TY - JOUR ID - 16049 TI - On the Usefulness of Pre-processing Methods in Rotating ‎Machines Faults Classification using Artificial Neural Network JO - Journal of Applied and Computational Mechanics JA - JACM LA - en SN - AU - Alzghoul, Ahmad AU - Jarndal, Anwar AU - Alsyouf, Imad AU - Bingamil, Ahmed Ameen AU - Ali, Muhammad Awais AU - AlBaiti, Saleh AD - Data Science Department, Princess Sumaya University for Technology, Amman, Jordan AD - Electrical Engineering Department, University of Sharjah, Sharjah, United Arab Emirates AD - Sustainable Engineering Asset Management (SEAM) Research Group, University of Sharjah, Sharjah, United Arab Emirates Y1 - 2021 PY - 2021 VL - 7 IS - 1 SP - 254 EP - 261 KW - Rotating machines KW - Multi-fault diagnostic KW - Data Pre-processing KW - Handling Imbalance Dataset KW - machine learning DO - 10.22055/jacm.2020.35354.2639 N2 - 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. UR - https://jacm.scu.ac.ir/article_16049.html L1 - https://jacm.scu.ac.ir/article_16049_983a7efcea486af4010045db59a7a3c6.pdf ER -