Big Data Analytics for Predictive Maintenance in Manufacturing

Authors

  • Dana Turner PhD
  • Drew Scott Dr.
  • Pat Brown Prof.

Keywords:

Big Data Analytics, Predictive Maintenance, Manufacturing, Machine Learning, IoT

Abstract

The use of big data analytics in predictive maintenance is transforming manufacturing processes by enabling the early detection of equipment failures. This article examines the role of big data technologies, such as machine learning and IoT, in predicting maintenance needs and optimizing equipment performance. Through various case studies, it highlights the benefits of predictive maintenance, including cost reduction and improved operational efficiency.
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Author Biographies

Dana Turner, PhD

PhD
University of Auckland
Auckland 1010, New Zealand

Drew Scott, Dr.

Dr.
ETH Zurich
Rämistrasse 101, 8092 Zürich, Switzerland

Pat Brown, Prof.

Prof.
University of Malaya
50603 Kuala Lumpur, Malaysia

References

Kumar, N., & Kataria, V. Enhanced Sentiment Classification using a Multi-layered Stacked Ensemble Architecture.

Рагимов, Э. Р. (2006). Об одном подходе оценки риска при проектировании защищенных корпоративных сетей. Информационные технологии моделирования и управления, (1), 94-98.

Published

2024-10-24

Issue

Section

Articles