Abstract
The combination of Condition Based monitoring
techniques with the predictive capabilities of neural networks
represents a topic of central importance when it comes to
maximizing production profits and consequently reducing costs
and downtime. The ability to plan the best strategy based on the
prediction of potential damaging events can represent a
significant contribution, especially for the maintenance
function. In fact, optimization of the management of the
equipment is a fundamental step to guarantee the
competitiveness of companies in the current market. In this
paper, a tool based on the implementation of Radial Basis
Function Neural Networks was developed to support the
maintenance function in the decision-making process. In
addition to providing an indication of the status of the
equipment, the current approach provides an additional level of
information in terms of predicting the confidence interval
around the prediction of the neural network. The confidence
interval combined with the prediction of the future state of the
equipment can be of fundamental importance in order to avoid
strategic decisions based on a low level knowledge of the system
status or prediction performance of the applied algorithm. The
developed tool is tested on the prediction of a naval propulsion
system gas turbine performance decay, where the statuses of
both the turbine and the compressor of the system are predicted
as well as predicting their confidence intervals.
techniques with the predictive capabilities of neural networks
represents a topic of central importance when it comes to
maximizing production profits and consequently reducing costs
and downtime. The ability to plan the best strategy based on the
prediction of potential damaging events can represent a
significant contribution, especially for the maintenance
function. In fact, optimization of the management of the
equipment is a fundamental step to guarantee the
competitiveness of companies in the current market. In this
paper, a tool based on the implementation of Radial Basis
Function Neural Networks was developed to support the
maintenance function in the decision-making process. In
addition to providing an indication of the status of the
equipment, the current approach provides an additional level of
information in terms of predicting the confidence interval
around the prediction of the neural network. The confidence
interval combined with the prediction of the future state of the
equipment can be of fundamental importance in order to avoid
strategic decisions based on a low level knowledge of the system
status or prediction performance of the applied algorithm. The
developed tool is tested on the prediction of a naval propulsion
system gas turbine performance decay, where the statuses of
both the turbine and the compressor of the system are predicted
as well as predicting their confidence intervals.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 |
| Editors | Yang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu |
| Publisher | IEEE |
| Pages | 2151-2159 |
| Number of pages | 9 |
| ISBN (Electronic) | 978-1-5386-5035-6 |
| ISBN (Print) | 978-1-5386-5036-3 |
| DOIs | |
| Publication status | Published - 24 Jan 2019 |
| Event | 2018 IEEE International Conference on Big Data - Seattle, United States Duration: 10 Dec 2018 → 13 Dec 2018 |
Conference
| Conference | 2018 IEEE International Conference on Big Data |
|---|---|
| Country/Territory | United States |
| City | Seattle |
| Period | 10/12/18 → 13/12/18 |
Bibliographical note
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Keywords
- Confidence Interval
- Industry 4.0
- Predictive Maintenance
- Radial Basis Function Neural Networks