Showing papers by "Hee-Jun Kang published in 2016"

Bearing Defect Classification Based on Individual Wavelet Local Fisher Discriminant Analysis with Particle Swarm Optimization

Abstract: In order to enhance the performance of bearing defect classification, feature extraction and dimensionality reduction have become important. In order to extract the effective features, wavelet kernel local fisher discriminant analysis (WKLFDA) is first proposed; herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. In order to automatically select the parameters of WKLFDA, a particle swarm optimization (PSO) algorithm is employed, yielding a new PSO-WKLFDA. When compared with the other state-of-the-art methods, the proposed PSO-WKLFDA yields better performance. However, the use of a single global transformation of PSO-WKLFDA for the multiclass task does not provide excellent classification accuracy due to the fact that the projected data still significantly overlap with each other in the projected subspace. In order to enhance the performance of bearing defect classification, a novel method is then proposed by transforming the multiclass task into all possible binary classification tasks using a one-against-one (OAO) strategy. Then, individual PSO-WKLFDA (I-PSO-WKLFDA) is used for extracting effective features of each binary class. The extracted effective features of each binary class are input to a support vector machine (SVM) classifier. Finally, a decision fusion mechanism is employed to merge the classification results from each SVM classifier to identify the bearing condition. Simulation results using synthetic data and experimental results using different bearing fault types show that the proposed method is well suited and effective for bearing defect classification.

Two-stage feature selection for bearing fault diagnosis based on dual-tree complex wavelet transform and empirical mode decomposition

Abstract: This paper presents an automatic fault diagnosis of different rolling element bearing faults using a dual-tree complex wavelet transform, empirical mode decomposition, and a novel two-stage feature selection technique. In this method, dual-tree complex wavelet transform and empirical mode decomposition were used to preprocess the original vibration signal to obtain more accurate fault characteristic information. Then, features in the time domain were extracted from each of the original signals, the coefficients of the dual-tree complex wavelet transform, and some useful intrinsic mode functions to generate a rich combined feature set. Next, a two-stage feature selection algorithm was proposed to generate the smallest set of features that leads to the superior classification accuracy. In the first stage of the two-stage feature selection, we found the candidate feature set using the distance evaluation technique and a k-nearest neighbor classifier. In the second stage, a genetic algorithm-based k-nearest n...

Adaptive Extended Computed Torque Control of 3 DOF Planar Parallel Manipulators Using Neural Network and Error Compensator

Abstract: In this paper, an adaptive extended computed torque controller is proposed for trajectory tracking of 3 degree-of-freedom planar parallel manipulators. The dynamic model, including the modeling errors and uncertainties, is established in the joint space of 3 degree-of-freedom planar parallel manipulators. Based on the dynamic model, an adaptive extended computed torque control scheme is proposed in which a feed-forward neural network is combined with error compensators for adaptive compensating the unknown modeling errors and uncertainties of the parallel manipulators. The weights of the neural network are based on sliding functions and self-tuned online during the tracking control of system without any offline training phase. Using the combination of Sim Mechanics and Solid works, the comparative simulations are conducted for verifying the efficiency of the proposed control scheme.

Fuzzy Neural Sliding Mode Control for Robot Manipulator

Abstract: A fuzzy neural sliding mode controller (FNNSMC) is proposed for robot manipulators. Sliding mode controller is implemented based on two radial basic function neural networks and a fuzzy system. The first neural network is used to estimate the robot dynamic function. The second neural network combines with a fuzzy system to present the switching control term of sliding mode control. This combination resolves the chattering phenomenon. The stability of proposed controller is proven. Finally, simulation is done on a 2-link serial robot manipulator to verify the effectiveness.

Synchronization and Stabilization for Hyperchaotic Systems via a New Modified Finite-time Control

Abstract: This paper investigates complete synchronization and stabilization of hyperchaotic systems. A new modified finite-time control method is introduced to guarantee either the stability of the closed-loop system and finite-time complete synchronization or finite-time stabilization. Since the proposed controller possesses simple construction and easy implementation, it could be utilized in synchronization and stabilization of a general class of hyperchaotic systems. Simulation results are provided to demonstrate the feasibility and the efficacy of the proposed method.

An Adaptive Controller with An Orthogonal Neural Network and A Third Order Sliding Mode Observer for Robot Manipulators

Abstract: In this paper, An adaptive controller with an orthogonal neural network (ONN) and a third order sliding mode(TOSM) observer for robot manipulators is proposed. Firstly, the TOSM observer is designed to observe joint velocities. Then, the ONN is designed to compensate robot dynamic uncertainties on line inside a computed torque control structure. Therefore, the proposed controller allows only position measurements due to the TOSM observer and achieve highly accurate trajectory tracking performance due to the ONN’s uncertainty compensation. Finally, computer simulation for a 2-DOF manipulator is performed to show verify the effectiveness of the proposed controller. 