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A Data-driven holistic approach to fault prognostics in a cyclic manufacturing process
ID Kozjek, Dominik (Author), ID Vrabič, Rok (Author), ID Kralj, David (Author), ID Butala, Peter (Author)

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Abstract
The complexity of manufacturing systems is increasing due to the increased requirements related to the variety and quality of the products, their complexity, and due to the general technological developments. In turn, the data related to the manufacturing processes is growing in size and in complexity. This presents new challenges for real-time monitoring, diagnostics, and prognostics of the processes. The challenges are addressed by new tools, methodologies, and concepts, collectively referred to as Big Data. The paper deals with the use of advanced methods for prognostics of infrequent faults on available but highly dimensional manufacturing process data. A holistic approach, which includes data generation, acquisition, storage, processing, and prognostics, is shown in a case of a plastic injection moulding process. Real industrial data acquired from five injection moulding machines and the Manufacturing Execution System within a period of six months is used. It is shown how the approach is able to tackle the high dimensionality and the large size of the data to create and evaluate prediction models for prognostics of the unplanned machine stops.

Language:English
Keywords:fault diagnostics, Big Data, plastic injection moulding
Typology:1.08 - Published Scientific Conference Contribution
Organization:FS - Faculty of Mechanical Engineering
Year:2017
Number of pages:F. 1-6
UDC:658.5(045)
ISSN on article:2212-8271
DOI:10.1016/j.procir.2017.03.109 This link opens in a new window
COBISS.SI-ID:15509787 This link opens in a new window
Publication date in RUL:18.06.2018
Views:1031
Downloads:506
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Record is a part of a proceedings

Title:The 50th CIRP Conference on Manufacturing Systems, May 3rd - 5th, Taichung, Taiwan
COBISS.SI-ID:15509531 This link opens in a new window

Record is a part of a journal

Title:Procedia CIRP
Publisher:Elsevier
ISSN:2212-8271
COBISS.SI-ID:12981019 This link opens in a new window

Secondary language

Language:Slovenian
Keywords:diagnosticiranje napak, velepodatki, brizganje plastike

Projects

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project number:P2-0270

Funder:Drugi - Drug financer ali več financerjev
Funding programme:Ministry of Higher Education, Science and Technology of the Republic of Slovenia
Project number:1000-15-510

Funder:Drugi - Drug financer ali več financerjev
Funding programme:Ministry of Higher Education, Science and Technology of the Republic of Slovenia
Project number:C330-16-529000

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