Building Predictive Maintenance Models

  • Roger Barga
  • Valentine Fontama
  • Wee Hyong Tok

Abstract

Leading manufacturers are now investing in predictive maintenance, which holds the potential to reduce cost and increase margin and customer satisfaction. Though traditional techniques from statistics and manufacturing have helped, the industry is still plagued by serious quality issues and the high cost of business disruption when components fail. Advances in machine learning offer a unique opportunity to reduce cost and improve customer satisfaction. This chapter will show how to build models for predictive maintenance using Microsoft Azure Machine Learning. Through examples we will demonstrate how you can use Microsoft Azure Machine Learning to build, validate, and deploy a predictive model for predictive maintenance.

Keywords

Feature Selection Preventive Maintenance Minority Class Class Imbalance Boost Decision Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Roger Barga, Valentine Fontama, and Wee Hyong Tok 2015

Authors and Affiliations

  • Roger Barga
    • 1
  • Valentine Fontama
    • 1
  • Wee Hyong Tok
    • 1
  1. 1.WAUS

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