A data fusion approach for track monitoring from multiple in-service trains

George Lederman, Siheng Chen, James H. Garrett, Jelena Kovacevic, Hae Young Noh, Jacobo Bielak

Research output: Contribution to journalArticle

Abstract

We present a data fusion approach for enabling data-driven rail-infrastructure monitoring from multiple in-service trains. A number of researchers have proposed using vibration data collected from in-service trains as a low-cost method to monitor track geometry. The majority of this work has focused on developing novel features to extract information about the tracks from data produced by individual sensors on individual trains. We extend this work by presenting a technique to combine extracted features from multiple passes over the tracks from multiple sensors aboard multiple vehicles. There are a number of challenges in combining multiple data sources, like different relative position coordinates depending on the location of the sensor within the train. Furthermore, as the number of sensors increases, the likelihood that some will malfunction also increases. We use a two-step approach that first minimizes position offset errors through data alignment, then fuses the data with a novel adaptive Kalman filter that weights data according to its estimated reliability. We show the efficacy of this approach both through simulations and on a data-set collected from two instrumented trains operating over a one-year period. Combining data from numerous in-service trains allows for more continuous and more reliable data-driven monitoring than analyzing data from any one train alone; as the number of instrumented trains increases, the proposed fusion approach could facilitate track monitoring of entire rail-networks.

Original languageEnglish (US)
Pages (from-to)363-379
Number of pages17
JournalMechanical Systems and Signal Processing
Volume95
DOIs
StatePublished - Oct 1 2017

Fingerprint

Data fusion
Monitoring
Sensors
Rails
Railroad tracks
Electric fuses
Kalman filters
Geometry
Costs

Keywords

  • Adaptive Kalman filter
  • Data fusion
  • Inertial sensing
  • Signal processing
  • Vehicle-based inspection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications

Cite this

A data fusion approach for track monitoring from multiple in-service trains. / Lederman, George; Chen, Siheng; Garrett, James H.; Kovacevic, Jelena; Noh, Hae Young; Bielak, Jacobo.

In: Mechanical Systems and Signal Processing, Vol. 95, 01.10.2017, p. 363-379.

Research output: Contribution to journalArticle

Lederman, George ; Chen, Siheng ; Garrett, James H. ; Kovacevic, Jelena ; Noh, Hae Young ; Bielak, Jacobo. / A data fusion approach for track monitoring from multiple in-service trains. In: Mechanical Systems and Signal Processing. 2017 ; Vol. 95. pp. 363-379.
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