Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20359
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Big data architecture for intelligent maintenance : a focus on query processing and machine learning algorithms
Authors: Lehmann, Claude
Goren Huber, Lilach
Horisberger, Thomas
Scheiba, Georg
Sima, Ana-Claudia
Stockinger, Kurt
et. al: No
DOI: 10.1186/s40537-020-00340-7
10.21256/zhaw-20359
Published in: Journal of Big Data
Volume(Issue): 7
Issue: 1
Issue Date: Aug-2020
Publisher / Ed. Institution: Springer
ISSN: 2196-1115
Language: English
Subjects: Big Data architecture; Query processing; Machine learning; Heterogeneous data integration; Stream processing; Intelligent maintenance; Prognostics; Health management
Subject (DDC): 005: Computer programming, programs and data
Abstract: Exploiting available condition monitoring data of industrial machines for intelligent maintenance purposes has been attracting attention in various application fields. Machine learning algorithms for fault detection, diagnosis and prognosis are popular and easily accessible. However, our experience in working at the intersection of academia and industry showed that the major challenges of building an end-to-end system in a real-world industrial setting go beyond the design of machine learning algorithms. One of the major challenges is the design of an end-to-end data management solution that is able to efficiently store and process large amounts of heterogeneous data streams resulting from a variety of physical machines. In this paper we present the design of an end-to-end Big Data architecture that enables intelligent maintenance in a real-world industrial setting. In particular, we will discuss various physical design choices for optimizing high-dimensional queries, such as partitioning and Z-ordering, that serve as the basis for health analytics. Finally, we describe a concrete fault detection use case with two different health monitoring algorithms based on machine learning and classical statistics and discuss their advantages and disadvantages. The paper covers some of the most important aspects of the practical implementation of such an end-to-end solution and demonstrates the challenges and their mitigation for the specific application of laser cutting machines.
URI: https://digitalcollection.zhaw.ch/handle/11475/20359
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Published as part of the ZHAW project: Decision Support System For Predictive Maintenance of Laser Cutting Machines
Appears in Collections:Publikationen School of Engineering

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