Railcar Diagnostics Using Minimal-Redundancy Maximum- Relevance Feature Selection and Support Vector Machine Classification

Railcar condition is an important factor in the complex web of relationships between railroads, railcar leasing companies, shippers and railcar builders. The most important reasons for this are operational safety and economic considerations pertaining to equipment maintenance. In this study, an approach is presented for the diagnostics of railcar component health from vibration data, utilizing mutual information (MI) based minimal-redundancy-maximalrelevance (mRMR) feature selection and multi-class support vector machine classification. The proposed monitoring solution is a data-driven method which was developed with measurements taken at a railroad test laboratory under controlled conditions. Vibration data was collected from multiple locations on a railcar over several test runs, each utilizing wheelsets with different levels of wear. The input of controlled wheel wear levels was aimed at varying the system outputs to resemble those of cars with different levels of mileage in revenue service. The measured data sets were processed in the time domain, frequency domain and throughwavelet transforms, resulting in the extraction of a set of 687 features from the acceleration signals. A maximum-relevance minimum-redundancy feature selection algorithm was usedto find the optimal combination of features for classification. The algorithm performance was tested for the effect of feature set size, different kernels and scaling techniques on classification accuracy. The results and methods of this assessment are presented in the paper. The paper concludes with a proposal for a monitoring strategy aimed at specifically detecting faulty components and practicing predictive maintenance.

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Support vector machine classification for large data sets via minimum enclosing ball clustering

Neurocomputing ◽

10.1016/j.neucom.2007.07.028 ◽

2008 ◽

Vol 71 (4-6) ◽

pp. 611-619 ◽

Cited By ~ 59

Author(s):

Jair Cervantes ◽

Xiaoou Li ◽

Wen Yu ◽

Kang Li

Keyword(s):

Support Vector Machine ◽

Large Data ◽

Large Data Sets ◽

Support Vector ◽

Data Sets ◽

Support Vector Machine Classification ◽

Minimum Enclosing Ball

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Artificial bee colony algorithm for feature selection and improved support vector machine for text classification

Information Discovery and Delivery ◽

10.1108/idd-09-2018-0045 ◽

2019 ◽

Vol 47 (3) ◽

pp. 154-170

Author(s):

Janani Balakumar ◽

S. Vijayarani Mohan

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Text Classification ◽

Support Vector ◽

Data Sets ◽

Selection Algorithm ◽

Data Set ◽

Content Type ◽

Benchmark Data ◽

Bee Colony

Purpose Owing to the huge volume of documents available on the internet, text classification becomes a necessary task to handle these documents. To achieve optimal text classification results, feature selection, an important stage, is used to curtail the dimensionality of text documents by choosing suitable features. The main purpose of this research work is to classify the personal computer documents based on their content. Design/methodology/approach This paper proposes a new algorithm for feature selection based on artificial bee colony (ABCFS) to enhance the text classification accuracy. The proposed algorithm (ABCFS) is scrutinized with the real and benchmark data sets, which is contrary to the other existing feature selection approaches such as information gain and χ2 statistic. To justify the efficiency of the proposed algorithm, the support vector machine (SVM) and improved SVM classifier are used in this paper. Findings The experiment was conducted on real and benchmark data sets. The real data set was collected in the form of documents that were stored in the personal computer, and the benchmark data set was collected from Reuters and 20 Newsgroups corpus. The results prove the performance of the proposed feature selection algorithm by enhancing the text document classification accuracy. Originality/value This paper proposes a new ABCFS algorithm for feature selection, evaluates the efficiency of the ABCFS algorithm and improves the support vector machine. In this paper, the ABCFS algorithm is used to select the features from text (unstructured) documents. Although, there is no text feature selection algorithm in the existing work, the ABCFS algorithm is used to select the data (structured) features. The proposed algorithm will classify the documents automatically based on their content.

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