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

Silvia Cisotto, Randa Herzallah

    Research output: Chapter in Book/Published conference outputConference publication


    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.
    Original languageEnglish
    Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
    EditorsYang 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
    Number of pages9
    ISBN (Electronic)978-1-5386-5035-6
    ISBN (Print)978-1-5386-5036-3
    Publication statusPublished - 24 Jan 2019
    Event2018 IEEE International Conference on Big Data - Seattle, United States
    Duration: 10 Dec 201813 Dec 2018


    Conference2018 IEEE International Conference on Big Data
    Country/TerritoryUnited States

    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.


    • Confidence Interval
    • Industry 4.0
    • Predictive Maintenance
    • Radial Basis Function Neural Networks


    Dive into the research topics of 'Performance Prediction using Neural Network and Confidence Intervals: a Gas Turbine application'. Together they form a unique fingerprint.

    Cite this