scholarly journals A Sensor Data Processing Algorithm for Wind Turbine Hydraulic Pitch System Diagnosis

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 33
Author(s):  
Iker Elorza ◽  
Iker Arrizabalaga ◽  
Aritz Zubizarreta ◽  
Héctor Martín-Aguilar ◽  
Aron Pujana-Arrese ◽  
...  
Keyword(s):  
Data Processing ◽  
Wind Turbine ◽  
Failure Modes ◽  
State Of The Art ◽  
Synthetic Data ◽  
Simulation Software ◽  
Sensor Data ◽  
Hydraulic Simulation ◽  
Pitch System ◽  
Model Equations

Modern wind turbines depend on their blade pitch systems for start-ups, shutdowns, and power control. Pitch system failures have, therefore, a considerable impact on their operation and integrity. Hydraulic pitch systems are very common, due to their flexibility, maintainability, and cost; hence, the relevance of diagnostic algorithms specifically targeted at them. We propose one such algorithm based on sensor data available to the vast majority of turbine controllers, which we process to fit a model of the hydraulic pitch system to obtain significant indicators of the presence of the critical failure modes. This algorithm differs from state-of-the-art, model-based algorithms in that it does not numerically time-integrate the model equations in parallel with the physical turbine, which is demanding in terms of in situ computation (or, alternatively, data transmission) and is highly susceptible to drift. Our algorithm requires only a modest amount of local sensor data processing, which can be asynchronous and intermittent, to produce negligible quantities of data to be transmitted for remote storage and analysis. In order to validate our algorithm, we use synthetic data generated with state-of-the-art aeroelastic and hydraulic simulation software. The results suggest that a diagnosis of the critical wind turbine hydraulic pitch system failure modes based on our algorithm is viable.

Approaches to Acoustic and Visual Underwater Sensing

2015 ◽  
Vol 63 (5) ◽  
Author(s):  
Philipp Woock ◽  
Thomas Stephan ◽  
Jürgen Beyerer
Keyword(s):  
Radiative Transfer ◽  
Data Processing ◽  
State Of The Art ◽  
Sensor Data ◽  
Acoustic Sensor

AbstractThis paper presents an overview of the challenges encountered in underwater sensing. Two state-of-the-art approaches are presented dealing with optic and acoustic sensor data processing. Employing suitable methods for the different modalities enables fusion of the information contained in the optic radiative transfer and acoustic reflectivity.

Condition Monitoring and Diagnostics of Wind Turbines: A Survey

2014 ◽  
Author(s):  
Paolo Pennacchi ◽  
Pietro Borghesani ◽  
Steven Chatterton ◽  
Candas Gultekin
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Failure Modes ◽  
State Of The Art ◽  
The State ◽  
Diagnostic Methods ◽  
System Architectures ◽  
Maintenance Costs

Wind energy conversion is the fastest growing source of electricity generation in the world among the other renewable energy production technologies. Whereas investment costs have decreased over years, operational and maintenance costs of wind turbines are still high, thus attracting the focus of researchers and industrial operators. Classical maintenance techniques, i.e.: run-to-failure and scheduled-preventive maintenance, are still dominant in this sector; however, condition monitoring has gained space in the wind turbine market and new diagnostic methods and techniques are continuously being proposed. Condition monitoring techniques seem the most effective tools to minimize operational and maintenance costs and reduce downtimes by early detection of faults. This paper is aimed at reviewing the state of the art of condition monitoring for horizontal axis wind turbines. After a brief introduction presenting the current trends in the market of wind energy, the paper reviews the most common failure modes of wind turbines and the traditional approach to maintenance. The core of this study details the state of the art in the field of system architectures, sensors and signal processing techniques for the diagnostic of faults in wind turbine components. Finally, some general conclusions are drawn on the overall trends in the field of condition monitoring of wind turbines.

scholarly journals G-Tric: generating three-way synthetic datasets with triclustering solutions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
João Lobo ◽  
Rui Henriques ◽  
Sara C. Madeira
Keyword(s):  
State Of The Art ◽  
Synthetic Data ◽  
Ground Truth ◽  
Real Data ◽  
Three Dimensions ◽  
Additional Advantage ◽  
Urban Dynamics ◽  
Data Generator ◽  
Real World Datasets ◽  
Synthetic Datasets

Abstract Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

A Simulation Software for the Evaluation of Vulnerabilities in Reputation Management Systems

2021 ◽  
Vol 37 (1-4) ◽  
pp. 1-30
Author(s):  
Vincenzo Agate ◽  
Alessandra De Paola ◽  
Giuseppe Lo Re ◽  
Marco Morana
Keyword(s):  
Quality Of Service ◽  
Distributed Systems ◽  
State Of The Art ◽  
Simulation Software ◽  
Reputation Management ◽  
Management Systems ◽  
Security Attacks ◽  
Vulnerability Evaluation ◽  
Multi Agent

Multi-agent distributed systems are characterized by autonomous entities that interact with each other to provide, and/or request, different kinds of services. In several contexts, especially when a reward is offered according to the quality of service, individual agents (or coordinated groups) may act in a selfish way. To prevent such behaviours, distributed Reputation Management Systems (RMSs) provide every agent with the capability of computing the reputation of the others according to direct past interactions, as well as indirect opinions reported by their neighbourhood. This last point introduces a weakness on gossiped information that makes RMSs vulnerable to malicious agents’ intent on disseminating false reputation values. Given the variety of application scenarios in which RMSs can be adopted, as well as the multitude of behaviours that agents can implement, designers need RMS evaluation tools that allow them to predict the robustness of the system to security attacks, before its actual deployment. To this aim, we present a simulation software for the vulnerability evaluation of RMSs and illustrate three case studies in which this tool was effectively used to model and assess state-of-the-art RMSs.

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

scholarly journals Integrated sensor data processing for occupancy detection in residential buildings

2021 ◽  
Vol 237 ◽  
pp. 110810
Author(s):  
Chenli Wang ◽  
Jun Jiang ◽  
Thomas Roth ◽  
Cuong Nguyen ◽  
Yuhong Liu ◽  
...  
Keyword(s):  
Data Processing ◽  
Residential Buildings ◽  
Sensor Data ◽  
Integrated Sensor ◽  
Occupancy Detection

Design of Control System for Hydraulically Vibrated Processing Tomato Fruit Seedling Separation Device

2014 ◽  
Vol 651-653 ◽  
pp. 693-696
Author(s):  
Li Hong Wang ◽  
Rong Qing Liang ◽  
Cheng Song Li ◽  
Za Kan ◽  
Jin Wei Qin
Keyword(s):  
Tomato Fruit ◽  
Simulation Software ◽  
Signal Frequency ◽  
System Control ◽  
Frequency Range ◽  
Hydraulic Simulation ◽  
Separation Device ◽  
Processing Tomato ◽  
Input Signal Frequency ◽  
Set Up

Eccentric style processing tomato fruit seeding separation device exist high machining and assembly precision or other issues. In order to solve this problem, the mode of vibration of hydraulic replaced the eccentric style to drive the fruit seedling separation roller to separate processing tomato effectively. To facilitate adjustment of the hydraulic system, a kind of control circuit PLC as the core was designed according to the actual production requirements. PLC and other elements were selected. The system control signal frequency was initially set up as 1~5 HZ, within the frequency range hydraulic simulation software was used to simulate and analyze the hydraulic vibration system. The result shows that the system rams steady when the input signal frequency range was 1~5HZ.

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