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Using Neural Network Approaches to Detect Mooring Line Failure

Amir Muhammed Saad, Florian Schopp, Rodrigo A. Barreira, Ismael H. F. Santos, Eduardo A. Tannuri, Edson S. Gomi, Anna H. Reali Costa
2021 IEEE Access  
INDEX TERMS Mooring line failure, failure detection, machine learning, neural networks, floating production storage and offloading.  ...  The current approaches used to monitor mooring lines are inefficient as line tension sensors are expensive to install, maintain, and have durability problems.  ...  A convolutional neural network (CNN) based binary classifier was implemented for mooring line failure detection in [11] .  ... 
doi:10.1109/access.2021.3058592 fatcat:xfldcfp2ajeanplmip6vbsgwnm

Assessment of Wind Turbine Aero-Hydro-Servo-Elastic Modelling on the Effects of Mooring Line Tension via Deep Learning

Zi Lin, Xiaolei Liu
2020 Energies  
Among different failure mechanics, an excessive mooring line tension is one of the most essential factors contributing to mooring failure.  ...  Even advanced sensing offers an effective way of failure detections, but it is still difficult to comprehend why failures happened.  ...  way of collecting data and detecting failures, but it is unable to explain the inherent driven force on these failures.  ... 
doi:10.3390/en13092264 fatcat:fhuyicmf2nahfpot3v5rfcmnha

Evaluation of Dynamic Tensions of Single Point Mooring System under Random Waves with Artificial Neural Network

Peng Li, Conglin Jin, Gang Ma, Jie Yang, Liping Sun
2022 Journal of Marine Science and Engineering  
Finally, taking the motion of FPSO which is not encountered by LSTM neural network model as input, we predict the mooring line tension with this model.  ...  Real-time monitoring of the mooring safety of floating structures is of great significance to their production operations.  ...  [19] studied a damage detection method of tension leg platform mooring line based on deep neural network. Sidarta et al.  ... 
doi:10.3390/jmse10050666 fatcat:6narq3biqbbhtfuhtcem2wz2pm

Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network

Md Arifuzzaman, Md. Alhaz Uddin, Mohammed Jameel, Mohammad Towhidur Rahman Bhuiyan
2022 Applied Sciences  
The backpropagation technique depletes feed-forward neural networks, allowing the network to be trained.  ...  Although FEM is an extremely laborious and time-consuming process for predicting platform responses using hydrodynamic loads, artificial neural networks (ANNs) can predict the response quickly, as required  ...  Acknowledgments: The authors extend their appreciation to the Deanship of Scientific Research (DSR) at King Faisal University (KFU) for funding this work through research grant number GRANT329.  ... 
doi:10.3390/app12125954 fatcat:sac5xvrmureszlmvksuu5fshgu

Fault detection and replacement of a temperature sensor in a cement rotary kiln

Tiago Matias, Dulce Gabriel, Francisco Souza, Rui Araujo, J. Costa Pereira
2013 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA)  
Exploring the analytical redundancy that usually exist in industrial processes, the proposed methodology uses a neural network trained using an online sequential extreme learning machine to online construct  ...  Using the error between the measured and estimated temperatures, faults in the measurement can be detected and thus the replacement of the measured temperatures by the estimated output is made.  ...  The model will be constructed using a single hidden-layer feedforward neural network trained with a OS-ELM approach and the online scaled variables.  ... 
doi:10.1109/etfa.2013.6648038 dblp:conf/etfa/MatiasGSAP13 fatcat:rck7jbzjm5aptcsylzdngvw3li

Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines

Pere Marti-Puig, Alejandro Blanco-M., Moisès Serra-Serra, Jordi Solé-Casals
2021 Applied Sciences  
In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented.  ...  The experimental results indicate that this methodology leads to the detection of mismatches in the stages of the system's failure, thus making it possible to schedule the maintenance operation before  ...  Acknowledgments: The authors would like to thank the Smartive company ( for providing the data used in the experimental part.  ... 
doi:10.3390/app11020590 fatcat:r2jai7awavbcvmgd2j2inwi52m

Fault Diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: a Mixed Model and Signal-based Approach [article]

Yichao Liu and Riccardo Ferrari and Ping Wu and Xiaoli Jiang and Sunwei Li and Jan-Willem van Wingerden
2020 arXiv   pre-print
In addition, a signal-based scheme is established, within the proposed architecture, for detecting and isolating two representative mooring lines faults.  ...  In addition, the advantages and limitations are discussed by comparing its fault detection to the results delivered by other approaches.  ...  Signal-based fault diagnosis for the mooring lines Detecting and isolating faults in 2 which are associated with the mooring system may prove to be difficult using the model-based approach of previous  ... 
arXiv:2007.01708v1 fatcat:frqyn5b2eremrikp3i524h2zre

Fault diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: A mixed model and signal-based approach

Yichao Liu, Riccardo Ferrari, Ping Wu, Xiaoli Jiang, Sunwei Li, Jan-Willem van Wingerden
2020 Renewable Energy  
In addition, a signal-based scheme is established, within the proposed architecture, for detecting and isolating two representative mooring lines faults.  ...  In addition, the advantages and limitations are discussed by comparing its fault detection to the results delivered by other approaches.  ...  Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper  ... 
doi:10.1016/j.renene.2020.06.130 fatcat:ppbkrgrmnbgtfpt6mrx6fzdikq

Attitude estimation using horizon detection in thermal images

Adrian Carrio, Hriday Bavle, Pascual Campoy
2018 International Journal of Micro Air Vehicles  
For this, a novel Convolutional Neural Network architecture has been trained using measurements from an inertial navigation system.  ...  The first method consists of a novel approach to estimate the line that best fits the horizon in a thermal image.  ...  Acknowledgements The authors would like to thank Prof Miguel Marchamalo and the ETSI de Caminos, Canales y Puertos at Universidad Polite´cnica de Madrid for the generous collaboration in obtaining the  ... 
doi:10.1177/1756829318804761 fatcat:ppbvx5p4avdmvp6xlp46it5frq

A Survey of Fault Prediction and Location Methods in Electrical Energy Distribution Networks

Rahman Dashti, Mohammad Daisy, Hamid Mirshekali, Hamid Reza Shaker, Mahmood Hosseini Aliabadi
2021 Measurement (London)  
In addition, it includes an up to date review of the methods for distance measurement and fault location considering different network types (AC/DC), presence of DG, communication and automation standards  ...  To this end, the existing methods and views in the context of fault prediction are reviewed first; then, fault location is investigated.  ...  Typically, the machine learning approach and historical electrical network data are used to predict the risk of failure for the distribution network ingredients.  ... 
doi:10.1016/j.measurement.2021.109947 fatcat:sl2gocydcvd7hj33ep4rat2cpy

Digital tools for floating offshore wind turbines (FOWT): A state of the art

Alexandra Ciuriuc, José Ignacio Rapha, Raúl Guanche, José Luis Domínguez-García
2022 Energy Reports  
This fact is of special interest on maintenance, since the early detection of failures or malfunctions lead to reduced costly corrective maintenance.  ...  Finally, the review paper provides an analysis of existing gaps, needs and challenges of the sector to provide guides on research and innovation to foster offshore wind sector.  ...  The research leading to these results has received funding from the European Union's H2020 Programme under Grant Agreement n • 815083 -Corewind  ... 
doi:10.1016/j.egyr.2021.12.034 fatcat:br5355cvnjhqpal4i2hikk6bdy

Techniques of Vibration Signature Analysis
IJARCCE - Computer and Communication Engineering

Sumit Kumar Sar, Dr. Ramesh Kumar
2015 IJARCCE  
This paper presents recent developments of techniques, based on vibration signature analysis to detect the fault in a machine.  ...  This paper shows vibration signature analysis implemented to detect fault in various machineries such as gear, rolling element bearing, journal bearing, induction motor, centrifugal pump and beam.  ...  Vahid-Alizadeh analysis of Vibration signature is applied to detect fault (cavitations) in a centrifugal pump using a neural network system.  ... 
doi:10.17148/ijarcce.2015.4359 fatcat:tzk7k75xbvdapc6nbrc2y644da

A soft computing method for detecting lifetime building thermal insulation failures

Javier Sedano, Leticia Curiel, Emilio Corchado, Enrique de la Cal, José R. Villar
2010 Integrated Computer-Aided Engineering  
The detection of thermal insulation failures in buildings in operation responds to the challenge of improving building energy efficiency.  ...  It also applies system identification theory and neural networks for modeling the thermal dynamics of the building.  ...  Acknowledgements We would like to extend our thanks to Phd  ... 
doi:10.3233/ica-2010-0337 fatcat:q62kljbzxjf75fbokqgssqb3ye


F. Arce, E. Zamora, G. Hernández, H. Sossa
2017 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this paper, we propose a methodology based on so-called Ellipsoidal Neural Networks with Dendritic Processing (ENNDPs) as a new approach to provide a solution to this important problem.  ...  Most of approaches used until now to face this problem combine conventional image processing, image analysis and pattern classification techniques.  ...  Sossa would like to acknowledge UPIITA-IPN and CIC-IPN, respectively, for the support provided in carrying out this research.  ... 
doi:10.5194/isprs-annals-iv-4-w3-13-2017 fatcat:wfcb4ajn4jdutpk3r3fd2vzvfa

Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning

George Loukas, Tuan Vuong, Ryan Heartfield, Georgia Sakellari, Yongpil Yoon, Diane Gan
2018 IEEE Access  
As input, it uses data captured in realtime that relate to both cyber and physical processes, which it feeds as time series data to a neural network architecture.  ...  Using detection latency as the criterion, we have developed a mathematical model to determine when computation offloading is beneficial given parameters related to the operation of the network and the  ...  Along similar lines, Moore et al.  ... 
doi:10.1109/access.2017.2782159 fatcat:njik42b4qfflta7tupl3y37xnq
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