Logo

Predictive Maintenance of Air Booster Compressor (ABC) Motor Failure using Artificial Neural Network trained by Particle Swarm Optimization

Rosli, N.S. and Ain Burhani, N.R. and Ibrahim, R. (2019) Predictive Maintenance of Air Booster Compressor (ABC) Motor Failure using Artificial Neural Network trained by Particle Swarm Optimization. [["eprint_typename_conference\_item" not defined]]

Full text not available from this repository.

Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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.

Item Type:["eprint_typename_conference\_item" not defined]
Impact Factor:cited By 1
Uncontrolled Keywords:Gas compressors; Maintenance; Mean square error; Neural networks, Booster compressor; High demand; High impact; Motor failure; Optimal weight; Predictive maintenance; Root mean square errors, Particle swarm optimization (PSO)
ID Code:24898
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:27 Aug 2021 06:38
Last Modified:27 Aug 2021 06:38

Repository Staff Only: item control page