A Naïve-Bayes classifier for damage detection in engineering materials

Abstract

This paper is intended to introduce the Bayesian network in general and the Naïve-Bayes classifier in particular as one of the most successful classification systems to simulate damage detection in engineering materials. A method for feature subset selection has also been introduced too. The method is based on mean and maximum values of the amplitudes of waves after dividing them into folds then grouping them by a clustering algorithm (e.g. k-means algorithm). The Naïve-Bayes classifier and the feature sub-set selection method were analyzed and tested on two sets of data. The data sets were conducted based on artificial damages created in quasi isotopic laminated composites of the AS4/3501-6 graphite/epoxy system and ball bearing of the type 6204 with a steel cage. The Naïve-Bayes classifier and the proposed feature subset selection algorithm have been shown as efficient techniques for damage detection in engineering materials.

Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier

“…The computation burden of BNN is increases as the number of likelihood term in the class raises exponentially with the attributes L= {L1, L2, … Ln }. To overcome this limitation, all features in a class are assumed to be independent that results in the Naive Bayes (NB) classifier that reduces the number of parameter to be estimated from 2(2n-1) to 2n [25,30,31]. NB is a linear classifier that divides the input data set into training and prediction step for identifying the type of class using…”

On The Automated Classification of Web Pages Using Artificial Neural Network

“…Further, enhancing the standard NB rule or using it in collaboration with other techniques has also been attempted by other researchers. Addin et al in [27] coupled a NB classifier with K-Means clustering to simulate damage detection in engineering materials. NB Tree in [23] induced a hybrid of NB and DTs by using the Bayes rule to construct the decision tree.…”

Data Mining Technique for Knowledge Discovery from Engineering Materials Data Sets

“…The naive Bayesian classifier, or simple Bayesian classifier generally used for classification or prediction task. As it is simple, robust and generality, this procedure is deployed for various applications such as materials damage detection [1], [2], agricultural land soils classification [3], web page classification [5], machine learning applications [19]. Therefore the application of this is extended to classification of engineering materials data sets [27] and to reduce the computational cost involved in materials classification and selection process.…”