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Chen-Yang Lan
Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan

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Journal article
Published: 31 August 2021 in Sensors
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A novel framework of model-based fault detection and identification (MFDI) for induction motor (IM)-driven rotating machinery (RM) is proposed in this study. A data-driven subspace identification (SID) algorithm is employed to obtain the IM state-space model from the voltage and current signals in a quasi-steady-state condition. This study aims to improve the frequency–domain fault detection and identification (FDI) by replacing the current signal with a residual signal where a thresholding method is applied to the residual signal. Through the residual spectrum and threshold comparison, a binary decision is made to find fault signatures in the spectrum. The statistical Q-function is used to generate the fault frequency band to distinguish between the fault signature and the noise signature. The experiment in this study is performed on a wastewater pump in an existing industrial facility to verify the proposed FDI. Two faulty conditions with mathematically known and mathematically unknown faulty signatures are experimented with and diagnosed. The study results present that the residual spectrum demonstrated to be more sensitive to fault signatures compare to the current spectrum. The proposed FDI has successfully shown to identify the fault signatures even for the mathematically unknown faulty signatures.

ACS Style

Widagdo Purbowaskito; Chen-Yang Lan; Kenny Fuh. A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum. Sensors 2021, 21, 5865 .

AMA Style

Widagdo Purbowaskito, Chen-Yang Lan, Kenny Fuh. A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum. Sensors. 2021; 21 (17):5865.

Chicago/Turabian Style

Widagdo Purbowaskito; Chen-Yang Lan; Kenny Fuh. 2021. "A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum." Sensors 21, no. 17: 5865.

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: 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.