Empirical Tests of Wireless Sensor Network in Jet Engine Including Characterization of Radio Wave Propagation and Fading

Grudén, Mathias; Jobs, Magnus; Rydberg, Anders

doi:10.1109/lawp.2014.2316311

Cited by 11 publications

(6 citation statements)

References 3 publications

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“…Using the data obtained through the proposed model, the K-factor for a given frequency f and distance z was calculated using the average signal amplitude when going around the structure along a circular path. This was done in order to evaluate the fading experiences when rotating around the structure and to compare with the K-factors presented in [2,4]. The resulting plot is shown in Fig.…”

Section: Fig 1 Simplified Model Showing Coordinate Axis and Raysmentioning

confidence: 99%

“…Discussion: The main purpose of this letter is not to provide a new way to calculate the field distribution inside the structure but rather to investigate whether propagating helical waves could explain the location and frequency dependence in the results obtained in [1][2][3][4]. Although the exact geometry of a jet turbine differs from the simplified model, here we have considered it very likely that the same underlying principle can be used to model the propagation behaviour in a real turbine engine.…”

Section: Fig 1 Simplified Model Showing Coordinate Axis and Raysmentioning

confidence: 99%

“…Introduction: A number of recent studies [1][2][3][4] have measured the propagation behaviour of microwaves in jet turbine environments. In these studies, it has been found that propagating waves inside such a system suffer from heavy fading with large variations in the characteristics of the fading behaviour as a function of frequency.…”

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confidence: 99%

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Modelling of EM propagation in simplified jet turbine structure using helical rays

2015

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To explain the variations in field strength measurements seen when measuring radio-frequency (RF) propagation in jet turbines, a model based on helical propagating waves is presented. The model uses a simplified model of the jet turbine based on a cylindrical structure and is compared with both electromagnetic (EM) simulations and measured values. Measurements and simulated values show good correspondence and show that a sum of helical propagating waves could be a useful tool to model the expected field variations.

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Section: Fig 1 Simplified Model Showing Coordinate Axis and Raysmentioning

confidence: 99%

Section: Fig 1 Simplified Model Showing Coordinate Axis and Raysmentioning

confidence: 99%

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Modelling of EM propagation in simplified jet turbine structure using helical rays

2015

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“…Structural health systems have a wide application in countless civilian infrastructures such as bridges [24] and buildings [6]. Similarly, SHM systems have been also applied to monitor mechanical components such as fuselages helicopters [7], wind turbines installed on land [8,9] and sea (offshore) [10], aerospace equipment [11], aircraft [12], high-speed trains [13], aircraft turbines [14] and boats [15], in the same way SHM systems have been applied to marine renewable energy equipment [16]. It is noteworthy that the environmental conditions need to be considered to ensure a robust damage detection, in this sense, some works have been introduced to compensate the effects of the temperature changes [17,18].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Methodologies for Structural Damage Detection Based on Machine Learning Applications

Vitola¹,

Anaya²,

Burgos³

et al. 2016

Pattern Recognition - Analysis and Applications

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Structural health monitoring (SHM) is an important research area, which interest is the damage identification process. Different information about the state of the structure can be obtained in the process, among them, detection, localization and classification of damages are mainly studied in order to avoid unnecessary maintenance procedures in civilian and military structures in several applications. To carry out SHM in practice, two different approaches are used, the first is based on modelling which requires to build a very detailed model of the structure, while the second is by means of data-driven approaches which use information collected from the structure under different structural states and perform an analysis by means of data analysis. For the latter, statistical analysis and pattern recognition have demonstrated its effectiveness in the damage identification process because real information is obtained from the structure through sensors installed permanently to the observed object allowing a real-time monitoring. This chapter describes a damage detection and classification methodology, which makes use of a piezoelectric active system which works in several actuation phases and that is attached to the structure under evaluation, principal component analysis, and machine learning algorithms working as a pattern recognition methodology. In the chapter, the description of the developed approach and the results when it is tested in one aluminum plate are also included.

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“…Wireless Sensor Networks (WSNs) are recently employed in many real‐world applications such as health care monitoring , environment monitoring , habitat monitoring , surveillance systems and military applications . In most applications, sensors measure or collect data and then transmit the information to a processing sink.…”

Section: Introductionmentioning

confidence: 99%

Low end‐to‐end delay fuzzy networking protocol for mobile wireless sensing

Mazaheri

Kavian

Sharif

et al. 2016

Wireless Communications

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Because of the practical limitations of the energy and processing capabilities, the deployment of many Wireless Sensor Networks (WSN) is facing two main challenges of increasing network lifetime and reducing End to End Delay (EED) which become critical when the nodes are mobile and use non-rechargeable energy sources. One way to help to extend network lifetime is using fuzzy logic in a form of artificial intelligence. To this end we propose a new routing protocol for using mobile WSNs, which holds the nodes in an equal level of energy and decreases energy dissipation of the network. An optimum path is selected based on the cost of each node to increase network lifetime. In order to lessen EED, we also attempt to design a novel zoning-scheme for the network area. In this scheme, zonation is dynamic and works based on the Data Link (DL) position. The simulation result shows a significant improvement in lifetime and EED by proposed protocol compared with existing protocols.

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Empirical Tests of Wireless Sensor Network in Jet Engine Including Characterization of Radio Wave Propagation and Fading

Cited by 11 publications

References 3 publications

Modelling of EM propagation in simplified jet turbine structure using helical rays

Modelling of EM propagation in simplified jet turbine structure using helical rays

Data-Driven Methodologies for Structural Damage Detection Based on Machine Learning Applications

Low end‐to‐end delay fuzzy networking protocol for mobile wireless sensing

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