Identification of distortions to FBG spectrum using FBG fixed filters
- Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Wang, Hao
- Date: 2011
- Type: Text , Conference paper
- Relation: 18th International Conference on Composites Materials, ICCM 2011
- Full Text: false
- Reviewed:
- Description: Recent advances in fibre optic sensor technologies have provided great opportunities to develop more sophisticated in-situ SHM systems. There have been a large number of research reports on health monitoring of composite structures using Fibre Bragg Grating (FBG) sensors. Distortion of FBG sensors has been successfully used by many researchers to identify damage and to locate damage in composite structures. Observations of the distorted sensor spectrums due to stress concentrations caused by delaminations and cracks, have been using to estimate the damage conditions. The majority of the research works were focused on the investigation of the spectra of a FBG sensor embedded in the vicinity of a damage, in order to detect and identify the damage by relating to the distortion of the FBG sensor spectra. However the cause of the distortion of FBG spectra not only depends on the consequences of accumulated damage but also loading types and the fibre orientation. Embedding FBG's in-between non parallel fibre layers and the application of torque have caused substantial distortions to the FBG spectra. A reference FBG spectra needs to be incorporated to FBG measurements to identify the variations to the FBG spectrum and to distinguish the other effects causing distortions. For this purpose, a fixed FBG based system was developed to measure the reflected FBG spectra in time domain. The fixed FBG method was used to estimate the peak using non distorted FBG spectra previously. Unfortunately there was no work done on the identification of distortions of FBG spectra using fixed FBG sensors. This paper details the research work performed to identify distortions of reflected spectra of an embedded FBG sensors inside a composite laminate. The developed method will provide the flexibility of input FBG time domain data directly to post processing algorithms for decoding and damage identification.
UAV-enabled data acquisition scheme with directional wireless energy transfer for Internet of Things
- Authors: Liu, Yalin , Dai, Hong-Ning , Wang, Hao , Imran, Muhammad , Wang, Xiaofen , Shoaib, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Computer Communications Vol. 155, no. (2020), p. 184-196
- Full Text: false
- Reviewed:
- Description: Low power Internet of Things (IoT) is suffering from two limitations: battery-power limitation of IoT nodes and inflexibility of infrastructure-node deployment. In this paper, we propose an Unmanned Aerial Vehicle (UAV)-enabled data acquisition scheme with directional wireless energy transfer (WET) to overcome the limitations of low power IoT. The main idea of the proposed scheme is to employ a UAV to serve as both a data collector and an energy supplier. The UAV first transfers directional wireless energy to an IoT node which then sends back the data packets to the UAV by using the harvested energy. Meanwhile, we minimize the overall energy consumption under conditions of balanced energy supply and limited overall time. Moreover, we derive the optimal values of WET time and data transmission power. After analysing the feasibility of the optimal WET time and data transmission, we design an allocation scheme based on the feasible ranges of data size level and channel-fading degree. The numerical results show the feasibility and adaptability of our allocation scheme against the varied values of multiple system parameters. We further extend our scheme to the multi-node scenario by re-designing energy beamforming and adopting multi-access mechanisms. Moreover, we also analyse the mobility of UAVs in the proposed scheme. © 2020 Elsevier B.V.
Prediction of obsolete FBG sensor using ANN for efficient and robust operation of SHM systems
- Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Wang, Hao , Lau, Alan
- Date: 2012
- Type: Text , Conference paper
- Relation: 4th Asia-Pacific Workshop on Structural Health Monitoring p. 546-553
- Full Text: false
- Reviewed:
- Description: Increased use of FRP composites for critical load bearing components and structures in recent years has raised the alarm for urgent need of a comprehensive health mentoring system to alert users about integrity and the health condition of advanced composite structures. A few decades of research and development work on structural health monitoring systems using Fibre Bragg Grating (FBG) sensors have come to an accelerated phase at the moment to address these demands in advanced composite industries. However, there are many unresolved problems with identification of damage status of composite structures using FBG spectra and many engineering challenges for implementation of such FBG based SHM system in real life situations. This paper details a research work that was conducted to address one of the critical problems of FBG network, the procedures for immediate rehabilitation of FBG sensor networks due to obsolete/broken sensors. In this study an artificial neural network (ANN) was developed and successfully deployed to virtually simulate the broken/obsolete sensors in a FBG sensor network. It has been found that the prediction of ANN network was within 0.1% error levels.
Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies
- Authors: Dai, Hong-Ning , Wang, Hao , Xu, Guangquan , Wan, Jiafu , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Enterprise Information Systems Vol. 14, no. 9-10 (2020), p. 1279-1303
- Full Text: false
- Reviewed:
- Description: Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area. © 2019 Informa UK Limited, trading as Taylor & Francis Group.
Impact of load ramping on power transformer dissolved gas analysis
- Authors: Cui, Huize , Yang, Liuging , Li, Shengtao , Qu, Guanghao , Wang, Hao , Abu-Siada, Ahmed , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 170343-170351
- Full Text:
- Reviewed:
- Description: Dissolved gas in oil analysis (DGA) is one of the most reliable condition monitoring techniques, which is currently used by the industry to detect incipient faults within the power transformers. While the technique is well matured since the development of various offline and online measurement techniques along with various interpretation methods, no much attention was given so far to the oil sampling time and its correlation with the transformer loading. A power transformer loading is subject to continuous daily and seasonal variations, which is expected to increase with the increased penetration level of renewable energy sources of intermittent characteristics, such as photovoltaic (PV) and wind energy into the current electricity grids. Generating unit transformers also undergoes similar loading variations to follow the demand, particularly in the new electricity market. As such, the insulation system within the power transformers is expected to exhibit operating temperature variations due to the continuous ramping up and down of the generation and load. If the oil is sampled for the DGA measurement during such ramping cycles, results will not be accurate, and a fault may be reported due to a gas evolution resulting from such temporarily loading variation. This paper is aimed at correlating the generation and load ramping with the DGA measurements through extensive experimental analyses. The results reveal a strong correlation between the sampling time and the generation/load ramping. The experimental results show the effect of load variations on the gas generation and demonstrate the vulnerabilities of misinterpretation of transformer faults resulting from temporary gas evolution. To achieve accurate DGA, transformer loading profile during oil sampling for the DGA measurement should be available. Based on the initial investigation in this paper, the more accurate DGA results can be achieved after a ramping down cycle of the load. This sampling time could be defined as an optimum oil sampling time for transformer DGA.
Use of FBG sensors in SHM of aerospace structures
- Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Wang, Hao
- Date: 2012
- Type: Text , Conference paper
- Relation: Third Asia Pacific Optical Sensors Conference
- Full Text: false
- Reviewed:
- Description: This paper discusses the use of Fibre Bragg grating sensors (FBG) in structural health monitoring (SHM) of Fibre reinforced polymer (FRP) aerospace structures. The diminutive sensor provided the capability of embedding inside FRP structures in order to monitor vital potential locations for damage. Some practical problems associate with manufacturing process of FRP with embedded FBG sensors, interrelation of distortion to FBG spectra with damage, and interpretation of FBG spectral responses for identifying the damage will be discussed.
Blockchain-based data privacy management with Nudge theory in open banking
- Authors: Wang, Hao , Ma, Shenglan , Dai, Hong-Ning , Imran, Muhammad , Wang, Tongsen
- Date: 2020
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 110, no. (2020), p. 812-823
- Full Text:
- Reviewed:
- Description: Open banking brings both opportunities and challenges to banks all over the world especially in data management. A blockchain as a continuously growing list of records managed by a peer-to-peer network is widely used in various application scenarios; and it is commonly agreed that the blockchain technology can improve the protection of financial data privacy. However, current blockchain technology still poses some challenges in fully meeting the needs of financial data privacy protection. In order to address the existing problems, this paper proposes a new data privacy management framework based on the blockchain technology for the financial sector. The framework consists of three components: (1) a data privacy classification method according to the characteristics of financial data; (2) a new collaborative-filtering-based model; and (3) a data disclosure confirmation scheme for customer strategies based on the Nudge Theory. We implement a prototype and propose a set of algorithms for this framework. The framework is validated through field experiments and laboratory experiments. © 2019 Elsevier B.V.
Optimized FBG sensor network for efficient detection of a delamination in FRP structures
- Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Wang, Hao , Lau, Alan
- Date: 2012
- Type: Text , Conference paper
- Relation: 8th Asian-Australasian Conference on Composite Materials 2012 p. 1443-1448
- Full Text: false
- Reviewed:
- Description: Delamination is a potential cause of failure of composite components. Due to the hidden nature of propagation, the detection of delaminations in composites is a time consuming and extremely difficult task. A few decades of research have shown the effectiveness of the embedded fibre Bragg grating (FBG) sensors to detect such damage in fibre reinforced polymeric (FRP) structures. However, a number of sensors are required to detect delaminations within a particular region of a composite structure due the limited receptive range of an FBG sensor. The complexity and the cost of manufacturing increases with the number of sensors attached and therefore, estimation of the optimum number of sensors for efficient identification of damage is an equally important factor to investigate. This paper details a study on optimization of the number of sensors used to monitor damage in a critical region of an FRP structure. A detailed finite element analysis (FEA) was used for the investigation. A delamination and several FBG sensors were simulated in FEA. The strain values at simulated FBG sensors were used as an input for the development of an optimization algorithm, using artificial neural network (ANN). The number of FBG sensors was decreased until the prediction of the algorithm was reached within a 0.1% error level. The optimal number of FBGs was taken at 0.1% error level with a minimum number of epoch. Furthermore, the effect of obsolete sensors of an optimized sensor network on prediction of the delamination, was also investigated. Copyright © (2012) Asian-Australasian Association for Composite Materials (AACM).
Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network
- Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Wang, Hao , Canning, John , Lau, Alan
- Date: 2013
- Type: Text , Journal article
- Relation: Measurement: Journal of the International Measurement Confederation Vol. 46, no. 10 (2013), p. 4045-4051
- Full Text:
- Reviewed:
- Description: Fibre Bragg Grating (FBG) sensors have been used in the development of structural health monitoring (SHM) and damage detection systems for advanced composite structures over several decades. Unfortunately, to date only a handful of appropriate configurations and algorithm sare available for using in SHM systems have been developed. This paper reveals a novel configuration of FBG sensors to acquire strain reading and an integrated statistical approach to analyse data in real time. The proposed configuration has proven its capability to overcome practical constraints and the engineering challenges associated with FBG-based SHM systems. A fixed filter decoding system and an integrated artificial neural network algorithm for extracting strain from embedded FBG sensor were proposed and experimentally proved. Furthermore, the laboratory level experimental data was used to verify the accuracy of the system and it was found that the error levels were less than 0.3% in predictions. The developed SMH system using this technology has been submitted to US patent office and will be available for use of aerospace applications in due course. © 2013 Elsevier Ltd. All rights reserved.