The Design and Ground Test Verification of an Energy-Efficient Wireless System for the Fatigue Monitoring of Wind Turbine Blades Based on Bistable Piezoelectric Energy Harvesting.

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.

Evaluation of a Deep Learning-Based Index for Prognosis of a Vessel's Propeller-Hull Degradation.

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.

A Combined Semi-Supervised Deep Learning Method for Oil Leak Detection in Pipelines Using IIoT at the Edge.

Pipelines are integral components for storing and transporting liquid and gaseous petroleum products. Despite being durable structures, ruptures can still occur, resulting not only in financial losses and energy waste but, most importantly, in immeasurable environmental disasters and possibly in human casualties. The objective of the ESTHISIS project is the development of a low-cost and efficient wireless sensor system for the instantaneous detection of leaks in metallic pipeline networks transporting liquid and gaseous petroleum products in a noisy industrial environment. The implemented methodology is based on processing the spectrum of vibration signals appearing in the pipeline walls due to a leakage effect and aims to minimize interference in the piping system. It is intended to use low frequencies to detect and characterize leakage to increase the range of sensors and thus reduce cost. In the current work, the smart sensor system developed for signal acquisition and data analysis is briefly described. For this matter, two leakage detection methodologies are implemented. A 2D-Convolutional Neural Network (CNN) model undertakes supervised classification in spectrograms extracted by the signals acquired by the accelerometers mounted on the pipeline wall. This approach allows us to supplant large-signal datasets with a more memory-efficient alternative to storing static images. Second, Long Short-Term Memory Autoencoders (LSTM AE) are employed, receiving signals from the accelerometers, and providing an unsupervised leakage detection solution.

A Deep Learning-Based Fault Detection Model for Optimization of Shipping Operations and Enhancement of Maritime Safety.

The ability to exploit data for obtaining useful and actionable information and for providing insights is an essential element for continuous process improvements. Recognizing the value of data as an asset, marine engineering puts data considerations at the core of system design. Used wisely, data can help the shipping sector to achieve operating cost savings and efficiency increase, higher safety, wellness of crew rates, and enhanced environmental protection and security of assets. The main goal of this study is to develop a methodology able to harmonize data collected from various sensors onboard and to implement a scalable and responsible artificial intelligence framework, to recognize patterns that indicate early signs of defective behavior in the operational state of the vessel. Specifically, the methodology examined in the present study is based on a 1D Convolutional Neural Network (CNN) being fed time series directly from the available dataset. For this endeavor, the dataset undergoes a preprocessing procedure. Aspiring to determine the effect of the parameters composing the networks and the values that ensure the best performance, a parametric inquiry is presented, determining the impact of the input period and the degree of degradation that our models identify adequately. The results provide an insightful picture of the applicability of 1D-CNN models in performing condition monitoring in ships, which is not thoroughly examined in the maritime sector for condition monitoring. The data modeling along with the development of the neural networks was undertaken with the Python programming language.

Eco-Friendly Lead-Free Solder Paste Printing via Laser-Induced Forward Transfer for the Assembly of Ultra-Fine Pitch Electronic Components.

Current challenges in printed circuit board (PCB) assembly require high-resolution deposition of ultra-fine pitch components (<0.3 mm and <60 µm respectively), high throughput and compatibility with flexible substrates, which are poorly met by the conventional deposition techniques (e.g., stencil printing). Laser-Induced Forward Transfer (LIFT) constitutes an excellent alternative for assembly of electronic components: it is fully compatible with lead-free soldering materials and offers high-resolution printing of solder paste bumps (<60 µm) and throughput (up to 10,000 pads/s). In this work, the laser-process conditions which allow control over the transfer of solder paste bumps and arrays, with form factors in line with the features of fine pitch PCBs, are investigated. The study of solder paste as a function of donor/receiver gap confirmed that controllable printing of bumps containing many microparticles is feasible for a gap < 100 µm from a donor layer thickness set at 100 and 150 µm. The transfer of solder bumps with resolution < 100 µm and solder micropatterns on different substrates, including PCB and silver pads, have been achieved. Finally, the successful operation of a LED interconnected to a pin connector bonded to a laser-printed solder micro-pattern was demonstrated.