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Dr. Minh-Quang Tran
Industry 4.0 Implementation Center, National Taiwan University of Science and Technology

Basic Info


Research Keywords & Expertise

0 Machine Learning
0 Signal Processing
0 Tool Condition Monitoring
0 Machining Dynamics
0 Intelligent Systems for Industry 4.0

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Machine Learning
Signal Processing

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Short Biography

Minh-Quang Tran is with the Industry 4.0 Implementation Center, National Taiwan University of Science and Technology, Taiwan. His research interests include tool conditions monitoring, machining dynamics, signal analysis of the manufacturing process, machine learning, and intelligent systems for industry 4.0.

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Journal article
Published: 16 August 2021 in IEEE Access
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In recent years, the internet of things (IoT) represents the main core of Industry 4.0 for cyber-physic systems (CPS) in order to improve the industrial environment. Accordingly, the application of IoT and CPS has been expanded in applied electrical systems and machines. However, cybersecurity represents the main challenge of the implementation of IoT against cyber-attacks. In this regard, this paper proposes a new IoT architecture based on utilizing machine learning techniques to suppress cyber-attacks for providing reliable and secure online monitoring for the induction motor status. In particular, advanced machine learning techniques are utilized here to detect cyber-attacks and motor status with high accuracy. The proposed infrastructure validates the motor status via communication channels and the internet connection with economical cost and less effort on connecting various networks. For this purpose, the CONTACT Element platform for IoT is adopted to visualize the processed data based on machine learning techniques through a graphical dashboard. Once the cyber-attacks signal has been detected, the proposed IoT platform based on machine learning will be visualized automatically as fake data on the dashboard of the IoT platform. Different experimental scenarios with data acquisition are carried out to emphasize the performance of the suggested IoT topology. The results confirm that the proposed IoT architecture based on the machine learning technique can effectively visualize all faults of the motor status as well as the cyber-attacks on the networks. Moreover, all faults of the motor status and the fake data, due to the cyber-attacks, are successfully recognized and visualized on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization, thereby contributing to enhancing the decision-making about the motor status. Furthermore, the introduced IoT architecture with Random Forest algorithm provides an effective detection for the faults on motor due to the vibration under industrial conditions with excellent accuracy of 99.03% that is significantly greater than the other machine learning algorithms. Besides, the proposed IoT has low latency to recognize the motor faults and cyber-attacks to present them in the main dashboard of the IoT platform.

ACS Style

Minh-Quang Tran; Mahmoud Elsisi; Karar Mahmoud; Meng-Kun Liu; Matti Lehtonen; Mohamed M. F. Darwish. Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment. IEEE Access 2021, 9, 115429 -115441.

AMA Style

Minh-Quang Tran, Mahmoud Elsisi, Karar Mahmoud, Meng-Kun Liu, Matti Lehtonen, Mohamed M. F. Darwish. Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment. IEEE Access. 2021; 9 ():115429-115441.

Chicago/Turabian Style

Minh-Quang Tran; Mahmoud Elsisi; Karar Mahmoud; Meng-Kun Liu; Matti Lehtonen; Mohamed M. F. Darwish. 2021. "Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment." IEEE Access 9, no. : 115429-115441.

Journal article
Published: 10 August 2021 in Measurement
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Feature selection represents the main challenge against the classification strategies for several applications of signal processing. Besides, the high computational speed and accuracy represent a critical requirement of chatter diagnosis. In this paper, a new fuzzy entropy measure with a similarity classifier is developed for feature selection vibration diagnosis in CNC machines. The measured signals are filtered using ensemble empirical mode decomposition and analyzed using Hilbert-Huang transform approach. The proposed feature selection technique can decrease noise and this strategy improves the classification accuracy. The proposed fuzzy entropy approach is compared with different feature selection and machine learning methods in the literature. The experimental results and comparative analyses confirm the superiority of the proposed fuzzy entropy method for chatter vibration identification with a low computational burden and a high accuracy of 98% that is significantly greater than other existing intelligent diagnosis techniques.

ACS Style

Minh-Quang Tran; Mahmoud Elsisi; Meng-Kun Liu. Effective feature selection with fuzzy entropy and similarity classifier for chatter vibration diagnosis. Measurement 2021, 184, 109962 .

AMA Style

Minh-Quang Tran, Mahmoud Elsisi, Meng-Kun Liu. Effective feature selection with fuzzy entropy and similarity classifier for chatter vibration diagnosis. Measurement. 2021; 184 ():109962.

Chicago/Turabian Style

Minh-Quang Tran; Mahmoud Elsisi; Meng-Kun Liu. 2021. "Effective feature selection with fuzzy entropy and similarity classifier for chatter vibration diagnosis." Measurement 184, no. : 109962.

Journal article
Published: 05 July 2021 in ISA Transactions
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This paper introduces a newly developed multi-sensor data fusion for the milling chatter detection with a cheap and easy implementation compared with traditional chatter detection schemes. The proposed multi-sensor data fusion utilizes microphone and accelerometer sensors to measure the occurrence of chatter during the milling process. It has the advantageous over the dynamometer in terms of easy installation and low cost. In this paper, the wavelet packet decomposition is adopted to analyze both measured sound and vibration signals. However, the parameters of the wavelet packet decomposition require fine-tuning to provide good performance. Hence the result of the developed scheme has been improved by optimizing the selection of the wavelet packet decomposition parameters including the mother wavelet and the decomposition level based on the kurtosis and crest factors. Furthermore, the important chatter features are selected using the recursive feature elimination method, and its performance is compared with metaheuristic algorithms. Finally, several machine learning techniques have been adopted to classify the cutting stabilities based on the selected features. The results confirm that the proposed multi-sensor data fusion scheme can provide an effective chatter detection under industrial conditions, and it has higher accuracy than the traditional schemes.

ACS Style

Minh-Quang Tran; Meng-Kun Liu; Mahmoud Elsisi. Effective multi-sensor data fusion for chatter detection in milling process. ISA Transactions 2021, 1 .

AMA Style

Minh-Quang Tran, Meng-Kun Liu, Mahmoud Elsisi. Effective multi-sensor data fusion for chatter detection in milling process. ISA Transactions. 2021; ():1.

Chicago/Turabian Style

Minh-Quang Tran; Meng-Kun Liu; Mahmoud Elsisi. 2021. "Effective multi-sensor data fusion for chatter detection in milling process." ISA Transactions , no. : 1.

Journal article
Published: 25 May 2021 in IEEE Access
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Recently, the Internet of Things (IoT) has an important role in the growth and development of digitalized electric power stations while offering ambitious opportunities, specifically real-time monitoring and cybersecurity. In this regard, this paper introduces a novel IoT architecture for the online monitoring of the gas-insulated switchgear (GIS) status instead of the traditional observation methods. The proposed IoT architecture is derived from the concept of the cyber-physic system (CPS) in Industry 4.0. However, the cyber-attacks and the classification of the GIS insulation defects represent the main challenges against the implementation of IoT topology for the online monitoring and tracking of the GIS status. For this purpose, advanced machine learning techniques are utilized to detect cyber-attacks to conduct the paradigm and verification. Different test scenarios on various defects in GIS are performed to demonstrate the effectiveness of the proposed IoT architecture. Partial discharge pulse sequence features are extracted for each defect to represent the inputs for IoT architecture. The results confirm that the proposed IoT architecture based on the machine learning technique, that is the extreme gradient boosting (XGBoost), can visualize all defects in the GIS with different alarms, besides showing the cyber-attacks on the networks effectively. Furthermore, the defects of GIS and the fake data due to the cyber-attacks are recognized and presented on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization to enhance the decision–making about the GIS status.

ACS Style

Mahmoud Elsisi; Minh-Quang Tran; Karar Mahmoud; Diaa-Eldin A. Mansour; Matti Lehtonen; Mohamed M. F. Darwish. Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning. IEEE Access 2021, 9, 1 -1.

AMA Style

Mahmoud Elsisi, Minh-Quang Tran, Karar Mahmoud, Diaa-Eldin A. Mansour, Matti Lehtonen, Mohamed M. F. Darwish. Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning. IEEE Access. 2021; 9 ():1-1.

Chicago/Turabian Style

Mahmoud Elsisi; Minh-Quang Tran; Karar Mahmoud; Diaa-Eldin A. Mansour; Matti Lehtonen; Mohamed M. F. Darwish. 2021. "Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning." IEEE Access 9, no. : 1-1.

Journal article
Published: 02 March 2021 in IEEE Access
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Wind speed fluctuations and load demand variations represent the big challenges against wind energy conversion systems (WECS). Besides, the inefficient measuring devices and the environmental impacts (e.g. temperature, humidity, and noise signals) affect the system equipment, leading to increased system uncertainty issues. In addition, the time delay due to the communication channels can make a gap between the transmitted control signal and the WECS that causes instability for the WECS operation. To tackle these issues, this paper proposes an adaptive neuro-fuzzy inference system (ANFIS) as an effective control technique for blade pitch control of the WECS instead of the conventional controllers. However, the ANFIS requires a suitable dataset for training and testing to adjust its membership functions in order to provide effective performance. In this regard, this paper also suggests an effective strategy to prepare a sufficient dataset for training and testing of the ANFIS controller. Specifically, a new optimization algorithm named the mayfly optimization algorithm (MOA) is developed to find the optimal parameters of the proportional integral derivative (PID) controller to find the optimal dataset for training and testing of the ANFIS controller. To demonstrate the advantages of the proposed technique, it is compared with different three algorithms in the literature. Another contribution is that a new time-domain named figure of demerit is established to confirm the minimization of settling time and the maximum overshoot in a simultaneous manner. A lot of test scenarios are performed to confirm the effectiveness and robustness of the proposed ANFIS based technique. The robustness of the proposed method is verified based on the frequency domain conditions that are driven from Hermite–Biehler theorem. The results emphases that the proposed controller provides superior performance against the wind speed fluctuations, load demand variations, system parameters uncertainties, and the time delay of the communication channels.

ACS Style

Mahmoud Elsisi; Minh-Quang Tran; Karar Mahmoud; Matti Lehtonen; Mohamed M. F. Darwish. Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations. IEEE Access 2021, 9, 37894 -37904.

AMA Style

Mahmoud Elsisi, Minh-Quang Tran, Karar Mahmoud, Matti Lehtonen, Mohamed M. F. Darwish. Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations. IEEE Access. 2021; 9 ():37894-37904.

Chicago/Turabian Style

Mahmoud Elsisi; Minh-Quang Tran; Karar Mahmoud; Matti Lehtonen; Mohamed M. F. Darwish. 2021. "Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations." IEEE Access 9, no. : 37894-37904.

Journal article
Published: 03 February 2021 in Sensors
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Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper’s innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.

ACS Style

Mahmoud Elsisi; Minh-Quang Tran; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. Deep Learning-Based Industry 4.0 and Internet of Things Towards Effective Energy Management for Smart Buildings. Sensors 2021, 21, 1038 .

AMA Style

Mahmoud Elsisi, Minh-Quang Tran, Karar Mahmoud, Matti Lehtonen, Mohamed Darwish. Deep Learning-Based Industry 4.0 and Internet of Things Towards Effective Energy Management for Smart Buildings. Sensors. 2021; 21 (4):1038.

Chicago/Turabian Style

Mahmoud Elsisi; Minh-Quang Tran; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. 2021. "Deep Learning-Based Industry 4.0 and Internet of Things Towards Effective Energy Management for Smart Buildings." Sensors 21, no. 4: 1038.

Journal article
Published: 11 December 2020 in Energies
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A novel concept of wind farm fault detection by monitoring the wind speed in the wake region is proposed in this study. A wind energy dissipation model was coupled with a computational fluid dynamics solver to simulate the fluid field of a wind turbine array, and the wind velocity and direction in the simulation were exported for identifying wind turbine faults. The 3D steady Navier–Stokes equations were solved by using the cell center finite volume method with a second order upwind scheme and a k−ε turbulence model. In addition, the wind energy dissipation model, derived from energy balance and Betz’s law, was added to the Navier–Stokes equations’ source term. The simulation results indicate that the wind speed distribution in the wake region contains significant information regarding multiple wind turbine faults. A feature selection algorithm specifically designed for the analysis of wind flow was proposed to reduce the number of features. This algorithm proved to have better performance than fuzzy entropy measures and recursive feature elimination methods under a limited number of features. As a result, faults in the wind turbine array could be detected and identified by machine learning algorithms.

ACS Style

Minh-Quang Tran; Yi-Chen Li; Chen-Yang Lan; Meng-Kun Liu. Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region. Energies 2020, 13, 6559 .

AMA Style

Minh-Quang Tran, Yi-Chen Li, Chen-Yang Lan, Meng-Kun Liu. Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region. Energies. 2020; 13 (24):6559.

Chicago/Turabian Style

Minh-Quang Tran; Yi-Chen Li; Chen-Yang Lan; Meng-Kun Liu. 2020. "Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region." Energies 13, no. 24: 6559.

Conference paper
Published: 24 November 2020 in Inventive Computation and Information Technologies
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Chatter is problematic during metal cutting process. It destroys the surface finish and reduce productivity and quality of productions. Therefore, prediction and identification of chatter vibration are needed to determine the range of stable cutting conditions. In this paper, the force signal in time domain is used to analyze the behaviors of the milling chatter vibration. The largest Lyapunov exponent index which can describe the different nonlinear characteristics of dynamical system behaviors is used as an effective indicator to distinguish the stable and unstable cutting conditions. The proposed chatter detection approach has been successfully validated by experiment.

ACS Style

Minh-Quang Tran; Meng-Kun Liu; Quoc-Viet Tran. Analysis of Milling Chatter Vibration Based on Force Signal in Time Domain. Inventive Computation and Information Technologies 2020, 192 -199.

AMA Style

Minh-Quang Tran, Meng-Kun Liu, Quoc-Viet Tran. Analysis of Milling Chatter Vibration Based on Force Signal in Time Domain. Inventive Computation and Information Technologies. 2020; ():192-199.

Chicago/Turabian Style

Minh-Quang Tran; Meng-Kun Liu; Quoc-Viet Tran. 2020. "Analysis of Milling Chatter Vibration Based on Force Signal in Time Domain." Inventive Computation and Information Technologies , no. : 192-199.

Original article
Published: 02 March 2020 in The International Journal of Advanced Manufacturing Technology
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In this paper, a novel approach of the real-time chatter detection in the milling process is presented based on the scalogram of the continuous wavelet transform (CWT) and the deep convolutional neural network (CNN). The cutting force signals measured from the stable and unstable cutting conditions were converted into two-dimensional images using the CWT. When chatter occurs, the amount of energy at the tooth passing frequency and its harmonics are shifted toward the chatter frequency. Hence, the scalogram images can serve as input to the CNN framework to identify the stable, transitive, and unstable cutting states. The proposed method does not require the subjective feature-generation and feature-selection procedures, and its classification accuracy of 99.67% is higher than the conventional machine learning techniques described in the existing literature. The result demonstrates that the proposed method can effectively detect the occurrence of chatter.

ACS Style

Minh-Quang Tran; Meng-Kun Liu; Quoc-Viet Tran. Milling chatter detection using scalogram and deep convolutional neural network. The International Journal of Advanced Manufacturing Technology 2020, 107, 1505 -1516.

AMA Style

Minh-Quang Tran, Meng-Kun Liu, Quoc-Viet Tran. Milling chatter detection using scalogram and deep convolutional neural network. The International Journal of Advanced Manufacturing Technology. 2020; 107 (3-4):1505-1516.

Chicago/Turabian Style

Minh-Quang Tran; Meng-Kun Liu; Quoc-Viet Tran. 2020. "Milling chatter detection using scalogram and deep convolutional neural network." The International Journal of Advanced Manufacturing Technology 107, no. 3-4: 1505-1516.

Original article
Published: 10 January 2020 in Journal of Mechanical Science and Technology
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Chatter causes machining instability and reduces productivity in the metal cutting process. It has negative effects on the surface finish, dimensional accuracy, tool life and machine life. Chatter identification is therefore necessary to control, prevent, or eliminate chatter and to determine the stable machining condition. Previous studies of chatter detection used either model-based or signal-based methods, and each of them has its drawback. Model-based methods use cutting dynamics to develop stability lobe diagram to predict the occurrence of chatter, but the off-line stability estimation couldn’t detect chatter in real time. Signal-based methods apply mostly Fourier analysis to the cutting or vibration signals to identify chatter, but they are heuristic methods and do not consider the cutting dynamics. In this study, the model-based and signal-based chatter detection methods were thoroughly investigated. As a result, a hybrid model- and signal-based chatter detection method was proposed. By analyzing the residual between the force measurement and the output of the cutting force model, milling chatter could be detected and identified efficiently during the milling process.

ACS Style

Meng-Kun Liu; Minh-Quang Tran; Chunhui Chung; Yi-Wen Qui. Hybrid model- and signal-based chatter detection in the milling process. Journal of Mechanical Science and Technology 2020, 34, 1 -10.

AMA Style

Meng-Kun Liu, Minh-Quang Tran, Chunhui Chung, Yi-Wen Qui. Hybrid model- and signal-based chatter detection in the milling process. Journal of Mechanical Science and Technology. 2020; 34 (1):1-10.

Chicago/Turabian Style

Meng-Kun Liu; Minh-Quang Tran; Chunhui Chung; Yi-Wen Qui. 2020. "Hybrid model- and signal-based chatter detection in the milling process." Journal of Mechanical Science and Technology 34, no. 1: 1-10.

Regular paper
Published: 01 January 2020 in International Journal of Precision Engineering and Manufacturing
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A model of process damping in milling was developed in this study. The process damping is a critical parameter to increase the stable cutting region at low cutting speed to avoid chatter. The previous studies conducted experiments to estimate the process damping. Nevertheless, it is time and cost consuming. A model of dynamic cutting force was employed in this study. The plowing force generated by the flank-wave contact is considered as the main source of process damping to dissipate vibratory energy during cutting. In addition to the material properties and plowing force, the effects of chatter amplitude and wavelength, which result in the various indentation conditions and affect the coefficient of process damping, were also considered. The consideration of wavy contact surface and indentation area in this model allows quick determination of cutting stability conditions with high accuracy. The process damping coefficient estimated by the proposed model successfully represented the effect of the tool wear on chatter because of the change of tool geometry. Experiments were conducted to verify the new model.

ACS Style

Chunhui Chung; Minh-Quang Tran; Meng-Kun Liu. Estimation of Process Damping Coefficient Using Dynamic Cutting Force Model. International Journal of Precision Engineering and Manufacturing 2020, 21, 623 -632.

AMA Style

Chunhui Chung, Minh-Quang Tran, Meng-Kun Liu. Estimation of Process Damping Coefficient Using Dynamic Cutting Force Model. International Journal of Precision Engineering and Manufacturing. 2020; 21 (4):623-632.

Chicago/Turabian Style

Chunhui Chung; Minh-Quang Tran; Meng-Kun Liu. 2020. "Estimation of Process Damping Coefficient Using Dynamic Cutting Force Model." International Journal of Precision Engineering and Manufacturing 21, no. 4: 623-632.

Conference paper
Published: 01 December 2019 in 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)
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Heart rate estimation from fitness plays an important role in the evaluation of fitness exercises. Conventional approaches use the photoplethysmography (PPG) sensor to consider the change of light absorption on the wrist skin for heart rate estimation. However, users are required to buy smartwatches for using this function. Various approaches based on video analysis are recently implemented for surveillance purpose. However, it is unstable for motion scenario such as fitness exercises due to the color distortion induced by movement. POS and CHROM are introduced to address this issue. Since the fixed projection planes from POS and CHROM are given in several sources of light, it is not widely applied for surveillance applications. Therefore, a novel projection plane that is adaptively changed with the lighting environment is proposed to estimate the heart rate from fitness videos in ambient light. Moreover, image and digital signal processing techniques are also applied to extract the clean pulse signal from a novel projection plane. From the experiments conducted, the proposed approach outperformed the existing approaches to be the best model for heart rate estimation from fitness videos with the accuracy up to 91.08%.

ACS Style

Quoc-Viet Tran; Shun-Feng Su; Minh-Quang Tran. Color Distortion Removal for Heart Rate Monitoring in Fitness Scenario. 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) 2019, 369 -374.

AMA Style

Quoc-Viet Tran, Shun-Feng Su, Minh-Quang Tran. Color Distortion Removal for Heart Rate Monitoring in Fitness Scenario. 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). 2019; ():369-374.

Chicago/Turabian Style

Quoc-Viet Tran; Shun-Feng Su; Minh-Quang Tran. 2019. "Color Distortion Removal for Heart Rate Monitoring in Fitness Scenario." 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) , no. : 369-374.

Conference paper
Published: 28 October 2019 in IOP Conference Series: Materials Science and Engineering
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ACS Style

Minh-Quang Tran; Meng- Kun Liu. Chatter Identification in End Milling Process Based on Cutting Force Signal Processing. IOP Conference Series: Materials Science and Engineering 2019, 654, 1 .

AMA Style

Minh-Quang Tran, Meng- Kun Liu. Chatter Identification in End Milling Process Based on Cutting Force Signal Processing. IOP Conference Series: Materials Science and Engineering. 2019; 654 ():1.

Chicago/Turabian Style

Minh-Quang Tran; Meng- Kun Liu. 2019. "Chatter Identification in End Milling Process Based on Cutting Force Signal Processing." IOP Conference Series: Materials Science and Engineering 654, no. : 1.

Research article
Published: 19 September 2019 in Shock and Vibration
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Induction machines are widely used in the industry as one of the major actuators, such as water pumps, air compressors, and fans. It is necessary to monitor and diagnose these induction motors to prevent any sudden shut downs caused by premature failures. Numerous fault detection and isolation techniques for the diagnosis of induction machines have been proposed over the past few decades. Among these techniques, motor current signature analysis (MCSA) and vibration analysis are two of the most common signal-based condition monitoring methods. They are often adopted independently, but each method has its strengths and weaknesses. This research proposed a systemic method to integrate the information received from the vibration and current measurements. We applied the wavelet packet decomposition to extract the time-frequency features of the vibration and current measurements and used the support vector machines as classifiers for the initial decision-making. The significant features were identified, and the performances of several classifiers were compared. As a result, the decision-level sensor fusion based on the Sugeno fuzzy integral was proposed to integrate the vibration and current information to improve the accuracy of the diagnosis.

ACS Style

Meng-Kun Liu; Minh-Quang Tran; Peng-Yi Weng. Fusion of Vibration and Current Signatures for the Fault Diagnosis of Induction Machines. Shock and Vibration 2019, 2019, 1 -17.

AMA Style

Meng-Kun Liu, Minh-Quang Tran, Peng-Yi Weng. Fusion of Vibration and Current Signatures for the Fault Diagnosis of Induction Machines. Shock and Vibration. 2019; 2019 ():1-17.

Chicago/Turabian Style

Meng-Kun Liu; Minh-Quang Tran; Peng-Yi Weng. 2019. "Fusion of Vibration and Current Signatures for the Fault Diagnosis of Induction Machines." Shock and Vibration 2019, no. : 1-17.

Original article
Published: 03 May 2019 in The International Journal of Advanced Manufacturing Technology
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Milling plays a core role in the manufacturing industry. If a milling tool suffers severe wear or breakage during the manufacturing process, it requires immediate attention to prevent precision errors and poor surface quality. Previous studies applied multiple sensors, such as force sensors, microphones, and acoustic emission sensors, to extract the features related to tool wear. However, owing to the complex mechanism causing tool wear, the results of these studies were not only quantitatively different but also varied with respect to cutting conditions. Therefore, this research discussed the relationship between sound signal and tool wear under multiple cutting conditions. Collinearity diagnostics and a stepwise regression procedure were used to optimize the time–frequency statistic features generated from the wavelet packet decomposition. The regression and an artificial neural network model were developed to predict the degree of tool wear. The proposed method provided solid statistical support for the feature selection process, and the results showed that it will maintain prediction accuracy regardless of the different cutting conditions. Further, the method achieved better accuracy than the commonly used root mean square value.

ACS Style

Meng-Kun Liu; Yi-Heng Tseng; Minh-Quang Tran. Tool wear monitoring and prediction based on sound signal. The International Journal of Advanced Manufacturing Technology 2019, 103, 3361 -3373.

AMA Style

Meng-Kun Liu, Yi-Heng Tseng, Minh-Quang Tran. Tool wear monitoring and prediction based on sound signal. The International Journal of Advanced Manufacturing Technology. 2019; 103 (9-12):3361-3373.

Chicago/Turabian Style

Meng-Kun Liu; Yi-Heng Tseng; Minh-Quang Tran. 2019. "Tool wear monitoring and prediction based on sound signal." The International Journal of Advanced Manufacturing Technology 103, no. 9-12: 3361-3373.

Conference paper
Published: 04 June 2017 in Volume 1: Processes
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Chatter identification is necessary in order to achieve stable machining conditions. However, the linear approximation in regenerative chatter vibration is problematic because of the rich nonlinear characteristics in machining. In this study, a novel method to detect chatter is proposed. Firstly, measured cutting force signals are decomposed into a set of intrinsic mode functions by using ensemble empirical mode decomposition. Hilbert transform is following to extract the instantaneous frequency. Fast Fourier transform is also utilized for each intrinsic mode function to determine the intrinsic mode function that contains rich chatter. Finally, the standard deviation and energy ratio in frequency domain of intrinsic mode functions are found as simply dimensionless chatter indicators. The effectively proposed approach is validated by analyzing the machined surface topography and also compared to the stability lobe diagram.

ACS Style

Meng-Kun Liu; Quang M. Tran; Yi-Wen Qui; Chun-Hui Chung. Chatter Detection in Milling Process Based on Time-Frequency Analysis. Volume 1: Processes 2017, 1 .

AMA Style

Meng-Kun Liu, Quang M. Tran, Yi-Wen Qui, Chun-Hui Chung. Chatter Detection in Milling Process Based on Time-Frequency Analysis. Volume 1: Processes. 2017; ():1.

Chicago/Turabian Style

Meng-Kun Liu; Quang M. Tran; Yi-Wen Qui; Chun-Hui Chung. 2017. "Chatter Detection in Milling Process Based on Time-Frequency Analysis." Volume 1: Processes , no. : 1.