Interactive Camera Network Design using a Virtual Reality Interface

Traditional literature on camera network design focuses on constructing automated algorithms. These require problem specific input from experts in order to produce their output. The nature of the required input is highly unintuitive leading to an unpractical workflow for human operators. In this work we focus on developing a virtual reality user interface allowing human operators to manually design camera networks in an intuitive manner. From real world practical examples we conclude that the camera networks designed using this interface are highly competitive with, or superior to those generated by automated algorithms, but the associated workflow is much more intuitive and simple. The competitiveness of the human-generated camera networks is remarkable because the structure of the optimization problem is a well known combinatorial NP-hard problem. These results indicate that human operators can be used in challenging geometrical combinatorial optimization problems given an intuitive visualization of the problem.Comment: 11 pages, 8 figure

Vibration suppression in multi-body systems by means of disturbance filter design methods

This paper addresses the problem of interaction in mechanical multi-body systems and shows that subsystem interaction can be considerably minimized while increasing performance if an efficient disturbance model is used. In order to illustrate the advantage of the proposed intelligent disturbance filter, two linear model based techniques are considered: IMC and the model based predictive (MPC) approach. As an illustrative example, multivariable mass-spring-damper and quarter car systems are presented. An adaptation mechanism is introduced to account for linear parameter varying LPV conditions. In this paper we show that, even if the IMC control strategy was not designed for MIMO systems, if a proper filter is used, IMC can successfully deal with disturbance rejection in a multivariable system, and the results obtained are comparable with those obtained by a MIMO predictive control approach. The results suggest that both methods perform equally well, with similar numerical complexity and implementation effort

RTM production monitoring of the A380 hinge arm droop nose mechanism: a multi-sensor approach

his research presents a case study of production monitoring on an aerospace composite component: the hinge arm of the droop nose mechanism on the Airbus A380 wing leading edge. A sensor network composed of Fibre Bragg Gratings, capacitive sensors for cure monitoring and thermocouples was embedded in its fibre reinforced lay-up and measurements were acquired throughout its Resin Transfer Moulding production process. Two main challenges had to be overcome: first, the integration of the sensor lines in the existing Resin Transfer Moulding mould without modifying it; second, the demoulding of the component without damaging the sensor lines. The proposed embedding solution has proved successful. The wavelength shifts of the Fibre Bragg Gratings were observed from the initial production stages, over the resin injection, the complete curing of the resin and the cooling-down prior to demoulding. The sensors proved to be sensitive to detecting the resin flow front, vacuum and pressure increase into the mould and the temperature increase caused by the resin curing. Measurements were also acquired during the post-curing cycle. Residual strains during all steps of the process were derived from the sensors’ wavelength shift, showing values up to 0.2% in compression. Moreover, the capacitive sensors were able to follow-up the curing degree during the production process. The sensors proved able to detect the resin flow front, whereas thermocouples could not measure an appreciable increase of temperature due to the fact that the resin had the same temperature as the mould

Detection, localization and quantification of impact events on a stiffened composite panel with embedded fiber bragg grating sensor networks

Nowadays, it is possible to manufacture smart composite materials with embedded fiber optic sensors. These sensors can be exploited during the composites' operating life to identify occurring damages such as delaminations. For composite materials adopted in the aviation and wind energy sector, delaminations are most often caused by impacts with external objects. The detection, localization and quantification of such impacts are therefore crucial for the prevention of catastrophic events. In this paper, we demonstrate the feasibility to perform impact identification in smart composite structures with embedded fiber optic sensors. For our analyses, we manufactured a carbon fiber reinforced plate in which we embedded a distributed network of fiber Bragg grating (FBG) sensors. We impacted the plate with a modal hammer and we identified the impacts by processing the FBG data with an improved fast phase correlation (FPC) algorithm in combination with a variable selective least squares (VS-LS) inverse solver approach. A total of 164 impacts distributed on 41 possible impact locations were analyzed. We compared our methodology with the traditional P-Inv based approach. In terms of impact localization, our methodology performed better in 70.7% of the cases. An improvement on the impact time domain reconstruction was achieved in 95.1% of the cases

Dynamic strain measurements on automotive and aeronautic composite components by means of embedded fiber bragg grating sensors

The measurement of the internal deformations occurring in real-life composite components is a very challenging task, especially for those components that are rather difficult to access. Optical fiber sensors can overcome such a problem, since they can be embedded in the composite materials and serve as in situ sensors. In this article, embedded optical fiber Bragg grating (FBG) sensors are used to analyze the vibration characteristics of two real-life composite components. The first component is a carbon fiber-reinforced polymer automotive control arm; the second is a glass fiber-reinforced polymer aeronautic hinge arm. The modal parameters of both components were estimated by processing the FBG signals with two interrogation techniques: the maximum detection and fast phase correlation algorithms were employed for the demodulation of the FBG signals; the Peak-Picking and PolyMax techniques were instead used for the parameter estimation. To validate the FBG outcomes, reference measurements were performed by means of a laser Doppler vibrometer. The analysis of the results showed that the FBG sensing capabilities were enhanced when the recently-introduced fast phase correlation algorithm was combined with the state-of-the-art PolyMax estimator curve fitting method. In this case, the FBGs provided the most accurate results, i.e., it was possible to fully characterize the vibration behavior of both composite components. When using more traditional interrogation algorithms (maximum detection) and modal parameter estimation techniques (Peak-Picking), some of the modes were not successfully identified

Calibration of UR10 robot controller through simple auto-tuning approach

This paper presents a calibration approach of a manipulator robot controller using an auto-tuning technique. Since the industry requires machines to run with increasing speed and precision, an optimal controller is too demanding. Even though the robots make use of an internal controller, usually, this controller does not fulfill the user specification with respect to their applications. Therefore, in order to overcome the user requirements, an auto-tuning method based on a single sine test is employed to obtain the optimal parameters of the proportional-integral-derivative PID controller. This approach has been tested, validated and implemented on a UR10 robot. The experimental results revealed that the performances of the robot increased when the designed controller, using the auto-tuning technique, was employed

Strain monitoring.

This chapter provides an overview of the use of strain sensors for structural health monitoring. Compared to acceleration-based sensors, strain sensors can measure the deformation of a structure at very low frequencies (up to DC) and enable the measurement of ultrasonic responses. Many existing SHM methods make use of strain measurement data. Furthermore, strain sensors can be easily integrated in (aircraft) structures. This chapter discusses the working principle of traditional strain gauges (Sect. 8.1) and different types of optical fiber sensors (Sect. 8.2). The installation requirements of strain sensors and the required hardware for reading out sensors are provided. We will also give an overview of the advantages and the limitations of commonly used strain sensors. Finally, we will present an overview of the applications of strain sensors for structural health monitoring in the aeronautics field

Conclusions

The state of the art of structural health monitoring damage detection systems reviewed in this book shows that it is a promising area of technologies. SHM damage detection systems in civil aviation are still mostly limited to lab applications because there are still issues, which need to be solved for such systems to be integrated in an aircraft structure. Therefore, further research is needed to solve the current drawbacks/limitations of the existing SHM approaches such that this technology can be used in aircrafts. Despite the current limitations, SHM application for damage detection in aircrafts would make the flying safer and the structure lifetime longer and reduce the maintenance time and costs considering that the maintenance could be performed not at the predetermined intervals, but upon the need based on the condition that would be determined by the SHM systems used. We conclude some of the important differences and the common challenges to the methods reviewed in this book and provide an outlook on the next steps to a successful implementation