Abstract
Predictive maintenance is very important for effective prevention of failures in an industry. The present paper describes a case study where a wood chip pump system was analyzed, and a predictive model was proposed. An Ishikawa diagram and FMECA are used to identify possible causes for system failure. The Chip Wood has several sensors installed to monitor the working conditions and system state. The authors propose a variation of exponential smoothing technique for short time forecasting and an artificial neural network for long time forecasting. The algorithms were integrated into a dashboard for online condition monitor-ing, where the users are alerted when a variable is determined or predicted to get out of the expected range. Experimental results show prediction errors in general less than 10 %. The proposed technique may be of help in monitoring and maintenance of the asset, aiming at greater availability.
| Original language | English |
|---|---|
| Pages (from-to) | 33-41 |
| Number of pages | 9 |
| Journal | Eksploatacja i Niezawodnosc |
| Volume | 24 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2022 |
Bibliographical note
Publisher Copyright:© 2022, Polish Academy of Sciences Branch Lublin. All rights reserved.
Funding
The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowvska-Curie grant agreement 871284 project SSHARE and the European Regional Development Fund (ERDF) through the Operational Programme for Competitive-ness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-029494, and by National Funds through the FCT—Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/29494/2017, UIDB/04131/2020, and UIDP/04131/2020. This research is sponsored by FEDER funds through the program COMPETE ? Programa Operacional Factores de Competitividade?and by national funds through FCT?Funda??o para a Ci?n-cia e a Tecnologia?under the project UIDB/00285/2020. This work was produced with the support of INCD funded by FCT and FEDER under the project 01/SAICT/2016 n? 022153. The research leading to these results has received funding from the European Union?s Horizon 2020 research and innovation programme under the Marie Sklodowvska-Curie grant agreement 871284 project SSHARE and the European Regional Development Fund (ERDF) through the Operational Programme for Competitive-ness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-029494, and by National Funds through the FCT?Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/29494/2017, UIDB/04131/2020, and UIDP/04131/2020.
| Funders | Funder number |
|---|---|
| INCD | |
| Operational Programme for Competitive-ness and Internationalization | |
| Horizon 2020 Framework Programme | 871284 |
| Fundação para a Ciência e a Tecnologia | UIDB/04131/2020, PTDC/EEI-EEE/29494/2017, UIDP/04131/2020 |
| European Regional Development Fund | |
| Programa Operacional Temático Factores de Competitividade | POCI-01-0145-FEDER-029494 |
Keywords
- Artificial neural net-works
- Condition based maintenance
- Forecasting
- Predictive maintenance
- Time series