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Neural Network Prognostics Model For Industrial Equipment Maintenance

Asmai, S. A. and Basari, A.S.H and Shibghatullah, Abdul Samad and Ibrahim, N. K. and Hussin, Burairah (2011) Neural Network Prognostics Model For Industrial Equipment Maintenance. In: 11th International Conference on Hybrid Intelligent Systems, HIS 2011 , 5-8 December 2011, Melaka. (Submitted)

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NEURAL NETWORK PROGNOSTICS MODEL FOR INDUSTRIAL EQUIPMENT MAINTENANCE-SITI AZIRAH ASMAI-MAK 00292 RAF.pdf

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Abstract

This paper presents a new prognostics model based on neural network technique for supporting industrial maintenance decision. In this study, the probabilities of failure based on the real condition equipment are initially calculated by using logistic regression method. The failure probabilities are subsequently utilized as input for prognostics model to predict the future value of failure condition and then used to estimate remaining useful lifetime of equipment, by having a time series of predicted failure probability, the failure distribution can be generated and used in the maintenance cost model to decide the optimal time to do maintenance. The proposed prognostic model is implemented in the industrial equipment known as autoclave burner. The result from the model reveals that it can give prior warnings and indication to the maintenance department to take an appropriate decision instead of dealing with the failures while the autoclave burner is still operating. This significant contribution provides new insights into the maintenance strategy which enables the use of existing condition data from industrial equipment and prognostics approach

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Neural networks, Neural networks (Computer science) -- Industrial applications
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Information and Communication Technology
Depositing User: Users 4097 not found.
Date Deposited: 14 Dec 2017 08:11
Last Modified: 14 Dec 2017 08:11
URI: http://eprints.utem.edu.my/id/eprint/20150

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