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Öğe Novel Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection(IEEE, 2021) Ibrahim, Alasmer; Anayi, Fatih; Packianather, Michael; Al-Omari, OsamaFault 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.Öğe Simulating LQR and PID controllers to stabilise a three-link robotic system(Institute of Electrical and Electronics Engineers Inc., 2022) Mohamed, Mahmoud; Anayi, Fatih; Packianather, Michael; Samad, Bdereddin Abdul; Yahya, KhalidThe study reported here concerns stabilisation control in a multiple-link robotic gymnast (Robogymnast) MATLAB model. The Robot Gymnast represents a high-complexity, triple-inverted pendulum system. The gymnast imitates a human gymnast hanging from an elevated bar and swinging up to increasing heights until it reaches full rotation. The Robogymnast is a 3-link structure, with components analogous to the legs, arms and torso, and has 3 joints: one operating passively, without power; and two other joints which use power. The overhead joint is a significant problem for the controlled motion of the robot and in achieving the smoothness necessary in its operation. The robot gymnast system has been built in reality. However, this paper will focus on the MATLAB model, and illustrate systems features as well as linearisation of the mathematical model for the system, the paper will investigate ways of identifying state space using Lagrange equations. A proportional-integral-derivative controller is applied to operate the system, to measure the degree of response stabilisation. Additionally, use is made of MATLAB Simulink for system simulations and displaying results for overshoot and rise and settle times. The primary purpose of this study was to investigate how linear quadratic regulators and proportional-integral-derivative controllers can be applied in robotic gymnastics. © 2022 IEEE.