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Jong-Myon Kim

Bio: Jong-Myon Kim is an academic researcher from University of Ulsan. The author has contributed to research in topics: Fault (power engineering) & Support vector machine. The author has an hindex of 30, co-authored 302 publications receiving 3047 citations. Previous affiliations of Jong-Myon Kim include Information Technology University.


Papers
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Journal ArticleDOI
28 Aug 2019-Energies
TL;DR: An electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture that can classify both the majority class and the minority class with good accuracy.
Abstract: Among an electricity provider’s non-technical losses, electricity theft has the most severe and dangerous effects. Fraudulent electricity consumption decreases the supply quality, increases generation load, causes legitimate consumers to pay excessive electricity bills, and affects the overall economy. The adaptation of smart grids can significantly reduce this loss through data analysis techniques. The smart grid infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning and deep learning techniques can accurately identify electricity theft users. In this paper, an electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture. CNN is a widely used technique that automates feature extraction and the classification process. Since the power consumption signature is time-series data, we were led to build a CNN-based LSTM (CNN-LSTM) model for smart grid data classification. In this work, a novel data pre-processing algorithm was also implemented to compute the missing instances in the dataset, based on the local values relative to the missing data point. Furthermore, in this dataset, the count of electricity theft users was relatively low, which could have made the model inefficient at identifying theft users. This class imbalance scenario was addressed through synthetic data generation. Finally, the results obtained indicate the proposed scheme can classify both the majority class (normal users) and the minority class (electricity theft users) with good accuracy.

201 citations

Journal ArticleDOI
TL;DR: A hybrid technique for bearing prognostics that utilizes regression-based adaptive predictive models to learn the evolving trend in a bearing's health indicator and then uses these models to project forward in time and estimate the RUL of a bearing.
Abstract: Rolling element bearings cause the largest number of failures in induction motors. Predicting an impending failure and estimating the remaining useful life (RUL) of a bearing is essential for scheduling maintenance and avoiding abrupt shutdowns of critical systems. This paper presents a hybrid technique for bearing prognostics that utilizes regression-based adaptive predictive models to learn the evolving trend in a bearing's health indicator. These models are then used to project forward in time and estimate the RUL of a bearing. The proposed algorithm addresses some key issues in existing methods for bearing health prognosis that affect their prognostic performance, specifically determining the time to start prediction (TSP), handling random fluctuations in a bearing's health indicator, and setting a dynamic failure threshold. The proposed algorithm is validated on publicly available bearing prognostics data from the Center for Intelligent Maintenance Systems. Experimental results show that the proposed approach is effective in determining an accurate TSP and failure threshold, as well as handling random fluctuations. Moreover, this approach achieves excellent prognostic performance and estimates the RUL of bearings within the specified error bounds, even at points very close to the TSP, where traditional methods yield relatively poor RUL estimates.

154 citations

Journal ArticleDOI
TL;DR: A reliable technique for the health prognosis of rolling element bearings is proposed, which infers a bearing's health through a dimensionless health indicator (HI) and estimates its RUL using dynamic regression models.

141 citations

Journal ArticleDOI
11 Dec 2017-Sensors
TL;DR: A two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type and demonstrates that it outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).
Abstract: Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

139 citations

Journal ArticleDOI
TL;DR: In this paper, a genetic algorithm (GA)-based kernel discriminative feature analysis is proposed for low-speed bearing defect detection. And the most useful fault features for diagnosis are then selected by utilizing a GA-based Kernel Discriminative Feature Analysis (KDA) cooperating with one-against-all multicategory support vector machines (OAA MCSVMs).
Abstract: This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposed GA-based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

135 citations


Cited by
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Journal ArticleDOI
TL;DR: The applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder and its variants, Restricted Boltzmann Machines, Convolutional Neural Networks, and Recurrent Neural Networks.

1,569 citations

Journal ArticleDOI
01 Oct 1980

1,565 citations

Journal ArticleDOI
TL;DR: This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications.

1,287 citations