A Data-Driven Holistic Approach to Fault Prognostics in a Cyclic Manufacturing Process
| Autor: | David Kralj, Dominik Kozjek, Peter Butala, Rok Vrabič |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Engineering business.industry Process (engineering) Test data generation media_common.quotation_subject Big data 02 engineering and technology 010501 environmental sciences 01 natural sciences Manufacturing engineering Data-driven Reliability engineering Variety (cybernetics) 020901 industrial engineering & automation General Earth and Planetary Sciences Prognostics Quality (business) business 0105 earth and related environmental sciences General Environmental Science media_common Manufacturing execution system |
| Zdroj: | Procedia CIRP. 63:664-669 |
| ISSN: | 2212-8271 |
| DOI: | 10.1016/j.procir.2017.03.109 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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