Intelligent fault diagnostic model for rotating machinery

The aim of this paper is to present an intelligent fault diagnostic to assess the changes and detect malfunctions in rotating machinery using real-time data. The developed model interprets performance condition monitoring data and determines machine health status with the use of Artificial Neural Ne...

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Main Authors: Muhammad, M.B., Sarwar, U., Tahan, M., Karim, Z.A.A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.21767 /
Published: IEEE Computer Society 2018
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045260805&doi=10.1109%2fIEEM.2017.8290213&partnerID=40&md5=cc01b39cbcebae732e6a88fb4854d41d
http://eprints.utp.edu.my/21767/
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id utp-eprints.21767
recordtype eprints
spelling utp-eprints.217672019-01-10T03:54:18Z Intelligent fault diagnostic model for rotating machinery Muhammad, M.B. Sarwar, U. Tahan, M. Karim, Z.A.A. The aim of this paper is to present an intelligent fault diagnostic to assess the changes and detect malfunctions in rotating machinery using real-time data. The developed model interprets performance condition monitoring data and determines machine health status with the use of Artificial Neural Networks (ANN). The ANN networks were trained for principle performance parameters from which actual system performance can be predicted based on given set of input parameters. The validity of the proposed model was evaluated through a case study on twin-shaft 18700 kW industrial gas turbine engine to detect a fault happened in engine bell-mouth. The results showed the networks trained using Levenberg-Marquardt (LM) training function can achieve more accurate results compared to Bayesian regulation (BR) and scaled conjugate gradient (SCG) training functions. In addition, the results also showed that both power output parameter and the fuel flow rate can be effectively used to monitor the performance of gas turbine. © 2017 IEEE. IEEE Computer Society 2018 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045260805&doi=10.1109%2fIEEM.2017.8290213&partnerID=40&md5=cc01b39cbcebae732e6a88fb4854d41d Muhammad, M.B. and Sarwar, U. and Tahan, M. and Karim, Z.A.A. (2018) Intelligent fault diagnostic model for rotating machinery. IEEE International Conference on Industrial Engineering and Engineering Management, 2017-D . pp. 1858-1862. http://eprints.utp.edu.my/21767/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description The aim of this paper is to present an intelligent fault diagnostic to assess the changes and detect malfunctions in rotating machinery using real-time data. The developed model interprets performance condition monitoring data and determines machine health status with the use of Artificial Neural Networks (ANN). The ANN networks were trained for principle performance parameters from which actual system performance can be predicted based on given set of input parameters. The validity of the proposed model was evaluated through a case study on twin-shaft 18700 kW industrial gas turbine engine to detect a fault happened in engine bell-mouth. The results showed the networks trained using Levenberg-Marquardt (LM) training function can achieve more accurate results compared to Bayesian regulation (BR) and scaled conjugate gradient (SCG) training functions. In addition, the results also showed that both power output parameter and the fuel flow rate can be effectively used to monitor the performance of gas turbine. © 2017 IEEE.
format Article
author Muhammad, M.B.
Sarwar, U.
Tahan, M.
Karim, Z.A.A.
spellingShingle Muhammad, M.B.
Sarwar, U.
Tahan, M.
Karim, Z.A.A.
Intelligent fault diagnostic model for rotating machinery
author_sort Muhammad, M.B.
title Intelligent fault diagnostic model for rotating machinery
title_short Intelligent fault diagnostic model for rotating machinery
title_full Intelligent fault diagnostic model for rotating machinery
title_fullStr Intelligent fault diagnostic model for rotating machinery
title_full_unstemmed Intelligent fault diagnostic model for rotating machinery
title_sort intelligent fault diagnostic model for rotating machinery
publisher IEEE Computer Society
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045260805&doi=10.1109%2fIEEM.2017.8290213&partnerID=40&md5=cc01b39cbcebae732e6a88fb4854d41d
http://eprints.utp.edu.my/21767/
_version_ 1741196517364989952
score 11.62408

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