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Building Predictive Maintenance Models

  • Roger Barga
  • Valentine Fontama
  • Wee Hyong Tok
Chapter

Abstract

The leading manufacturers are now investing in predictive maintenance, which holds the potential to reduce cost yet increase margin and customer satisfaction. Though traditional techniques such as 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 improve customer satisfaction and reduce service downtime.   This chapter shows 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 Mutual Information Preventive Maintenance Boost Decision Tree Reader Module 
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

© Valentine Fontama 2014

Authors and Affiliations

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

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