleet, http://www.airforcetimes.com/article/20131002/NEWS04/310020026/Readiness-declines-aging-overworked-fleet (2013, accessed 23 January 2014).

Spencer

. The precious sortie: the United States Air Force at the intersection of rising energy prices, an aging fleet, a struggling recapitalization effort, and stressed defense budgets. Master’s Thesis, Marine Corps War College, Quantico, VA, 2009.

MIL-STD-1530C. DoD standard practice. Aircraft Structural Integrity Program (ASIP). Wright-Patterson AFB, OH: United States Air Force, 2005.

Harter

. AFGROW users guide and technical manual. AFRL-VA-WP-TR-1999-3016, February 1999. Wright-Patterson AFB, OH: US Air Force Research Laboratory.

Meruane

Heylen

. Structural damage assessment under varying temperature conditions. Struct Health Monit 2012; 11(3): 345–358.

Figueiredo

Park

Farrar

. Machine learning algorithms for damage detection under operational and environmental variability. Struct Health Monit 2011; 10(6): 559–572.

MIL-HDBK-1823A. Nondestructive evaluation system reliability assessment. Washington, DC: United States Department of Defense, 2009.

Cobb

Michaels

. Ultrasonic structural health monitoring: a probability of detection case study. In: AIP review of progress in quantitative nondestructive evaluation (ed Thompson

Chimenti

), Kingston, RI, 26–31 July 2009, vol. 1096, pp. 1800–1807. Melville, NY: AIP.

10.

Aldrin

Medina

Santiago

. Demonstration study for reliability assessment of SHM systems incorporating model-assisted probability of detection approach. In: AIP review of progress in quantitative nondestructive evaluation (ed Thompson

Chimenti

), Denver, CO, 15–20 July 2012, vol. 1430, pp. 1543–1550. Melville, NY: AIP.

11.

Shook

Millwater

Enright

. Simulation of recurring automated inspections on probability-of-fracture estimates. Struct Health Monit 2008; 7(4): 293–307.

12.

Derriso

. Machine conscious architecture for state exploitation and decision making. PhD Thesis, Air Force Institute of Technology, Wright-Patterson AFB, OH, 2013.

13.

Derriso

DeSimio

McCurry

. Global workspace theory inspired architecture for autonomous structural health monitoring. In: IEEE national aerospace and electronics conference, Dayton, OH, 25–27 July 2012, pp. 190–195. New York: IEEE.

14.

Derriso

. Hot spots health monitoring for F-22/F-15 applications. In: Aircraft Structural Integrity Program conference, San Antonio, TX, 29 November–1 December 2011.

15.

Perry

TS.

John

Hennessy: risk taker. IEEE Spectr 2012; 49(5): 28–32.