Big data analytics for predictive maintenance strategies

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

25 Citations (Scopus)

Abstract

Maintenance aims to reduce and eliminate the number of failures occurred during production as any breakdown of machine or equipment may lead to disruption for the supply chain. Maintenance policy is set to provide the guidance for selecting the most cost-effective maintenance approach and system to achieve operational safety. For example, predictive maintenance is most recommended for crucial components whose failure will cause severe function loss and safety risk. Recent utilization of big data and related techniques in predictive maintenance greatly improves the transparency for system health condition and boosts the speed and accuracy in the maintenance decision making. In this chapter, a Maintenance Policies Management framework under Big Data Platform is designed and the process of maintenance decision support system is simulated for a sensor-monitored semiconductor manufacturing plant. Artificial Intelligence is applied to classify the likely failure patterns and estimate the machine condition for the faulty component.

Original languageEnglish
Title of host publicationSupply Chain Management in the Big Data Era
PublisherIGI Global
Pages50-74
Number of pages25
ISBN (Electronic)9781522509578
ISBN (Print)9781522509561
DOIs
Publication statusPublished - 4 Nov 2016

Keywords

  • MAINTENANCE POLICIES MANAGEMENT
  • MAINTENANCE DECISION SUPPORT SYSTEM
  • FUZZY LOGIC
  • ARTIFICIAL NEURAL NETWORK
  • DIAGONSIS AND PROGNOSIS
  • HEALTH CONDITION ASSESSMENT
  • MACHINE FAILURE PREDICTION
  • SENSOR-BASED PREDICTION

ASJC Scopus subject areas

  • Medicine (miscellaneous)

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