16 research outputs found
Data-driven prognostics and health management for maritime systems employing trustworthy digital twins
No full textDatasets envelope impact on marine engines prognostics and health management models accuracy
No full textThe machinery health management system required for developing maritime autonomous surface ships can be realised by employing prognostics and health management (PHM) methods. Pertinent PHM models are typically trained by using datasets corresponding to limited operating conditions and are subsequently employed to analyse a wide envelope of conditions. This study employs a PHM model that consists of a Deep Neural Network (DNN) submodel and an Auto-Regressive Integrated Moving Average (ARIMA) submodel for predicting the health indicator of a marine four-stroke engine. In specific, this study aims to quantify the accuracy of this PHM model predictions. The PHM model is developed by employing limited datasets and subsequently validated by employing extended datasets. The extended datasets reflect practical operating conditions including ambient temperature variations, stochastic degradation trends, several engine loads, and multiple simultaneous degradations. The results demonstrate that, when the testing dataset is employed, the PHM model predicts the engine exhaust valve health indicator for future time slices with high accuracy of R-squared values of 0.998. However, the model accuracy deteriorated reaching R-squared values of 0.707 when validation datasets representing extended operating envelope are used. This study's results emphasise that the PHM model accuracy is affected by the available datasets for training, necessitating the generation of trustworthy datasets and scientific methods for developing trustworthy PHM models
A framework to assure the trustworthiness of physical model-based digital twins for marine engines
No full textDigital twins (DTs) are gradually employed in the maritime industry to represent the physical systems and generate datasets, among others. However, the trustworthiness of both the digital twins and datasets must be assured. This study aims at developing a framework to assure the trustworthiness of marine engines DTs based on first-principle models. This framework considers the phases of the DT development, progressivity, and trustworthiness assurance, the latter being based on three steps, namely validation, verification, and robustness. Subsequently, a methodology is applied to develop the DT of a marine engine for healthy conditions, which is extended to represent a wider operating envelope considering systematically identified anomalies. The results demonstrate that the developed DT trustworthiness is assured, as the validation step provided errors within ±3%, the verification step provided sound trade-offs, whereas the robustness assessment step confirmed acceptable uncertainty ratios. Subsequently, the DT is employed to generate datasets required for developing a data-driven model for anomaly diagnosis, which exhibits an accuracy of 98.8% for anomaly detection, 97.6% for anomaly identification, and 90.1–91.8% for anomaly isolation. This is the first study addressing the trustworthiness of DTs for marine engines, and as such advances concepts of the fourth industrial revolution to the shipping industry
A methodology to develop and manage data-driven models for marine engine long-term health prognosis
No full textThis study proposes a novel methodology to develop and manage data-driven models for ship machinery Prognostics and Health Management (PHM). A four-stroke marine engine is investigated considering exhaust valve wear degradation. Simulated datasets are generated using a physics-based digital twin integrated with stochastic degradation models. Health indicators (HI) construction and forecast sub-models are developed, based on Multi-Layer Perceptron and Bayesian Neural Networks, respectively. Data-driven model management employs error and uncertainty metrics for deciding re-training of HI forecast sub-models, resulting in R2 increases from 0.24 to 0.89 and from 0.26 to 0.94 in Cases 1 and 2, respectively. This is the first study that integrates thermodynamic models with stochastic degradation models to develop marine engine digital twins, while also introducing data-driven model management, thus contributing to the PHM system adoption by the maritime industry
A Framework to Assure the Trustworthiness of Physical Model-Based Digital Twins for Marine Engines
No full textDigital twins (DTs) are gradually employed in the maritime industry to represent the physical systems and generate datasets, among others. However, the trustworthiness of both the digital twins and datasets must be assured. This study aims at developing a framework to assure the trustworthiness of marine engines DTs based on first-principle models. This framework considers the phases of the DT development, progressivity, and trustworthiness assurance, the latter being based on three steps, namely validation, verification, and robustness. Subsequently, a methodology is applied to develop the DT of a marine engine for healthy conditions, which is extended to represent a wider operating envelope considering systematically identified anomalies. The results demonstrate that the developed DT trustworthiness is assured, as the validation step provided errors within ±3%, the verification step provided sound trade-offs, whereas the robustness assessment step confirmed acceptable uncertainty ratios. Subsequently, the DT is employed to generate datasets required for developing a data-driven model for anomaly diagnosis, which exhibits an accuracy of 98.8% for anomaly detection, 97.6% for anomaly identification, and 90.1–91.8% for anomaly isolation. This is the first study addressing the trustworthiness of DTs for marine engines, and as such advances concepts of the fourth industrial revolution to the shipping industry
Integrated Design Process for an Elbow Joint Rehabilitation Device
No full textIn this study, we propose a design agenda for an integrated design process that considers not only industrial design factors, such as form and usability, but also engineering design factors, such as measurement and certification. Our design agenda is targeted at South Korea???s upper-limb rehabilitation context. By referring to Lawson???s design process model, we designed an elbow joint rehabilitation device based on design requirements derived from user interviews, observations, and desk research. Our design was thoughtfully crafted to consider various guides for factors like form, engineering, usability, certification, and development. As the final design outcome, a working prototype was developed and applied for patent and design award registration. We hope that our design will contribute to South Korea???s upper limb rehabilitation industry???s device guide. ?? 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG
Efficient Suppression of Defects and Charge Trapping in High Density In–Sn–Zn–O Thin Film Transistor Prepared using Microwave-Assisted Sputter
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Compressive Sensing Approaches for Condition Monitoring and Laser-scanning Based Damage Visualization
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