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Prof. Meng-Kun Liu
National Taiwan University of Science and Technology (Taiwan Tech).

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Research Keywords & Expertise

0 vibration analysis
0 Signal Analysis and Processing
0 artificial intelligence
0 Fault Diagnosis of AC Machine Drive System
0 robot force/tactile perception and control

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Fault Diagnosis of AC Machine Drive System
vibration analysis

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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: 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: 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.

Journal article
Published: 24 July 2020 in IEEE Sensors Journal
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A novel wearable human machine interface based on mechanomyogram (MMG) signals was presented in this study. A three-axis accelerometer was fixed to a customized watch strap to measure the MMG signals that were generated by the end of the extensor digitorum muscle. Eight gaming gestures, including clapping, index figure flicking, finger snapping, coin flipping, shooting, wrist extension, wrist flexion and fist-making, were identified in real time. This study extracted the features from both the time signals and the coefficients of the wavelet packet decomposition (WPD), and sequential forward selection (SFS) was used to identify the significant features to improve the classification accuracy and reduce the processing time. The performances of the classifiers such as the k-nearest neighbors (KNN), the support vector machine (SVM), linear discriminant analysis (LDA), and deep neural network (DNN) were compared. After testing the system on 35 subjects aged from 16 to 55 years old, the proposed system has advantages with respect to its convenient portability, stable signal acquisition, low power consumption, and high classification accuracy.

ACS Style

Meng-Kun Liu; Yu-Ting Lin; Zhao-Wei Qiu; Chao-Kuang Kuo; Chi-Kang Wu. Hand Gesture Recognition by a MMG-Based Wearable Device. IEEE Sensors Journal 2020, 20, 14703 -14712.

AMA Style

Meng-Kun Liu, Yu-Ting Lin, Zhao-Wei Qiu, Chao-Kuang Kuo, Chi-Kang Wu. Hand Gesture Recognition by a MMG-Based Wearable Device. IEEE Sensors Journal. 2020; 20 (24):14703-14712.

Chicago/Turabian Style

Meng-Kun Liu; Yu-Ting Lin; Zhao-Wei Qiu; Chao-Kuang Kuo; Chi-Kang Wu. 2020. "Hand Gesture Recognition by a MMG-Based Wearable Device." IEEE Sensors Journal 20, no. 24: 14703-14712.

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.

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.

Journal article
Published: 01 August 2019 in Measurement Science Review
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Motor-driven machines, such as water pumps, air compressors, and fans, are prone to fatigue failures after long operating hours, resulting in catastrophic breakdown. The failures are preceded by faults under which the machines continue to function, but with low efficiency. Most failures that occur frequently in the motor-driven machines are caused by rolling bearing faults, which could be detected by the noise and vibrations during operation. The incipient faults, however, are difficult to identify because of their low signal-to-noise ratio, vulnerability to external disturbances, and non-stationarity. The conventional Fourier spectrum is insufficient for analyzing the transient and non-stationary signals generated by these faults, and hence a novel approach based on wavelet packet decomposition and support vector machine is proposed to distinguish between various types of bearing faults. By using wavelet and statistical methods to extract the features of bearing faults based on time-frequency analysis, the proposed fault diagnosis procedure could identify ball bearing faults successfully.

ACS Style

Meng-Kun Liu; Peng-Yi Weng. Fault Diagnosis of Ball Bearing Elements: A Generic Procedure based on Time-Frequency Analysis. Measurement Science Review 2019, 19, 185 -194.

AMA Style

Meng-Kun Liu, Peng-Yi Weng. Fault Diagnosis of Ball Bearing Elements: A Generic Procedure based on Time-Frequency Analysis. Measurement Science Review. 2019; 19 (4):185-194.

Chicago/Turabian Style

Meng-Kun Liu; Peng-Yi Weng. 2019. "Fault Diagnosis of Ball Bearing Elements: A Generic Procedure based on Time-Frequency Analysis." Measurement Science Review 19, no. 4: 185-194.

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.

Journal article
Published: 28 February 2019 in Energies
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Electric motors are widely used in our society in applications like cars, household appliances, industrial equipment, etc. Costly failures can be avoided by establishing predictive maintenance (PdM) policies or mechanisms for the repair or replacement of the components in electric motors. One of key components in the motors are bearings, and it is critical to measure the key features of bearings to support maintenance decision. This paper proposes a data science approach with embedded statistical data mining and a machine learning algorithm to predict the remaining useful life (RUL) of the bearings in a motor. The vibration signals of the bearings are collected from the experimental platform, and fault detection devices are developed to extract the important features of bearings in time domain and frequency domain. Regression-based models are developed to predict the RUL, and weighted least squares regression (WLS) and feasible generalized least squares regression (FGLS) are used to address the heteroscedasticity problem in the vibration dataset. Support vector regression (SVR) is also applied for prediction benchmarking. Case studies show that the proposed data science approach handled large datasets with ease and predicted the RUL of the bearings with accuracy. The features extracted from time domain are more significant than those extracted from frequency domain, and they benefit engineering knowledge. According to the RUL results, the PdM policy is developed for component replacement at the right moment to avoid the catastrophic equipment failure.

ACS Style

Chia-Yen Lee; Ting-Syun Huang; Meng-Kun Liu; Chen-Yang Lan. Data Science for Vibration Heteroscedasticity and Predictive Maintenance of Rotary Bearings. Energies 2019, 12, 801 .

AMA Style

Chia-Yen Lee, Ting-Syun Huang, Meng-Kun Liu, Chen-Yang Lan. Data Science for Vibration Heteroscedasticity and Predictive Maintenance of Rotary Bearings. Energies. 2019; 12 (5):801.

Chicago/Turabian Style

Chia-Yen Lee; Ting-Syun Huang; Meng-Kun Liu; Chen-Yang Lan. 2019. "Data Science for Vibration Heteroscedasticity and Predictive Maintenance of Rotary Bearings." Energies 12, no. 5: 801.