Analysis of FBG Sensors Performances When Integrated Using Different Methods for Health and Structural Monitoring in Aerospace Applications

Aimasso, Alessandro; Vedova, Matteo Davide Lorenzo Dalla; Maggiore, Paolo

doi:10.1109/icsrs56243.2022.10067392

Cited by 5 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In an operational scenario, the actual trends from the monitored component are logged in a Prognostic and Health Management Computer (PHMC). In this sense, a routine of typically used control laws can be applied as explained in [21]. In other words, during walk-around checks or even when flight surfaces functioning is checked while approaching the take off lane, a series of predefined signals can be sent to the flight control actuators (e.g.…”

Section: Fdi Overview and Concept Of Operationsmentioning

confidence: 99%

Prognostics of aerospace electromechanical actuators: comparison between model-based metaheuristic methods

Baldo

Vedova

Querques

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Electro-Mechanical Actuators (EMAs) deployment as aircraft flight control actuators is an imperative step towards more electric concepts, which propose an increased electrification in aircraft subsystems at the expense of the hydraulic system. Despite the strong benefits linked to EMAs adoption, their deployment is slowed down due to the lack of statistical data and analyses concerning their often-critical failure modes. Prognostics and Health Management (PHM) techniques can support their adoption in safety critical domains. A very promising approach involves the development of model-driven prognostics methodologies based on metaheuristic bio-inspired algorithms. Evolutionary (Differential Evolution (DE)) and swarm intelligence (particle swarm (PSO), grey wolf (GWO)) methods are approached for PMSM based EMAs. Furthermore, two models were developed: a reference, high fidelity model and a monitoring, low fidelity counterpart. Several failure modes have implemented: dry friction, backlash, short circuit, eccentricity and proportional gain. The results show that these algorithms could be employed in pre-flight checks or during the flight at specific time intervals. Therefore, EMA actual state can be assessed and PHM strategies can provide technicians with the right information to monitor the system and to plan and act accordingly (e.g. estimating components Remaining Useful Life (RUL)), thus enhancing the system availability, reliability and safety.

show abstract

Section: Fdi Overview and Concept Of Operationsmentioning

confidence: 99%

Prognostics of aerospace electromechanical actuators: comparison between model-based metaheuristic methods

Baldo

Vedova

Querques

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

Influence of processing parameters on the resin pocket geometry of smart composites embedded with Fiber Bragg Grating sensor and its fast prediction

Zhao,

Zhang,

Liu

et al. 2024

Polymer Composites

View full text Add to dashboard Cite

Smart composites embedded with Fiber Bragg Grating (FBG) sensors are getting increasingly popular in aircraft structures due to their self‐sensing and monitoring capability. However, a resin pocket around the FBG sensor would be formed in the manufacturing process, which strongly impacts the mechanical properties of the composite host. Therefore, the influence of primary processing parameters (FBG orientation angle, preforming temperature, and preforming pressure) on the formation of resin pockets was investigated in this work. It is found that there is a sinusoidal relation between the resin pocket geometry and the FBG orientation angle. A negative linear relation is noted between the resin pocket geometry and the preforming temperature and pressure. A prediction model based on the backpropagation neural network (BPNN) was established, which was able to quickly and accurately predict resin pocket geometry subjected to given processing parameters. This work provides useful guidance for the processing control of smart composites embedded with FBG sensors.Highlights Resin pocket geometry was formed primarily in the preforming stage. Effect of processing parameters on resin pocket geometry was characterized. A BPNN model was established for the fast prediction of resin pocket geometry.

show abstract

Stress Simulation of Polydimethylsiloxane-Coated Fiber Bragg Grating Bend Sensor

Mohd Razali,

Saris,

Sopian

et al. 2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

Analysis of FBG Sensors Performances When Integrated Using Different Methods for Health and Structural Monitoring in Aerospace Applications

Cited by 5 publications

References 14 publications

Prognostics of aerospace electromechanical actuators: comparison between model-based metaheuristic methods

Prognostics of aerospace electromechanical actuators: comparison between model-based metaheuristic methods

Influence of processing parameters on the resin pocket geometry of smart composites embedded with Fiber Bragg Grating sensor and its fast prediction

Stress Simulation of Polydimethylsiloxane-Coated Fiber Bragg Grating Bend Sensor

Contact Info

Product

Resources

About