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Article

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

1
Information Technology Group, Wageningen University & Research, 6706 KN Wageningen, The Netherlands
2
Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(3), 932; https://doi.org/10.3390/s21030932
Received: 21 November 2020 / Revised: 12 January 2021 / Accepted: 18 January 2021 / Published: 30 January 2021
Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results. View Full-Text
Keywords: machine learning; production lines; predictive maintenance; data mining; maintenance prediction machine learning; production lines; predictive maintenance; data mining; maintenance prediction
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MDPI and ACS Style

Kang, Z.; Catal, C.; Tekinerdogan, B. Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks. Sensors 2021, 21, 932. https://doi.org/10.3390/s21030932

AMA Style

Kang Z, Catal C, Tekinerdogan B. Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks. Sensors. 2021; 21(3):932. https://doi.org/10.3390/s21030932

Chicago/Turabian Style

Kang, Ziqiu, Cagatay Catal, and Bedir Tekinerdogan. 2021. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks" Sensors 21, no. 3: 932. https://doi.org/10.3390/s21030932

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