Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89671
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Title: Big data analytics for predictive maintenance strategies
Authors: Lee, CKM 
Cao, Y 
Ng, KH 
Issue Date: 2017
Source: In HK Chan (Ed.), Nachiappan Subramanian, Muhammad Dan-Asabe Abdulrahman, Supply chain management in the big data era, chapter 4, p. 50-74. Hershey, PA: IGI Global, 2017
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.
ISBN: 9781522509561(ISBN13)
1522509569(ISBN10)
9781522509578(EISBN13)
DOI: 10.4018/978-1-5225-0956-1.ch004
Rights: Posted with permission of the publisher and author.
The following book chapter Lee, C. K., Cao, Y., & Ng, K. H. (2017). Big Data Analytics for Predictive Maintenance Strategies. In H. Chan, N. Subramanian, & M. Abdulrahman (Ed.), Supply Chain Management in the Big Data Era (pp. 50-74). IGI Global is available from http://doi:10.4018/978-1-5225-0956-1.ch004
Appears in Collections:Book Chapter

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