Title page for ETD etd-12242013-094021


Type of Document Master's Thesis
Author Bhaduri, Sreyoshi
Author's Email Address sreyoshi@vt.edu
URN etd-12242013-094021
Title Algorithm to enable intelligent rail break detection
Degree Master of Science
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Saied Taheri Committee Chair
Daniel J Stilwell Committee Member
Mehdi Ahmadian Committee Member
Keywords
  • Support Vector Machines
  • crossing and track safety
  • rail break detection
Date of Defense 2013-12-11
Availability unrestricted
Abstract
Wavelet intensity based algorithm developed previously at VirginiaTech has been furthered and paired with an SVM based classifier. The wavelet intensity algorithm acts as a feature extraction algorithm. The wavelet transform is an effective tool as it allows one to narrow down upon the transient, high frequency events and is able to tell their exact location in time. According to prior work done in the field of signal processing, the local regularities of a signal can be estimated using a Lipchitz exponent at each time step of the signal. The local Lipchitz exponent can then be used to generate the wavelet intensity factor values.

For each vertical acceleration value, corresponding to a specific location on the track, we now have a corresponding intensity factor. The intensity factor corresponds to break-no break information and can now be used as a feature to classify the vertical acceleration as a fault or no fault. Support Vector Machines (SVM) is used for this binary classification task. SVM is chosen as it is a well-studied topic with efficient implementations available. SVM instead of hard threshold of the data is expected to do a better job of classification without increasing the complexity of the system appreciably.

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