Detection of Damage in Composite Materials Using Classification and Novelty Detection Methods

2013 ◽  
pp. 203-212
Author(s):  
Ramin Amali ◽  
Bradley J. Hughes
Keyword(s):  
Composite Materials ◽  
Novelty Detection ◽  
Detection Methods

scholarly journals Defects and uncertainties of adhesively bonded composite joints

2021 ◽  
Vol 3 (9) ◽  
Author(s):  
Sadik Omairey ◽  
Nithin Jayasree ◽  
Mihalis Kazilas
Keyword(s):  
Composite Materials ◽  
Composite Structures ◽  
Production Efficiency ◽  
Adhesive Bonding ◽  
Detection Methods ◽  
Bonded Joints ◽  
Adhesively Bonded ◽  
Adhesively Bonded Joints ◽  
Wide Range ◽  
Bonded Composite

AbstractThe increasing use of fibre reinforced polymer composite materials in a wide range of applications increases the use of similar and dissimilar joints. Traditional joining methods such as welding, mechanical fastening and riveting are challenging in composites due to their material properties, heterogeneous nature, and layup configuration. Adhesive bonding allows flexibility in materials selection and offers improved production efficiency from product design and manufacture to final assembly, enabling cost reduction. However, the performance of adhesively bonded composite structures cannot be fully verified by inspection and testing due to the unforeseen nature of defects and manufacturing uncertainties presented in this joining method. These uncertainties can manifest as kissing bonds, porosity and voids in the adhesive. As a result, the use of adhesively bonded joints is often constrained by conservative certification requirements, limiting the potential of composite materials in weight reduction, cost-saving, and performance. There is a need to identify these uncertainties and understand their effect when designing these adhesively bonded joints. This article aims to report and categorise these uncertainties, offering the reader a reliable and inclusive source to conduct further research, such as the development of probabilistic reliability-based design optimisation, sensitivity analysis, defect detection methods and process development.

scholarly journals Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3536
Author(s):  
Jakub Górski ◽  
Adam Jabłoński ◽  
Mateusz Heesch ◽  
Michał Dziendzikowski ◽  
Ziemowit Dworakowski
Keyword(s):  
Novelty Detection ◽  
Feature Space ◽  
Detection Methods ◽  
Data Sets ◽  
Similar Distribution ◽  
Detection Algorithms ◽  
Rotary Machinery ◽  
Detection Approach ◽  
Input Reconstruction ◽  
Quantitative Indicators

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

Learning Factorial Codes by Predictability Minimization

1992 ◽  
Vol 4 (6) ◽  
pp. 863-879 ◽  
Author(s):  
Jürgen Schmidhuber
Keyword(s):  
Unsupervised Learning ◽  
Supervised Learning ◽  
General Principle ◽  
Novelty Detection ◽  
The Other ◽  
Detection Methods ◽  
Local Algorithm ◽  
The Novel ◽  
Abstract Concepts ◽  
Internal Representations

I propose a novel general principle for unsupervised learning of distributed nonredundant internal representations of input patterns. The principle is based on two opposing forces. For each representational unit there is an adaptive predictor, which tries to predict the unit from the remaining units. In turn, each unit tries to react to the environment such that it minimizes its predictability. This encourages each unit to filter "abstract concepts" out of the environmental input such that these concepts are statistically independent of those on which the other units focus. I discuss various simple yet potentially powerful implementations of the principle that aim at finding binary factorial codes (Barlow et al. 1989), i.e., codes where the probability of the occurrence of a particular input is simply the product of the probabilities of the corresponding code symbols. Such codes are potentially relevant for (1) segmentation tasks, (2) speeding up supervised learning, and (3) novelty detection. Methods for finding factorial codes automatically implement Occam's razor for finding codes using a minimal number of units. Unlike previous methods the novel principle has a potential for removing not only linear but also nonlinear output redundancy. Illustrative experiments show that algorithms based on the principle of predictability minimization are practically feasible. The final part of this paper describes an entirely local algorithm that has a potential for learning unique representations of extended input sequences.

Speech activity and speaker novelty detection methods for meeting processing

2009 ◽  
Author(s):  
Masahide Sugiyama ◽  
Konstantin Markov ◽  
Andrey Ronzhin ◽  
Victor Budkov ◽  
Alexey Karpov ◽  
...  
Keyword(s):  
Novelty Detection ◽  
Detection Methods ◽  
Speech Activity

An experimental evaluation of novelty detection methods

2014 ◽  
Vol 135 ◽  
pp. 313-327 ◽  
Author(s):  
Xuemei Ding ◽  
Yuhua Li ◽  
Ammar Belatreche ◽  
Liam P. Maguire
Keyword(s):  
Experimental Evaluation ◽  
Novelty Detection ◽  
Detection Methods

scholarly journals Novelty Detection via Contrastive Learning with Negative Data Augmentation

2021 ◽  
Author(s):  
Chengwei Chen ◽  
Yuan Xie ◽  
Shaohui Lin ◽  
Ruizhi Qiao ◽  
Jian Zhou ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Novelty Detection ◽  
Training Data ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
Negative Data ◽  
Discriminative Ability ◽  
Adversarial Networks ◽  
Training Mode ◽  
Augmentation Strategy

Novelty detection is the process of determining whether a query example differs from the learned training distribution. Previous generative adversarial networks based methods and self-supervised approaches suffer from instability training, mode dropping, and low discriminative ability. We overcome such problems by introducing a novel decoder-encoder framework. Firstly, a generative network (decoder) learns the representation by mapping the initialized latent vector to an image. In particular, this vector is initialized by considering the entire distribution of training data to avoid the problem of mode-dropping. Secondly, a contrastive network (encoder) aims to ``learn to compare'' through mutual information estimation, which directly helps the generative network to obtain a more discriminative representation by using a negative data augmentation strategy. Extensive experiments show that our model has significant superiority over cutting-edge novelty detectors and achieves new state-of-the-art results on various novelty detection benchmarks, e.g. CIFAR10 and DCASE. Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.

scholarly journals Evaluation of Novelty Detection Methods for Condition Monitoring applied to an Electromechanical System

10.5772/67531 ◽  
2017 ◽  
Author(s):  
Miguel Delgado Prieto ◽  
Jesús A. Cariño Corrales ◽  
Daniel Zurita Millán ◽  
Marta Millán Gonzalvez ◽  
Juan A. Ortega Redondo ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Novelty Detection ◽  
Electromechanical System ◽  
Detection Methods
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