%P 57-61
%A T. Abbasi
%A K.H. Lann
%A I. Ismail
%I Institute of Electrical and Electronics Engineers Inc.
%T Optimal input selection for recurrent neural network in predictive maintenance
%R 10.1109/CCOMS.2019.8821669
%D 2019
%L scholars11788
%J 2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019
%O cited By 1; Conference of 4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019 ; Conference Date: 23 February 2019 Through 25 February 2019; Conference Code:151726
%X Rotating equipment are essential components in oil and gas industry processes. Failure of rotating equipment can lead to critical downtime of a plant. Traditional preventive and reactive maintenance practices are ineffective in identifying equipment faults. On the other hand, predictive maintenance receives highly attention in the industry because it learns from the past information to predict future failure and subsequently early maintenance can be performed before the occurrence of equipment failure. In this paper, 14 parameters from air booster compressor (ABC) are retrieved for optimal input analysis and selection. A sequential of data are recorded in a real-time manner for prediction. However, training a data model with high dimensions requires vast time and complexity. Therefore, optimal input variable selection for recurrent neural network (RNN) model is performed using principal component analysis (PCA) for signal prediction of ABC motor parameters. The performance of the model is compared with large set of inputs were used. The obtained results showed that the selected input variables based on PCA results in lower error rate and better prediction accuracy. © 2019 IEEE.
%K Forecasting; Gas industry; Preventive maintenance; Recurrent neural networks; Rotating machinery, Correlation analysis; Input variable selection; Oil and Gas Industry; Prediction accuracy; Predictive maintenance; Reactive maintenance; Recurrent neural network (RNN); Rotating equipment, Principal component analysis