Browsing by Author "Addepalli, Pavan"

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    Data for research paper "The Spatial Resolution Enhancement for a Thermogram Enabled by Controlled Sub-pixel Movements"
    (Cranfield University, 2019-08-14 15:13) Zhao, Yifan; Dai, Weixiang; Addepalli, Pavan
    The measurement accuracy and reliability of thermography is largely limited by a relatively low spatial resolution of the thermal imager. Using a high-end camera to achieve high spatial resolution can be costly or infeasible due to a high sample rate required. Furthermore, the system miniaturisation becomes an inevitable trend with the continuous development of Internet of Things and their suitability to in-situ inspection scenarios. However, a miniaturised sensor usually suffers a low spatial resolution. Addressing this challenge, the paper reports a novel Spatial Resolution Enhancement for a Thermogram (SRE4T) system to significantly improve the spatial resolution without upgrading the sensor. A high-resolution thermal image is reconstructed by fusing a sequence of low-resolution images with sub-pixel movements. To achieve the best image quality, instead of benefiting from natural movements of existing studies, this paper proposes to use a high-resolution xy translation stage to produce a sequence of controlled sub-pixel movements. The performance of the proposed system was tested on both high-end and low-end thermal imagers. Both visual and quantitative results successfully demonstrated the considerable improvement of the quality of thermal images (up to 30.5% improvement of peak signal to noise ratio). This technique allows improving the measurement accuracy of thermography inspection without upgrading sensors. It also has the potential to be applied on other imaging systems.
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    Data for the paper "Pattern Recognition of Barely Visible Impact Damage in Carbon Composites using Pulsed Thermography"
    (Cranfield University, 2021-12-14 10:50) Zhao, Yifan; Addepalli, Pavan
    Please find the attachment.
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    Data relating to: "An uncertainty quantification and aggregation framework for system performance assessment in industrial maintenance" (2020)
    (Cranfield University, 2021-02-03 11:26) Grenyer, Alex; ahmet Erkoyuncu, John; Addepalli, Pavan; Zhao, Yifan
    Excel file corresponding to data in conference paper - tables summarising variables used in the paper, calculated in MATLABImages: Figures 1-4 as in conference paperPowerPoint presentationPublished paper
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    Data relating to: "Compound uncertainty quantification and aggregation (CUQA) for reliability measurement in industrial maintenance" (2021)
    (Cranfield University, 2023-05-24 11:00) Grenyer, Alex; ahmet Erkoyuncu, John; Addepalli, Pavan; Zhao, Yifan
    Tables summarising variables used, calculated in MATLAB Images: Figures 1-4 as in the manuscript README.txt Excel file corresponding to data in the manuscript
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    Data relating to: "Identifying challenges in quantifying uncertainty: case study in infrared thermography" (2018)
    (Cranfield University, 2020-03-11 08:24) Grenyer, Alex; Addepalli, Pavan; Zhao, Yifan; Oakey, Luke; ahmet Erkoyuncu, John; Roy, Rajkumar
    Excel file corresponding to data in conference paper:'Paper tables' tab contains summary of variables used in the paper, calculated using MATLAB 'Conditions' tab contains recorded temperatures and humidity for each run read by MATLAB 'Readings' tab collates reading values for each run read by MATLAB'Run1-10' tabs contain data recorded for each run including ROI size and locationPowerPoint file: Conference presentation
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    The design and development of the miniaturised active thermography for in-situ inspection of industrial components.
    (Cranfield University, 2021-06) Du, Weixiang; Zhao, Yifan; Addepalli, Pavan
    Nondestructive testing (NDT) is a common and reliable method for the detection of surface and subsurface defects. However, due to the increasing integration and complexity of industrial components and systems, the problem of mismatching of size and volume between the existing inspection unit and the targeted object has limited the applicability of NDT techniques. Especially for geometrically intricate systems, the deployment of NDT devices for in-situ inspection has become a major challenge. Addressing the challenge of inaccessibility and inapplicability, this research proposes a miniaturised active thermography (MAT) system, featured with a small-size and low-cost thermal sensor, and a portable optical heat excitation source. A novel spatial resolution enhancement for a thermogram (SRE4T) system, which includes an infrared (IR) sensor, an XY movement stage and a super-resolution image enhancement method, is also proposed to address the low spatial resolution of the miniaturised sensor without upgrading the sensor. Moreover, dedicated data analysis approaches to evaluate defects are proposed considering the degraded signal quality. Compared with existing non-miniaturised inspection systems, the proposed system is evaluated quantitatively and qualitatively by testing samples with different materials, structures, and a variety of defects. An accessibility test is designed and conducted to evaluate the proposed system’s performance to access geometrically intricate space. The results show that the proposed system can work effectively for the degradation assessment of composite laminates, and also has enhanced accessibility and applicability of deployment for geometrically intricate systems and narrow space targets. It is observed that the data quality for composite materials seems to be more reliable and quantifiable than metal due to the relatively low sample rate of the sensor and the high thermal conductivity of the metal component. The SRE4T system can significantly improve the spatial resolution of miniaturised sensors, although it has not been used for active thermography at the present stage. The current miniaturised IR cameras feature low spatial resolution and low Signal-to-Noise Ratio, which leads to the poor performance of most of the current data analysis methods on these sensors. We propose an effective analytics framework including data processing, image processing and feature extraction to reduce the influence of noise and enhance the detectability of damage.
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    Developing an ontological framework for effective data quality assessment and knowledge modelling
    (Cranfield University, 08/11/2022) Latsou, Christina; Garcia I Minguell, Marta; Sonmez, Ayse Nur; Orteu I Irurre, Roger; Palmisano, Martin Mark; Landon-Valdez, Suresh; Erkoyuncu, John Ahmet; Addepalli, Pavan; Sibson, Jim; Silvey, Olly
    Big data has become a major challenge in the 21st century, with research being carried out to classify, mine and extract knowledge from data obtained from disparate sources. Abundant data sources with non-standard structures complicate even more the arduous process of data integration. Currently, the major requirement is to understand the data available and detect data quality issues, with research being conducted to establish data quality assessment methods. Further, the focus is to improve data quality and maturity so that early onset of problems can be predicted and handled effectively. However, the literature highlights that comprehensive analysis, and research of data quality standards and assessment methods are still lacking. To handle these challenges, this paper presents a structured framework to standardise the process of assessing the quality of data and modelling the knowledge obtained from such an assessment by implementing an ontology. The main steps of the framework are: (i) identify user’s requirements; (ii) measure the quality of data considering data quality issues, dimensions and their metrics, and visualise this information into a data quality assessment (DQA) report; and (iii) capture the knowledge from the DQA report using an ontology that models the DQA insights in a standard reusable way. Following the proposed framework, an Excel-based tool to measure the quality of data and identify emerging issues is developed. An ontology, created in Protégé, provides a standard structure to model the data quality insights obtained from the assessment, while it is frequently updated to enrich captured knowledge, reducing time and costs for future projects. An industrial case study in the context of Through life Engineering Services, using operational data of high value engineering assets, is employed to validate the proposed ontological framework and tool; the results show a well-structured guide that can effectively assess data quality and model knowledge.
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    ECBS Additional Information - Blue 1
    (Cranfield University, 2022-09-07 15:58) Addepalli, Pavan
    Additional documentation supporting EMBA assignment ECBS
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    'Quantifying uncertainty in pulsed thermography inspection by analysing the thermal diffusivity measurements of metals and composites - Dataset to reconstruct the results presented in the paper'
    (Cranfield University, 2021-08-14 10:40) Addepalli, Pavan; Zhao, Yifan; ahmet Erkoyuncu, John; Roy, Rajkumar
    'This is the underlying dataset for the paper based on which uncertainty quantification was carried out.'
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    A robust design for lifecycle cost with reliability analysis integration
    (Elsevier, 2023-07-08) Farsi, Maryam; Namoano, Bernadin; Sonmez, Ayse Nur; Addepalli, Pavan; Erkoyuncu, John Ahmet
    Maintenance, repair, and overhaul (MRO) is the most significant cost driver over a complex engineering asset lifecycle. Therefore, high-value manufacturers are required to plan MRO occurrences to optimize the overhaul cost while achieving the desired performance. This trade-off imposes a shift towards a proactive maintenance strategy. However, creating a long-term proactive maintenance plan is challenging due to uncertainties in the performance of the asset and its critical components. Hence, this paper presents a robust design framework for the lifecycle cost estimation process by integrating reliability life data analysis. The level of data availability across the lifecycle is considered. The framework is proposed based on a literature review and the Delphi method. This study highlights that the level of robustness in the lifecycle cost estimates can be achieved by continuous feedback to the design phase and to the body of knowledge over the asset lifecycle. Moreover, this study suggests that the optimization model for the trade-off between cost and reliability should fulfil safety and environmental sustainability requirements when providing a cost-effective reliability solution.