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Chun-Yao Lee
Department of Electrical Engineering, Chung Yuan Christian University, No. 200, Zhongbei Road, Zhongli District, Taoyuan City 320, Taiwan

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Short Biography

Chun-Yao Lee (Member, IEEE) received his Ph.D. degree in Electrical Engineering from the National Taiwan University of Science and Technology, Taipei, Taiwan, in 2007. Sponsored by the Ministry of Science and Technology of R.O.C., he was a visiting scholar with the University of Washington, Seattle, from 2004 to 2005. He was also a Distribution System Designer with the Engineering Division, Taipei Government, from 2001 to 2008. He joined Chung Yuan Christian University as a Faculty Member in 2008. His research interests include power distribution, optimization algorithms, and damage diagnosis.

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Journal article
Published: 18 July 2021 in Symmetry
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A fault diagnosis system with the ability to recognize many different faults obviously has a certain complexity. Therefore, improving the performance of similar systems has attracted much research interest. This article proposes a system of feature ranking and differential evolution for feature selection in BLDC fault diagnosis. First, this study used the Hilbert–Huang transform (HHT) to extract the features of four different types of brushless DC motor Hall signal. When there is a fault, the symmetry of the Hall signal will be influenced. Second, we used feature selection based on a distance discriminant (FSDD) to calculate the feature factors which base on the category separability of features to select the features which have a positive correlation with the types. The features were entered sequentially into the two supervised classifiers: backpropagation neural network (BPNN) and linear discriminant analysis (LDA), and the identification results were then evaluated. The feature input for the classifier was derived from the FSDD, and then we optimized the feature rank using differential evolution (DE). Finally, the results were verified from the BLDC motor’s operating environment simulation with the same features by adding appropriate signal-to-noise ratio magnitudes. The identification system obtained an accuracy rate of 96% when there were 14 features. Additionally, the experimental results show that the proposed system has a robust anti-noise ability, and the accuracy rate is 92.04%, even when 20 dB of white Gaussian noise is added to the signal. Moreover, compared with the systems established from the discrete wavelet transform (DWT) and a variety of classifiers, our proposed system has a higher accuracy with fewer features.

ACS Style

Chun-Yao Lee; Chen-Hsu Hung. Feature Ranking and Differential Evolution for Feature Selection in Brushless DC Motor Fault Diagnosis. Symmetry 2021, 13, 1291 .

AMA Style

Chun-Yao Lee, Chen-Hsu Hung. Feature Ranking and Differential Evolution for Feature Selection in Brushless DC Motor Fault Diagnosis. Symmetry. 2021; 13 (7):1291.

Chicago/Turabian Style

Chun-Yao Lee; Chen-Hsu Hung. 2021. "Feature Ranking and Differential Evolution for Feature Selection in Brushless DC Motor Fault Diagnosis." Symmetry 13, no. 7: 1291.

Journal article
Published: 24 June 2021 in Mathematics
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This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

ACS Style

Chun-Yao Lee; Guang-Lin Zhuo. A Hybrid Whale Optimization Algorithm for Global Optimization. Mathematics 2021, 9, 1477 .

AMA Style

Chun-Yao Lee, Guang-Lin Zhuo. A Hybrid Whale Optimization Algorithm for Global Optimization. Mathematics. 2021; 9 (13):1477.

Chicago/Turabian Style

Chun-Yao Lee; Guang-Lin Zhuo. 2021. "A Hybrid Whale Optimization Algorithm for Global Optimization." Mathematics 9, no. 13: 1477.

Journal article
Published: 16 March 2021 in Symmetry
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This article proposes an effective rotor fault diagnosis model of an induction motor (IM) based on local mean decomposition (LMD) and wavelet packet decomposition (WPD)-based multilayer signal analysis and hybrid genetic binary chicken swarm optimization (HGBCSO) for feature selection. Based on the multilayer signal analysis, this technique can reduce the dimension of raw data, extract potential features, and remove background noise. To compare the validity of the proposed HGBCSO method, three well-known evolutionary algorithms are adopted, including binary-particle swarm optimization (BPSO), binary-bat algorithm (BBA), and binary-chicken swarm optimization (BCSO). In addition, the robustness of three classifiers including the decision tree (DT), support vector machine (SVM), and naive Bayes (NB) was compared to select the best model to detect the rotor bar fault. The results showed that the proposed HGBCSO algorithm can obtain better global exploration ability and a lower number of selected features than other evolutionary algorithms that are adopted in this research. In conclusion, the proposed model can reduce the dimension of raw data and achieve high robustness.

ACS Style

Chun-Yao Lee; Guang-Lin Zhuo. Effective Rotor Fault Diagnosis Model Using Multilayer Signal Analysis and Hybrid Genetic Binary Chicken Swarm Optimization. Symmetry 2021, 13, 487 .

AMA Style

Chun-Yao Lee, Guang-Lin Zhuo. Effective Rotor Fault Diagnosis Model Using Multilayer Signal Analysis and Hybrid Genetic Binary Chicken Swarm Optimization. Symmetry. 2021; 13 (3):487.

Chicago/Turabian Style

Chun-Yao Lee; Guang-Lin Zhuo. 2021. "Effective Rotor Fault Diagnosis Model Using Multilayer Signal Analysis and Hybrid Genetic Binary Chicken Swarm Optimization." Symmetry 13, no. 3: 487.

Journal article
Published: 08 January 2021 in Symmetry
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This paper presents a feature selection model based on mean impact value (MIV) to solve induction motor (IM) fault diagnosis on the current signal. In this paper, particle swarm optimization (PSO) is combined with back propagation neural network (BPNN) to classify the current signal of IM. First, the purpose of this study is to establish IM fault diagnosis system. Additionally, this study proposes a feature selection process that is composed of MIV, whose objective is to reduce the number of classifier input features. Secondly, the features are extracted as a feature database after analyzing the current signal of IM, and the fault diagnosis is established through the model of PSO-BPNN. Finally, redundant features are deleted through this feature selection process and a classifier is built. The result shows that the feature selection model based on MIV can filter the features effectively at a signal to noise ratio of 30 dB and 20 dB for the IM fault detection problem. In addition, the computing time of BPNN is also reduced which is helpful for online detection.

ACS Style

Chun-Yao Lee; Hong-Yi Ou. Induction Motor Multiclass Fault Diagnosis Based on Mean Impact Value and PSO-BPNN. Symmetry 2021, 13, 104 .

AMA Style

Chun-Yao Lee, Hong-Yi Ou. Induction Motor Multiclass Fault Diagnosis Based on Mean Impact Value and PSO-BPNN. Symmetry. 2021; 13 (1):104.

Chicago/Turabian Style

Chun-Yao Lee; Hong-Yi Ou. 2021. "Induction Motor Multiclass Fault Diagnosis Based on Mean Impact Value and PSO-BPNN." Symmetry 13, no. 1: 104.

Journal article
Published: 01 January 2021 in Energies
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Besides achieving an optimal scheduling generator, the operation safety of the generator itself needs to be focused on. The development of the virtual visualization of a generator capability curve simulation to visualize the operation condition of a generator is proposed in this paper. In this paper, a neural network is applied to redraw the original generator’s capability curve. The virtual visualization of a generator’s capability curve can simulate the generator’s operating condition considering the limitation of the constraints on the various elements of the generator. Furthermore, it is able to show the various possibilities that occur in the operation of a generator in reality, and it can even simulate special conditions which are based on various conditions.

ACS Style

Chun-Yao Lee; Maickel Tuegeh. Virtual Visualization of Generator Operation Condition through Generator Capability Curve. Energies 2021, 14, 185 .

AMA Style

Chun-Yao Lee, Maickel Tuegeh. Virtual Visualization of Generator Operation Condition through Generator Capability Curve. Energies. 2021; 14 (1):185.

Chicago/Turabian Style

Chun-Yao Lee; Maickel Tuegeh. 2021. "Virtual Visualization of Generator Operation Condition through Generator Capability Curve." Energies 14, no. 1: 185.

Journal article
Published: 22 October 2020 in Symmetry
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This paper proposes a diagnosis method, combining signal analysis and classification models, to the rotor defect problems of motors. Two manufacture technologies, nonmagnetic high-temperature resistant ceramic adhesive and electrical discharge machining (EDM), are applied to make testing samples, including blowhole and perforation defects of rotor bars in this study. The typical multiresolution analysis (MRA) model is used to analyze acquired source current signals of motors. The features are extracted from the signals of each column of MRA-matrix, including maximum, mean, standard deviation, root-mean-square, and summation. The typical back-propagation neural network (BPNN) model is used to diagnose the rotor bar defects of motors, and then the various signal-to-noise ratio (SNR) of white Gaussian noise (WGN), 30, 25, and 20 dB, are added to the signals to verify the robustness of the proposed method. The results show the availability of the proposed method to diagnose the rotor bar defects of motors.

ACS Style

Chun-Yao Lee; Kuan-Yu Huang; Lai-Yu Jen; Guang-Lin Zhuo. Diagnosis of Defective Rotor Bars in Induction Motors. Symmetry 2020, 12, 1753 .

AMA Style

Chun-Yao Lee, Kuan-Yu Huang, Lai-Yu Jen, Guang-Lin Zhuo. Diagnosis of Defective Rotor Bars in Induction Motors. Symmetry. 2020; 12 (11):1753.

Chicago/Turabian Style

Chun-Yao Lee; Kuan-Yu Huang; Lai-Yu Jen; Guang-Lin Zhuo. 2020. "Diagnosis of Defective Rotor Bars in Induction Motors." Symmetry 12, no. 11: 1753.

Journal article
Published: 21 October 2020 in Processes
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This paper proposes a motor fault detection method based on wavelet transform (WT) and improved PSO-BP neural network which is combined with improved particle swarm optimization (PSO) and a back propagation (BP) neural network with linearly increasing inertia weight. First, this research used WT to analyze the current signals of the healthy motor, bearing damage motor, stator winding inter-turn short circuit motor, and broken rotor bar motor. Second, features after completing the signal analysis were extracted, and three types of classifiers were used to classify. The results show that the improved PSO-BP neural network can effectively detect the cause of failure. In addition, in order to simulate the actual operating environment of the motor, this study added white noise with signal noise ratios of 30 dB, 25 dB, and 20 dB to verify that this model has a better anti-noise ability.

ACS Style

Chun-Yao Lee; Yi-Hsin Cheng. Motor Fault Detection Using Wavelet Transform and Improved PSO-BP Neural Network. Processes 2020, 8, 1322 .

AMA Style

Chun-Yao Lee, Yi-Hsin Cheng. Motor Fault Detection Using Wavelet Transform and Improved PSO-BP Neural Network. Processes. 2020; 8 (10):1322.

Chicago/Turabian Style

Chun-Yao Lee; Yi-Hsin Cheng. 2020. "Motor Fault Detection Using Wavelet Transform and Improved PSO-BP Neural Network." Processes 8, no. 10: 1322.

Journal article
Published: 21 October 2020 in Energies
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In this paper, we propose using particle swarm optimization (PSO) which can improve weighted k-nearest neighbors (PWKNN) to diagnose the failure of a wind power system. PWKNN adjusts weight to correctly reflect the importance of features and uses the distance judgment strategy to figure out the identical probability of multi-label classification. The PSO optimizes the weight and parameter k of PWKNN. This testing is based on four classified conditions of the 300 W wind generator which include healthy, loss of lubrication in the gearbox, angular misaligned rotor, and bearing fault. Current signals are used to measure the conditions. This testing tends to establish a feature database that makes up or trains classifiers through feature extraction. Not lowering the classification accuracy, the correlation coefficient of feature selection is applied to eliminate irrelevant features and to diminish the runtime of classifiers. A comparison with other traditional classifiers, i.e., backpropagation neural network (BPNN), k-nearest neighbor (k-NN), and radial basis function network (RBFN) shows that PWKNN has a higher classification accuracy. The feature selection can diminish the average features from 16 to 2.8 and can reduce the runtime by 61%. This testing can classify these four conditions accurately without being affected by noise and it can reach an accuracy of 83% in the condition of signal-to-noise ratio (SNR) is 20dB. The results show that the PWKNN approach is capable of diagnosing the failure of a wind power system.

ACS Style

Chun-Yao Lee; Kuan-Yu Huang; Yi-Xing Shen; Yao-Chen Lee. Improved Weighted k-Nearest Neighbor Based on PSO for Wind Power System State Recognition. Energies 2020, 13, 5520 .

AMA Style

Chun-Yao Lee, Kuan-Yu Huang, Yi-Xing Shen, Yao-Chen Lee. Improved Weighted k-Nearest Neighbor Based on PSO for Wind Power System State Recognition. Energies. 2020; 13 (20):5520.

Chicago/Turabian Style

Chun-Yao Lee; Kuan-Yu Huang; Yi-Xing Shen; Yao-Chen Lee. 2020. "Improved Weighted k-Nearest Neighbor Based on PSO for Wind Power System State Recognition." Energies 13, no. 20: 5520.

Journal article
Published: 29 August 2020 in Processes
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This paper proposes a feature selection (FS) approach, namely, correlation and fitness value-based feature selection (CFFS). CFFS is an improvement feature selection approach of correlation-based feature selection (CFS) for the common failure cases of the induction motor. CFFS establishes the induction motor fault detection (FD) system with artificial neural network (ANN). This study analyzes the current signal of the induction motor with multiresolution analysis (MRA), extracts the features, and uses feature selection approaches (ReliefF, CFS, and CFFS) to reduce the number of features and maintain the accuracy of the induction motor fault detection system. Finally, the induction motor fault detection system is trained by the feature selection approaches selected features. The best induction motor fault detection system will be established through the comparison of the efficiency of these FS approaches.

ACS Style

Chun-Yao Lee; Meng-Syun Wen. Establish Induction Motor Fault Diagnosis System Based on Feature Selection Approaches with MRA. Processes 2020, 8, 1055 .

AMA Style

Chun-Yao Lee, Meng-Syun Wen. Establish Induction Motor Fault Diagnosis System Based on Feature Selection Approaches with MRA. Processes. 2020; 8 (9):1055.

Chicago/Turabian Style

Chun-Yao Lee; Meng-Syun Wen. 2020. "Establish Induction Motor Fault Diagnosis System Based on Feature Selection Approaches with MRA." Processes 8, no. 9: 1055.

Journal article
Published: 26 August 2020 in Energies
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This paper presents four refined distance models to the application of forecasting short-term electricity price namely Euclidean norm, Manhattan distance, cosine coefficient, and Pearson correlation coefficient. The four refined models were constructed and used to select the days, which are like a reference day in electricity prices and loads, called similar days in this study. Using the similar days, the electricity prices of a forecast day were further obtained by similar day regression (SDR) and similar day based artificial neural network (SDANN). The simulation results of the case of the PJM (Pennsylvania, New Jersey and Maryland) interchange energy market indicate the superiority and availability of the selection 45 framework days and three similar days based on Pearson correlation coefficient model.

ACS Style

Chun-Yao Lee; Chang-En Wu. Short-Term Electricity Price Forecasting Based on Similar Day-Based Neural Network. Energies 2020, 13, 4408 .

AMA Style

Chun-Yao Lee, Chang-En Wu. Short-Term Electricity Price Forecasting Based on Similar Day-Based Neural Network. Energies. 2020; 13 (17):4408.

Chicago/Turabian Style

Chun-Yao Lee; Chang-En Wu. 2020. "Short-Term Electricity Price Forecasting Based on Similar Day-Based Neural Network." Energies 13, no. 17: 4408.

Journal article
Published: 04 August 2020 in Applied Sciences
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This study proposes a fast correlation-based filter with particle-swarm optimization method. In FCBF–PSO, the weights of the features selected by the fast correlation-based filter are optimized and combined with backpropagation neural network as a classifier to identify the faults of induction motors. Three significant parts were applied to support the FCBF–PSO. First, Hilbert–Huang transforms were used to analyze the current signals of motor normal, bearing damage, broken rotor bars and short circuits in stator windings. Second, ReliefF, symmetrical uncertainty and FCBF three feature-selection methods were applied to select the important features after the feature was captured. Moreover, the accuracy comparison was performed. Third, particle-swarm optimization (PSO) was combined to optimize the selected feature weights which were used to obtain the best solution. The results showed excellent performance of the FCBF–PSO for the induction motor fault classification such as had fewer feature numbers and better identification ability. In addition, the analyzed of the induction motor fault in this study was applied with the different operating environments, namely, SNR = 40 dB, SNR = 30 dB and SNR = 20 dB. The FCBF–PSO proposed by this research could also get the higher accuracy than typical feature-selection methods of ReliefF, SU and FCBF.

ACS Style

Chun-Yao Lee; Wen-Cheng Lin. Induction Motor Fault Classification Based on FCBF-PSO Feature Selection Method. Applied Sciences 2020, 10, 5383 .

AMA Style

Chun-Yao Lee, Wen-Cheng Lin. Induction Motor Fault Classification Based on FCBF-PSO Feature Selection Method. Applied Sciences. 2020; 10 (15):5383.

Chicago/Turabian Style

Chun-Yao Lee; Wen-Cheng Lin. 2020. "Induction Motor Fault Classification Based on FCBF-PSO Feature Selection Method." Applied Sciences 10, no. 15: 5383.

Journal article
Published: 20 July 2020 in Energies
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A modified particle swarm optimization and incorporated chaotic search to solve economic dispatch problems for smooth and non-smooth cost functions, considering prohibited operating zones and valve-point effects is proposed in this paper. An inertia weight modification of particle swarm optimization is introduced to enhance algorithm performance and generate optimal solutions with stable solution accuracy and offers faster convergence characteristic. Moreover, an incorporation of chaotic search, called logistic map, is used to increase the global searching capability. To demonstrate the effectiveness and feasibility of the proposed algorithm compared to the several existing methods in the literature, five systems with different criteria are verified. The results show the excellent performance of the proposed method to solve economic dispatch problems.

ACS Style

Chun-Yao Lee; Maickel Tuegeh. An Optimal Solution for Smooth and Non-Smooth Cost Functions-Based Economic Dispatch Problem. Energies 2020, 13, 3721 .

AMA Style

Chun-Yao Lee, Maickel Tuegeh. An Optimal Solution for Smooth and Non-Smooth Cost Functions-Based Economic Dispatch Problem. Energies. 2020; 13 (14):3721.

Chicago/Turabian Style

Chun-Yao Lee; Maickel Tuegeh. 2020. "An Optimal Solution for Smooth and Non-Smooth Cost Functions-Based Economic Dispatch Problem." Energies 13, no. 14: 3721.

Journal article
Published: 26 June 2019 in Applied Sciences
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This paper proposes a model which uses the greedy algorithm to select the optimal intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD), namely the greedy empirical mode decomposition (GEMD) model. The optimal IMFs can more sufficiently represent the characteristics of damage bearings since the proposed GEMD model effectively selects some IMFs not affected by noise. To validate the superiority of the proposed GEMD model, various damage types of motor bearings were shaped by electrical discharge machining (EDM) in this experiment. The measured motor current signals of various types were decomposed to IMFs by using EMD. Then the optimal IMFs can be obtained by using the proposed GEMD model. The results show that the Hilbert–Huang transform (HHT) spectrums when using the optimal IMFs become easier in the detection system than when using all IMFs. Simultaneously, the detection accuracy of motor bearing damages is increased by using the features extracted from the lower complexity HHT spectrum. The average detection accuracy can be also improved from 69.5% to 74.6% by using the features extracted from the GEMD-HHT spectrum even in a noise interference 10dB

ACS Style

Chun-Yao Lee; Kuan-Yu Huang; Yu-Hua Hsieh; Po-Hung Chen. Optimal Intrinsic Mode Function Based Detection of Motor Bearing Damages. Applied Sciences 2019, 9, 2587 .

AMA Style

Chun-Yao Lee, Kuan-Yu Huang, Yu-Hua Hsieh, Po-Hung Chen. Optimal Intrinsic Mode Function Based Detection of Motor Bearing Damages. Applied Sciences. 2019; 9 (13):2587.

Chicago/Turabian Style

Chun-Yao Lee; Kuan-Yu Huang; Yu-Hua Hsieh; Po-Hung Chen. 2019. "Optimal Intrinsic Mode Function Based Detection of Motor Bearing Damages." Applied Sciences 9, no. 13: 2587.