Neural network prognostics model for industrial equipment maintenance

• Main Authors: Asmai, Siti Azirah, Hasan Basari, Abd Samad, Shibghatullah, Abdul Samad, Ibrahim, Nuzulha Khilwani, Hussin, Burairah
• Format: Conference or Workshop Item
• Language: English
• Published: 2011
• Subjects: T Technology (General); TJ Mechanical engineering and machinery
• Online Access: http://eprints.utem.edu.my/id/eprint/20150/1/NEURAL%20NETWORK%20PROGNOSTICS%20MODEL%20FOR%20INDUSTRIAL%20EQUIPMENT%20MAINTENANCE-SITI%20AZIRAH%20ASMAI-MAK%2000292%20RAF.pdf; http://eprints.utem.edu.my/id/eprint/20150/
• Institution: Universiti Teknikal Malaysia Melaka

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