A novel information-gap technique to assess reliability of neural network-based damage detection

2006 ◽  
Vol 293 (1-2) ◽  
pp. 96-111 ◽  
Author(s):  
S.G. Pierce ◽  
K. Worden ◽  
G. Manson
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Gap Technique ◽  
Information Gap

Damage Detection of Offshore Platform Mooring Line Using Artificial Neural Network

2018 ◽  
Author(s):  
Djoni E. Sidarta ◽  
Jim O’Sullivan ◽  
Ho-Joon Lim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Total Mass ◽  
Offshore Platform ◽  
Mooring Line ◽  
Ann Model ◽  
Station Keeping ◽  
Mooring Lines ◽  
Artificial Neural

Station-keeping using mooring lines is an important part of the design of floating offshore platforms, and has been used on most types of floating platforms, such as Spar, Semi-submersible, and FPSO. It is of great interest to monitor the integrity of the mooring lines to detect any damaged and/or failures. This paper presents a method to train an Artificial Neural Network (ANN) model for damage detection of mooring lines based on a patented methodology that uses detection of subtle shifts in the long drift period of a moored floating vessel as an indicator of mooring line failure, using only GPS monitoring. In case of an FPSO, the total mass or weight of the vessel is also used as a variable. The training of the ANN model employs a back-propagation learning algorithm and an automatic method for determination of ANN architecture. The input variables of the ANN model can be derived from the monitored motion of the platform by GPS (plus vessel’s total mass in case of an FPSO), and the output of the model is the identification of a specific damaged mooring line. The training and testing of the ANN model use the results of numerical analyses for a semi-submersible offshore platform with twenty mooring lines for a range of metocean conditions. The training data cover the cases of intact mooring lines and a damaged line for two selected adjacent lines. As an illustration, the evolution of the model at various training stages is presented in terms of its accuracy to detect and identify a damaged mooring line. After successful training, the trained model can detect with great fidelity and speed the damaged mooring line. In addition, it can detect accurately the damaged mooring line for sea states that are not included in the training. This demonstrates that the model can recognize and classify patterns associated with a damaged mooring line and separate them from patterns of intact mooring lines for sea states that are and are not included in the training. This study demonstrates a great potential for the use of a more general and comprehensive ANN model to help monitor the station keeping integrity of a floating offshore platform and the dynamic behavior of floating systems in order to forecast problems before they occur by detecting deviations in historical patterns.

scholarly journals A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Sergio V. Farias ◽  
Osamu Saotome ◽  
Haroldo F. Campos Velho ◽  
Elcio H. Shiguemori
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Lamb Wave ◽  
Learning Strategy ◽  
Damage Identification ◽  
Particle Collision ◽  
Learning Approaches ◽  
Propagation Methods ◽  
Artificial Neural Network Ann ◽  
Multiple Particle

A critical task of structural health monitoring is damage detection and localization. Lamb wave propagation methods have been successfully applied for damage identification in plate-like structures. However, Lamb wave processing is still a challenging task due to its multimodal and dispersive characteristics. To address this issue, data-driven machine learning approaches as artificial neural network (ANN) have been proposed. However, the effectiveness of ANN can be improved based on its architecture and the learning strategy employed to train it. The present paper proposes a Multiple Particle Collision Algorithm (MPCA) to design an optimum ANN architecture to detect and locate damages in plate-like structures. For the first time in the literature, the MPCA is applied to find damages in plate-like structures. The present work uses one piezoelectric transducer to generate Lamb wave signals on an aluminum plate structure and a linear array of four transducers to capture the scattered signals. The continuous wavelet transform (CWT) processes the captured signals to estimate the time-of-flight (ToF) that is the ANN inputs. The ANN output is the damage spatial coordinates. In addition to MPCA optimization, this paper uses a quantitative entropy-based criterion to find the best mother wavelet and the scale values. The presented experimental results show that MPCA is capable of finding a simple ANN architecture with good generalization performance in the proposed damage localization application. The proposed method is compared with the 1-dimensional convolutional neural network (1D-CNN). A discussion about the advantages and limitations of the proposed method is presented.

scholarly journals Improving English Language Speaking Skills Using “Absyak” On-Line Learning Model for Second Semester in Higher Education

2020 ◽  
Vol 3 (2) ◽  
pp. 684-694
Author(s):  
Abd. Syakur ◽  
Sugirin Sugirin ◽  
Margana Margana ◽  
Esti Junining ◽  
Yulianto Sabat
Keyword(s):  
Online Learning ◽  
Teaching And Learning ◽  
English Language ◽  
Student Activities ◽  
Gap Technique ◽  
Information Gap ◽  
On Line ◽  
On Line Learning ◽  
Information Gaps ◽  
Speaking Skills

This study aims to improve students' speaking skills. The research sample consisted of 30 students (20 female students and 8 male students) all of whom were studying English, the majority of the sample were second semester students, the form of this study was to analyze (1) teaching and learning plans to speak through the task of information gaps that exist in "Absyak" on-line learning media websites, (2) the implementation of teaching speaking through structured tasks and information gaps with "absyak" on-line learning media websites, (3) teaching teaching evaluation systems through the task gap "absyak" information media website-based online learning, (4) improvement of student speaking using the information gap task on "absyak" website-based on-line learning. This is a classroom action research that takes place in 2 cycles. The subjects of the study were the second semester students of Surabaya Pharmacy Academy, Regular Class A consisting of 30 students in the academic year 2019/2020. Data collected by tests, observation notes and documentation. Data were analyzed descriptively qualitatively. The results showed that learning that was designed using the information gap technique with "absyak". Website-based online learning with cycle procedures. Cycle I results are still not optimal, then continue to the second cycle. In cycle II the results are maximum. Student activities increase every cycle II for learning, student activity, performance indicators, learning processes, completeness scores increase, decrease the number of unfinished and success indicators. Based on the results of research 11 which has been conducted 11 with 1 application of information gap techniques and "absyak" on-line learning website in learning Speaking in the second semester students of the Surabaya pharmacy academy that there is a significant increase in speaking skills from the results of learning achievements of cycle I and cycle II. This means that the students' speaking skills from the stages are increasingly increasing and succeeding well.

Damage Detection of Composite Materials Using Data Fusion With Deep Neural Networks

2020 ◽  
Author(s):  
Shweta Dabetwar ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Composite Materials ◽  
Data Fusion ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Mixed Data ◽  
Image Encoding ◽  
Using Data

Abstract Composite materials have enormous applications in various fields. Thus, it is important to have an efficient damage detection method to avoid catastrophic failures. Due to the existence of multiple damage modes and the availability of data in different formats, it is important to employ efficient techniques to consider all the types of damage. Deep neural networks were seen to exhibit the ability to address similar complex problems. The research question in this work is ‘Can data fusion improve damage classification using the convolutional neural network?’ The specific aims developed were to 1) assess the performance of image encoding algorithms, 2) classify the damage using data from separate experimental coupons, and 3) classify the damage using mixed data from multiple experimental coupons. Two different experimental measurements were taken from NASA Ames Prognostic Repository for Carbon Fiber Reinforced polymer. To use data fusion, the piezoelectric signals were converted into images using Gramian Angular Field (GAF) and Markov Transition Field. Using data fusion techniques, the input dataset was created for a convolutional neural network with three hidden layers to determine the damage states. The accuracies of all the image encoding algorithms were compared. The analysis showed that data fusion provided better results as it contained more information on the damages modes that occur in composite materials. Additionally, GAF was shown to perform the best. Thus, the combination of data fusion and deep neural network techniques provides an efficient method for damage detection of composite materials.

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