Predictive analytics in the production of elevators

Valeria Boldosova (Corresponding author), Jani Hietala, Jari Pakkala, Riku Salokangas, Petri Kaarmila, Eetu Puranen

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

Abstract

With the emerging role of digitalization in the industrial sector, more and more companies attempt to increase asset availability, improve product quality and reduce maintenance costs. Manufacturing companies are faced with the need to transform traditional services into remote factory monitoring solutions using big data and advanced analytics. Kone is a global leader in the elevator and escalator production industry, which is continuously looking for new ways of improving production efficiency and reducing machine downtime in order to run unmanned 24/7 production. However, the process of collecting data from equipment and utilizing it for predictive analytics can be challenging and time consuming. Therefore, during Serena project Kone cooperated with VTT and Prima Power, which provided necessary capabilities and competencies in the areas of data collection, analysis and utilization for developing and testing predictive maintenance solutions in the elevator manufacturing industry. As a result of this collaboration, VTT integrated sensors into Prima Power production line used at Kone and developed algorithms for measuring the remaining useful life of conveyor bearings. As a machine tool builder, Prima Power contributed to the project with a cloud environment for remote collection of vibration measurement data and Serena Customer Web analytics for condition-based maintenance.
Original languageEnglish
Title of host publicationPredictive Maintenance in Smart Factories
Subtitle of host publicationArchitectures, Methodologies, and Use-cases
EditorsT. Cerquitelli, N. Nikolakis, N. O’Mahony, E. Macii, M. Ippolito, S. Makris
PublisherSpringer
Chapter3
Pages165-185
ISBN (Electronic)978-981-16-2940-2
ISBN (Print)978-981-16-2939-6
DOIs
Publication statusPublished - 2021
MoE publication typeA3 Part of a book or another research book

Publication series

SeriesInformation Fusion and Data Science
ISSN2510-1528

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