Predictive Maintenance of Machine Tool Linear Axes: A Case from Manufacturing Industry
| Autor: | Lihui Wang, Bernard Schmidt |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Tillförlitlighets- och kvalitetsteknik
0209 industrial biotechnology business.product_category Computer science business.industry condition monitoring Sustainable manufacturing Big data Context (language use) 02 engineering and technology Industrial and Manufacturing Engineering Manufacturing engineering Predictive maintenance Machine tool predictive maintenance 020901 industrial engineering & automation Artificial Intelligence Manufacturing machine tool 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Environmental impact assessment Cloud manufacturing Reliability and Maintenance business |
| Zdroj: | Procedia Manufacturing. 17:118-125 |
| ISSN: | 2351-9789 |
| DOI: | 10.1016/j.promfg.2018.10.022 |
| Popis: | In sustainable manufacturing, the proper maintenance is crucial to minimise the negative environmental impact. In the context of Cloud Manufacturing, Internet of Things and Big Data, amount of available information is not an issue, the problem is to obtain the relevant information and process them in a useful way. In this paper a maintenance decision support system is presented that utilises information from multiple sources and of a different kind. The key elements of the proposed approach are processing and machine learning method evaluation and selection, as well as estimation of long-term key performance indicators (KPIs) such as a ratio of unplanned breakdowns or a cost of maintenance approach. Presented framework is applied to machine tool linear axes. Statistical models of failures and Condition Based Maintenance (CBM) are built based on data from a population of 29 similar machines from the period of over 4 years and with use of proposed processing approach. Those models are used in simulation to estimate the long-term effect on selected KPIs for different strategies. Simple CBM approach allows, in the considered case, a cost reduction of 40% with the number of breakdowns reduced 6 times in respect to an optimal time-based approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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