Mr. Jaewoong Bae Profile

Jaewoong Bae

at KAIST

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Proceedings Article | 20 April 2022 Presentation + Paper

Utilization of high-fidelity simulation data for data augmentation of artificial neural net-based rotor faults diagnosis

Daeguen Lim, Wonho Jung, Jaewoong Bae, Yonghwa Park

Proceedings Volume 12043, 120431A (2022) https://doi.org/10.1117/12.2612314

KEYWORDS: Data modeling, Systems modeling, Data acquisition, Numerical simulations, Finite element methods, Neurons, Motion models, Model-based design, Modal analysis, Machine learning

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Nowadays, vibration monitoring system (VMS) using machine learning has been increasingly used to predict rotor faults. However, a sufficient amount of fault data is harder to collect practically than the normal data, as a result, the imbalanced training data set can significantly affect the accuracy of the trained classifier. In this paper, we proposed a data augmentation approach that uses physics-based high-fidelity dynamics simulation as an alternative to acquiring practical fault data. The overall procedure includes: (1) The high-fidelity numerical simulation model reflecting the behavior of rotor to obtain the vibration signature of fault data; and, (2) data augmentation with the simulation fault data in conjunction with experimental normal and fault datasets. A fully connected Neural Network (FCNN) is applied to build the classification model that identifies rotor faults focusing on mass unbalance. The rotor system considered in this study consists of rigid discs, shaft, eccentric mass, and bearing housings. The numerical simulation model in this work considers high-fidelity physical behaviors such flexible multibody dynamics having centrifugal force and gyroscopic effects. Time domain data of the vertical and horizontal vibration responses of bearing housings are obtained from simulation and then FFT is applied to extract the main feature in frequency domain, which is the amplitude of the 1X harmonics of the vibration responses. The data augmentation is accomplished with frequency domain data both from simulation results and experimental acquisition. This approach can tackle data imbalance problem which is one of the most critical hurdles in neural net-based. fault diagnosis. From experimental verification, high accuracies more than 90 % of rotor fault diagnosis, which demonstrates the effectiveness of the proposed framework compared to the model with insufficient fault data.

Proceedings Article | 20 April 2022 Presentation + Paper

Time-series imaging method for rotating machinery fault diagnosis using unsupervised sparse dictionary learning

Wonho Jung, Dae-Guen Lim, Jaewoong Bae, Yong-Hwa Park

Proceedings Volume 12043, 120431B (2022) https://doi.org/10.1117/12.2612532

KEYWORDS: Feature extraction, Mahalanobis distance, Convolutional neural networks, Diagnostics

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Time-series signal collected from rotating machinery is subjected to different environmental and operational conditions. The vibration signal is sensitively affected by external noises and load conditions. To solve these problems, this paper presents a diagnostic method for rotating machinery using the proposed robust time-series imaging method. The overall procedure includes the following three key steps: (1) transformation of a one-dimensional current signal to a twodimensional image in time-domain, (2) extracting features using convolutional neural networks, and (3) calculating a health indicator using Mahalanobis distance. Transformation of the time-series signal is based on recurrence plots (RP). The original RP method provides a binary image that makes it insensitive to detecting faulty signal. The proposed RP method develops from sparse dictionary learning that provides the dominant fault feature representations in a robust way. The proposed RP method can detect the weak difference between normal and fault signal, while enhancing robustness to external noise. The dataset acquired from KAIST rotor testbed is used to examine the proposed method’s capability to monitor the condition of rotating machinery. The results show that the proposed method outperforms vibration signalbased condition monitoring methods.

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