University of Oulu

Juuso, E. (2017) Intelligent Performance Analysis with a Natural Language Interface, 25 (3), doi:10.1515/mspe-2017-0025

Intelligent performance analysis with a natural language interface

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Author: Juuso, Esko K.1
Organizations: 1University of Oulu
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2017103050351
Language: English
Published: De Gruyter Open, 2017
Publish Date: 2017-10-30
Description:

Abstract

Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.

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Series: Management systems in production engineering
ISSN: 2299-0461
ISSN-E: 2450-5781
ISSN-L: 2299-0461
Volume: 25
Issue: 3
DOI: 10.1515/mspe-2017-0025
OADOI: https://oadoi.org/10.1515/mspe-2017-0025
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Copyright information: © 2017. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
  https://creativecommons.org/licenses/by-nc-nd/4.0/