Predictive Maintenance of Air Booster Compressor (ABC) Motor Failure using Artificial Neural Network trained by Particle Swarm Optimization
Predictive maintenance becomes crucial nowadays in industry 4.0 since it will have a high impact on the industrial economy. Therefore, accurate predictive maintenance growing high demand for handling the failure of big plants effectively. In this paper, the model of predictive maintenance for Air Bo...
| Main Authors: | Rosli, N.S., Ain Burhani, N.R., Ibrahim, R. |
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| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.23614 / |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2019
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075635449&doi=10.1109%2fSCORED.2019.8896330&partnerID=40&md5=57026a08d8a8b4317fe0bd50c8f3153b http://eprints.utp.edu.my/23614/ |
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| Summary: |
Predictive maintenance becomes crucial nowadays in industry 4.0 since it will have a high impact on the industrial economy. Therefore, accurate predictive maintenance growing high demand for handling the failure of big plants effectively. In this paper, the model of predictive maintenance for Air Booster Compressor (ABC) Motor failure is using Artificial Neural Network (ANN) is presented. However, the optimal weights of the network are one of the parameters that lead to the accuracy of ANN. Therefore, Particle Swarm Optimization (PSO) is proposed to train the weights and bias of ANN. The result presented in this paper is compared with conventional ANN based on Mean Square Error (MSE) and Root Mean Square Error (RMSE) © 2019 IEEE. |
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