| 1. |
- Eleftheroglou, Nick, et al.
(author)
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Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the Lithium-Polymer batteries of unmanned aerial vehicles with uncertainty quantification
- 2019
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In: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 254
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Journal article (peer-reviewed)abstract
- In this paper, the discharge voltage is utilized as a critical indicator towards the probabilistic estimation of the Remaining Useful Life until the End-of-Discharge of the Lithium-Polymer batteries of unmanned aerial vehicles. Several discharge voltage histories obtained during actual flights constitute the in-house developed training dataset. Three data-driven prognostic methodologies are presented based on state-of-the-art as well as innovative mathematical models i.e. Gradient Boosted Trees, Bayesian Neural Networks and Non-Homogeneous Hidden Semi Markov Models. The training and testing process of all models is described in detail. Remaining Useful Life prognostics in unseen data are obtained from all three methodologies. Beyond the mean estimates, the uncertainty associated with the point predictions is quantified and upper/lower confidence bounds are also provided. The Remaining Useful Life prognostics during six random flights starting from fully charged batteries are presented, discussed and the pros and cons of each methodology are highlighted. Several special metrics are utilized to assess the performance of the prognostic algorithms and conclusions are drawn regarding their prognostic capabilities and potential.
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| 2. |
- Eleftheroglou, Nick, et al.
(author)
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Real time Diagnostics and Prognostics of UAV Lithium-Polymer Batteries
- 2019
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In: Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019. - : Prognostics and Health Management Society.
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Conference paper (other academic/artistic)abstract
- This paper examines diagnostics and prognostics of Lithium-Polymer (Li-Po) batteries for unmanned aerial vehicles (UAVs). Several discharge voltage histories obtained during actual indoor flights constitute the training data for a data-driven approach, utilizing the Non-Homogenous Hidden Semi Markov model (NHHSMM). NHHSMM is a suitable candidate as it has a rich mathematical structure, which is capable of describing the discharge process of Li-Po batteries and providing diagnostic and prognostic measures. Diagnostics and prognostics in unseen data are obtained and compared with the actual remaining flight time in order to validate the effectiveness of the selected model.
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| 3. |
- Kanellakis, Christoforos, et al.
(author)
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Towards Autonomous Surveying of Underground Mine using MAVs
- 2019
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Conference paper (peer-reviewed)abstract
- Micro Aerial Vehicles (MAVs) are platforms that received great attention during the last decade. Recently, the mining industry has been considering the usage of aerial autonomous platforms in their processes. This article initially investigates potential application scenarios for this technology in mining. Moreover, one of the main tasks refer to surveillance and maintenance of infrastructure assets. Employing these robots for underground surveillance processes of areas like shafts, tunnels or large voids after blasting, requires among others the development of elaborate navigation modules. This paper proposes a method to assist the navigation capabilities of MAVs in challenging mine environments, like tunnels and vertical shafts. The proposed method considers the use of Potential Fields method, tailored to implement a sense-and-avoid system using a minimal ultrasound-based sensory system. Simulation results demonstrate the effectiveness of the proposed strategy.
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| 4. |
- Karvelis, Petros, et al.
(author)
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Short Time Wind Forecasting with Uncertainty
- 2019
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In: The 10th International Conference on Information, Intelligence, Systems and Applications, 15-17 July 2019, Patras, Greece. - : IEEE. ; , s. 511-518
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Conference paper (peer-reviewed)abstract
- Forecasting the weather and especially the wind is important for a number of applications like wind farms or for maritime operations. Nowadays machine learning techniques are becoming more reliable and robust for forecasting due to the fact that a plethora of available datasets exist. However, forecasts for shorter time horizon less than two hour is not reliable due to the frequent wind fluctuations. Nevertheless, the need for algorithms that can have a small memory and cpu footprint is needed for hardware e.g. microcontrollers that are on board of vessels. In this manuscript a method for short time wind forecasting is proposed and scaled for a microcontroller. The method also computes prediction intervals with a certain probability. Our method was tested using real data recorded from a weather station on board of a ship conducting trips across the Aegean Sea (Greece).
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| 5. |
- Tsoumas, I. P., et al.
(author)
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Analytical Investigation of the Transient Switch-On Current of Direct-On-Line Induction Motors
- 2019
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In: Proceedings. - : IEEE. ; , s. 3667-3672
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Conference paper (other academic/artistic)abstract
- This work presents an exact analytical equation for the calculation of the switch-on current of induction motors considering the general case of unequal stator and rotor parameters. Based on this analytical solution of the differential equations, the influence of the motor parameters on the amplitude and the duration of the electrical transient is investigated. The exact knowledge of the transient current is necessary for the assessment of its potential impact on transient motor current signature analysis methods for fault diagnosis.
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