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

Parham Shahidi, Dan Maraini, and Brad Hopkins
Publication Target: 
IJPHM
Publication Issue: 
Special Issue Big Data and Analytics
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_16_034.pdf2.22 MBJanuary 11, 2017 - 4:51pm

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-maximal-relevance (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 through wavelet transforms, resulting in the extraction of a set of 687 features from the acceleration signals. A maximum-relevance minimum-redundancy feature selection algorithm was used to 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.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
034
Page Count: 
13
Submission Keywords: 
Support Vector Machine
Mutual Information
diagnostic
Railcar
Asset management
Minimal-Redundancy Maximum-Relevance
Bogie
Submission Topic Areas: 
Component-level PHM
Data-driven methods for fault detection, diagnosis, and prognosis
Health management system design and engineering
Industrial applications
Sensors
Systems and platform applications
Verification and validation
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