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

Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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JR_JACM-7-1_024

تاریخ نمایه سازی: 9 خرداد 1400

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 ۹۷.۱% 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.

Authors

Ahmad Alzghoul

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

Anwar Jarndal

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

Imad Alsyouf

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

Ahmed Ameen Bingamil

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

Muhammad Awais Ali

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

Saleh AlBaiti

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

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