Journal of Applied and Computational Mechanics

On the Usefulness of Pre-processing Methods in Rotating ‎Machines Faults Classification using Artificial Neural Network

Document Type : Research Paper

Authors

1 Data Science Department, Princess Sumaya University for Technology, Amman, Jordan

2 Electrical Engineering Department, University of Sharjah, Sharjah, United Arab Emirates

3 Sustainable Engineering Asset Management (SEAM) Research Group, University of Sharjah, Sharjah, United Arab Emirates

4 Industrial Engineering and Engineering Management Department, University of Sharjah, Sharjah, United Arab Emirates

Abstract

This work presents a multi-fault classification system using artificial neural network (ANN) to distinguish between different faults in rotating machines automatically. Rotation frequency and statistical features, including mean, entropy, and kurtosis were considered in the proposed model. The effectiveness of this model lies in using Synthetic Minority Over-sampling Technique (SMOTE) to overcome the problem of imbalance data classes. Furthermore, the Relief feature selection method was used to find the most influencing features and thus improve the performance of the model. Machinery Fault Database (MAFAULDA) was deployed to evaluate the performance of the prediction models, achieving an accuracy of 97.1% which surpasses other literature that used the same database. Results indicate that handling imbalance classes hold a key role in increasing the overall accuracy and generalizability of multi-layer perceptron (MLP) classifier. Furthermore, results showed that considering only statistical features and rotational speed are good enough to get a model with high classification accuracy.

Keywords

Main Subjects

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Journal of Applied and Computational Mechanics
Volume 7, Issue 1
January 2021
Pages 254-261