Real-Time Induction Motor Health Index Prediction in A Petrochemical Plant using Machine Learning

Authors

  • Waritsara Khrakhuean Chulalongkorn University
  • Parames Chutima Chulalongkorn University; The Royal Society of Thailand

DOI:

https://doi.org/10.4186/ej.2022.26.5.91

Keywords:

real-time prediction, machine learning, artificial neural network, particle swarm optimisation, gradient boost tree, random forest

Abstract

This paper presents real-time health prediction of induction motors (IMs) utilised in a petrochemical plant through the application of intelligent sensors and machine learning (ML) models. At present, maintenance engineers of the company implement time-based and condition-based maintenance techniques in periodically examining and diagnosing the health of IMs which results in sporadic breakdowns of IMs. Such breakdowns sometimes force the entire production process to stop for emergency maintenance resulting in a huge loss in the company’s revenue. Hence, top management decides to switch the operational practice to real-time predictive maintenance instead. Intelligent sensors are installed on IMs to collect necessary information related to their working statuses. ML exploits the real-time information received from intelligent sensors to flag abnormalities of mechanical or electrical components of IMs before potential failures are reached. Four ML models are investigated to evaluate which one is the best, i.e. Artificial Neural Network (ANN), Particle Swarm Optimization (PSO), Gradient Boosting Tree (GBT) and Random Forest (RF). Standard performance metrics are used to compare the relative effectiveness among different ML models including Precision, Recall, Accuracy, F1-score, and AUC-ROC curve. The results reveal that PSO not only obtains the highest average weighted Accuracy but also can differentiate the statuses (Class 0 – Class 3) of the IM more correctly than other counterpart models.

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Author Biographies

Waritsara Khrakhuean

Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Thailand

Parames Chutima

Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Thailand

The Academy of Science, The Royal Society of Thailand, Thailand

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Published In
Vol 26 No 5, May 31, 2022
How to Cite
[1]
W. Khrakhuean and P. Chutima, “Real-Time Induction Motor Health Index Prediction in A Petrochemical Plant using Machine Learning”, Eng. J., vol. 26, no. 5, pp. 91-107, May 2022.