State-of-the-art in integrated vehicle health management

Benedettini, Ornella; Baines, Tim; Lightfoot, Howard; Greenough, Richard

doi:10.1243/09544100jaero446

Cited by 98 publications

(64 citation statements)

References 52 publications

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“…Since the sensors are small, they can be embedded or on-surface installed without giving a tremendous weight penalty. The main aircraft manufacturers, such as Boeing (Benedettini et al, 2009) and Airbus, are gearing toward implementing this technology as the material structures become more advanced.…”

Section: Structural Health Monitoring (Shm)mentioning

confidence: 99%

Damage identification using wireless structural health monitoring system through smart sensor application

Shirazi¹,

Mustapha²,

Ahmad

2017

Int. j. adv. appl. sci

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Delamination, disbanding, void, low impact resistance and visible internal damage are some of damages uniquely found in the structure. Any damage found on the structure require repair. The inspection need to be done to detect the damage on structure before the structure can be repaired. NonDestructive Inspection (NDI) and Structural health monitoring (SHM) concept were used to detect flaws from structures. But the SHM differs from NDT which the system used to monitor the integrity of mechanical structures in a continuous and independent way. SHM helps to reduce financial cost for maintenance. SHM can monitor this situation in active and passive states, either by online or offline monitoring. The use of SHM is to augment the NDI application and not to replace it. This paper presents the damage identification technique by using the improved design of wireless structural health monitoring system. Smart PZT sensors were used as an actuator and receiver, coupled with two XBee's and two Ardiuno as signal generator and signal receiver. Program execution on transmitting and receiving the ultrasonic guided wave via the PZT sensor had been written in Makerplot. The acquired results showed that the wave is more even in non-defected area and disrupted in affected area.

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Section: Structural Health Monitoring (Shm)mentioning

confidence: 99%

Damage identification using wireless structural health monitoring system through smart sensor application

Shirazi¹,

Mustapha²,

Ahmad

2017

Int. j. adv. appl. sci

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“…This application is focused on the IVHM system, as to ensure that system health status complies with the standard. [7] Assert that the IVHM system has been so pronouncing and effectively used in the aircraft system. However, the IVHM system can also be used in some other vehicle such as the land vehicle, helicopters, and watercraft [8].…”

Section: Diagnostics and Prognostics Applicationsmentioning

confidence: 99%

The Development of a test bed to support the Design of Diagnostics and Prognostics Applications of a complex system

Emmanuel¹,

Agbep²,

Komi³

2017

IOSR JEEE

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“…The full synthetic measurement (for all four sensor locations) is obtained asŝ ŝ 1 ; : : : ;ŝ 4 . Because each sensor is assumed to have independent noise, we can assemble the full measurement noise model pŝjD j for damage case D j as the product…”

Section: Structural Strain Sensor Modelmentioning

confidence: 99%

“…In OLM, onboard aircraft sensors gather structural loading information to identify damage and fatigue (most often postflight) in order to reduce maintenance costs and increase reliability [2,3]. At the systems level, the integrated vehicle health management (IVHM) field involves frameworks that incorporate multiple sources of operational data, physics-based models, and prognosis techniques [4,5]. Modern IVHM architectures have been developed at NASA [6] and the Department of Defense [7].…”

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confidence: 99%

Methodology for Dynamic Data-Driven Online Flight Capability Estimation

2015

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This paper presents a data-driven approach for the online updating of the flight envelope of an unmanned aerial vehicle subjected to structural degradation. The main contribution of the work is a general methodology that leverages both physics-based modeling and data to decompose tasks into two phases: expensive offline simulations to build an efficient characterization of the problem and rapid data-driven classification to support online decision making. In the approach, physics-based models at the wing and vehicle level run offline to generate libraries of information covering a range of damage scenarios. These libraries are queried online to estimate vehicle capability states. The state estimation and associated quantification of uncertainty are achieved by Bayesian classification using sensed strain data. The methodology is demonstrated on a conceptual unmanned aerial vehicle executing a pullup maneuver, in which the vehicle flight envelope is updated dynamically with onboard sensor information. During vehicle operation, the maximum maneuvering load factor is estimated using structural strain sensor measurements combined with physics-based information from precomputed damage scenarios that consider structural weakness. Compared to a baseline case that uses a static as-designed flight envelope, the self-aware vehicle achieves both an increase in probability of executing a successful maneuver and an increase in overall usage of the vehicle capability. = number of observable vector data samples N s = number of sensors n = load factor n max = maximum load factor before failure n truth max = truth reference maximum load factor n op = operational load factor decided upon using dynamic capability estimate n static op = operational load factor decided upon using static capability estimate from design n util = average utilization of maximum vehicle capability p· = probability p op = threshold probability for decisions using dynamic capability estimate R = number of damage library records S = support vector machine discriminant function ("score") S = observable vector space s = observable vector s = observable vector measurement V = airspeed w c = chordwise extent of damage on wing w s = spanwise extent of damage on wing X = vehicle state space x = vehicle state α j = support vector machine weight for jth training sample β 1 , β 2 = probabilistic support vector machine regression model parameters ϵ k = kth strain gage rosette output ε k = plane strain at location of kth strain gage rosette

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State-of-the-art in integrated vehicle health management

Cited by 98 publications

References 52 publications

Damage identification using wireless structural health monitoring system through smart sensor application

Damage identification using wireless structural health monitoring system through smart sensor application

The Development of a test bed to support the Design of Diagnostics and Prognostics Applications of a complex system

Methodology for Dynamic Data-Driven Online Flight Capability Estimation

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