Prisma Electronics (Greece)

A wireless monitoring system based on piezoelectric energy harvesting (PEH) is presented to provide fatigue data of wind turbine blades in operation. The system comprises three subsystems, each respectively providing the following functions: (i) the conversion of mechanical to electric energy by exploiting the bistable vibration of a composite beam with piezoelectric patches in post-buckling, (ii) harvesting the converted energy by means of a modified, commercial, off-the-shelf (COTS) circuit to feed a LiPo battery and (iii) the battery-powered acquisition and wireless transmission of sensory signals to the cloud to be elaborated upon by the end-user. The system was verified with ground tests under representative operation conditions, which demonstrated the fulfillment of the design requirements. The measurements indicated that the system provided 23% of the required power for fully autonomous operation when subjected to white noise base excitation of 1 g acceleration in the range of 1-20 Hz.

The development and experimental validation of a Machine Learning (ML) vibration based Structural Health Monitoring (SHM) prototype system for composite aerostructures operating under uncertainty is presented. Following initial training, it is intended to operate autonomously, in almost real-time, without interrupting normal operation, relying on a limited number of acceleration and strain measurements from properly selected locations on the aerostructure. It is equipped with a multi-level information fusion methodology comprising multiple sensor technologies and diagnostic algorithms for optimized SHM performance in terms of damage detection and characterization. Through its flexible software, the system provides multiple options of ML based SHM methods targeting wide applicability to various types of structures. The system is validated based on numerous experiments with healthy and damaged full-scale composite bonded spars operating under multiple uncertainty factors. Its diagnostic performance indicates that the current progress in hardware and robust vibration-based Machine Learning algorithms may lead to compact, effective, and low cost SHM systems for composite aerostructures under normal operating conditions.

Vessels frequently encounter challenging marine conditions that expose the propeller-hull to corrosive water and marine fouling. These challenges necessitate innovative approaches to optimize propeller-hull performance. This study aims to assess a method for predicting propeller-hull degradation. The proposed solution revolves around an innovative Key Performance Indicator (KPI) based on Artificial Neural Networks (ANNs). Our objective is to validate the findings; thus, a thorough comparison is conducted between the proposed method and the baseline solution derived from the ISO-19030. Emphasis is placed on determining the optimal parameters for computing the KPI, which involves applying various features, filters, and pre-processing techniques. The proposed method is tested on real data collected by an Internet of Things (IoT) system installed in different types of vessels. Four distinct experiments with ANNs are conducted. Results demonstrate that the ANN-based indicator offers greater accuracy in predicting propeller-hull degradation compared to the baseline method. Additionally, it is demonstrated that selecting a diverse set of features and implementing consistent filtering and preprocessing techniques enhance the performance of the traditional indicator. The utilization of Deep Learning (DL) in the maritime industry is of great significance, as it enables a comprehensive and dynamic assessment of predictive maintenance of the propeller-hull. The DL index method holds potential for diverse maintenance applications, providing a holistic platform with anticipated environmental and financial benefits.

Liquefaction of granular materials in a rectangular container has been experimentally investigated using NTUA’s School of Naval Architecture and Marine Engineering “shaking table” facility. Two different materials (sand and olive pomace) in several moisture content scenarios were tested. Harmonic forcing in a range of frequencies and amplitudes has been applied. The intention was to develop some qualitative understanding on how liquefaction comes about for materials of different properties; and also how the phenomenon relates with the duration and intensity of the excitation. The two materials presented substantially different behaviour, interpreted to be due to differences in moisture’s diffusion in material’s body and in their specific gravity. In a parallel study, was investigated the impact of liquefaction to a bulk carrier’s stability by using commercial design software. Different cases of cargo stowage and distribution in the holds were examined. This study confirms that homogeneous cargo loading can lead to substantial loss of stability after cargo liquefaction and that alternating or suitable inhomogeneous loading is often preferable. The current paper is an updated and improved version of a paper presented in the International Ship Stability Workshop, held in Brest in 2013 [16].

A NON-LINEAR PIEZOELECTRIC ENERGY HARVESTING SETUP FOR THE CONTINUOUS POWER SUPPLY OF A WIND TURBINE MONITORING SYSTEM - 10th ECCOMAS Thematic Conference on Smart Structures and Materials - 03-05 July 2023, Patras, Greece

Recent advances in computer vision brought us closer to the levels of human vision without the need for high computing power. Developments at the algorithmic level are complemented by the wide availability of visual content data necessary for the training of computer vision, as the ever-increasing digitization of society is accompanied by the large-scale production and sharing of digital data. Working on that direction, the e-Vision project aims at the development of an assistive system for people with a reduced vision which enables the use of an array of sensors (camera and headset) mounted on special glasses to transfer the field of vision to their mobile device. The system, in turn, informs the users through the acoustic diode about the object or person in front of them. In this work, the design and development of the sensor system adapted to glasses that will support communication with the application of e-vision via mobile phone are presented. Sensor data are being transmitted in real-time, through different wireless communication approaches. Details regarding initial requirements and feedback from the pilot testing are also provided. It is shown that the system can support its mission for the improvement of the autonomy of the visually impaired.

International audience

The Fast TracKer (FTK) is an extremely powerful and very compact processing unit, essential for efficient Level 2 trigger selection in future high-energy physics experiments at the LHC. FTK employs Associative Memories (AM) to perform pattern recognition; input and output data are transmitted over serial links at 2 Gbit/s to reduce routing congestion at the board level.Prototypes of the AM chip and of the AM board have been manufactured and tested, in preparation of the imminent design of the final version.