Performance Prediction using Neural Network and Confidence Intervals: a Gas Turbine application


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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 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.
Event Title: 2018 IEEE International Conference on Big Data
Event Type: Other
Event Dates: 2018-12-10 - 2018-12-13
Uncontrolled Keywords: Confidence Interval,Industry 4.0,Predictive Maintenance,Radial Basis Function Neural Networks,Computer Science Applications,Information Systems
ISBN: 978-1-5386-5036-3, 978-1-5386-5035-6
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Related URLs: https://ieeexpl ... ocument/8621919 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2019-01-24
Accepted Date: 2018-10-16
Authors: Cisotto, Silvia
Herzallah, Randa (ORCID Profile 0000-0001-9128-6814)



Version: Accepted Version

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