Novel Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection

Abstract

Fault diagnosis of anomalies in induction motors is essential to ensure industry safety. This paper presents a new hybrid Invasive Weed Optimization and Machine Learning approach for fault diagnosis in an induction motor. The vibration signal provides a lot of information about the motor's operating conditions. Therefore, the vibration signal of the motor was chosen to investigate the fault diagnosis. Two identical 400-V, 50-Hz, 4-pole 0.75 HP induction motors were under healthy, mechanical, and electrical faults tested in a laboratory with different loading. A hybrid model was developed using the vibration signal, the Invasive Weed Optimization algorithm (IWO), and machine learning classifiers. Some statistical features were extracted from the signal using Discrete Wavelet Transform (DWT). The invasive weed optimization algorithm (IWO) was utilized to reduce the number of the extracted features and select the most suitable ones. Then, three classification algorithms namely k-Nearest Neighbor neural network (KNN), Support Vector Machine (SVM), and Random Forest (RF), were trained using k-fold cross-validation and tested to predict the true class. The advantage of combining these techniques is to reduce the training time and increase the average accuracy of the model. The performance of the proposed fault diagnosis model was evaluated by measuring the Specificity, Accuracy, Precision, Recall, and F1_score. The experimental results prove that the proposed model has achieved more than 99.90% of accuracy. Furthermore, the other evaluation parameters also show the same representation of performance. The hybrid model has proved successfully its robust for diagnosing the faults under different load conditions.