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Field-Regularised Factorization Machines for Mining the Maintenance Logs of Equipment

  • Zhibin Li
  • Jian Zhang
  • Qiang Wu
  • Christina Kirsch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11320)

Abstract

Failure prediction is very important for railway infrastructure. Traditionally, data from various sensors are collected for this task. Value of maintenance logs is often neglected. Maintenance records of equipment usually indicate equipment status. They could be valuable for prediction of equipment faults. In this paper, we propose Field-regularised Factorization Machines (FrFMs) to predict failures of railway points with maintenance logs. Factorization Machine (FM) and its variants are state-of-the-art algorithms designed for sparse data. They are widely used in click-through rate prediction and recommendation systems. Categorical variables are converted to binary features through one-hot encoding and then fed into these models. However, field information is ignored in this process. We propose Field-regularised Factorization Machines to incorporate such valuable information. Experiments on data set from railway maintenance logs and another public data set show the effectiveness of our methods.

Keywords

Factorization Machines Failure prediction Categorical data 

Notes

Acknowledgements

The authors greatly appreciate the financial support from the Rail Manufacturing Cooperative Research Centre (funded jointly by participating rail organisations and the Australian Federal Governments Business Cooperative Research Centres Program) through Project R3.7.2 - Big data analytics for condition based monitoring and maintenance.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zhibin Li
    • 1
  • Jian Zhang
    • 1
  • Qiang Wu
    • 1
  • Christina Kirsch
    • 2
  1. 1.University of Technology SydneySydneyAustralia
  2. 2.Sydney Trains-Operational TechnologySydneyAustralia

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