Jongmyon Kim

Jongmyon Kim
University of Ulsan | UOU · School of IT convergence

PhD

About

471
Publications
157,469
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4,534
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Introduction
Skills and Expertise
Additional affiliations
March 2007 - July 2017
University of Ulsan
Position
  • Professor

Publications

Publications (471)
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SI: The Role of Data Science, and Computer Vision in Public Health The use of massive amounts of data has become critical in the public health sector. We have seen several cases throughout this COVID-19 outbreak. We have witnessed significant advancements in cancer detection, COVID x-ray processing, brain signal analysis, and stress analysis using...
Article
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Bearings cause the most breakdowns in induction motors, which can result in significant economic losses. If faults in the bearings are not detected in time, they can cause the whole system to fail. System failures can lead to unexpected breakdowns, threats to worker safety, and huge economic losses. In this investigation, a new approach is proposed...
Article
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Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and minimizing expenditures. In this study, an intelligent fault classification model that combines a signal-to-image encoding technique and a convolution neural network (CNN) with the motor-current signal is proposed to classify bearing faults. In the b...
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About this Research Topic In recent years, energy industries have been facing increasing pressure on the immediate hike in energy demands and environmental protection. The research on “Smart Grid” and “Renewable Energy” has been mainstreamed to cope with this grand challenge. Smart grid technology is enabling an effective distribution and managemen...
Article
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Using an adaptive noise canceling technique (ANCT) and distance ratio principal component analysis (DRPCA), this paper proposes a new fault diagnostic model for multi-degree tooth-cut failures (MTCF) in a gearbox operating at inconsistent speeds. To account for background and disturbance noise in the vibration characteristics of gear failures, the...
Article
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Acoustic emission techniques are widely used to monitor industrial pipelines. Intelligent methods using acoustic emission signals can analyze acoustic waves and provide important information for leak detection and localization. To address safety and protect the operation of industrial pipelines, a novel hybrid approach based on acoustic emission si...
Article
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This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback–Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete structures. Through the construction of a HI, the deteri...
Article
A rub-impact fault is recognized as a complex, non-stationary, and non-linear type of mechanical fault that frequently occurs in turbines. Extracting features for diagnosing rubbing faults at their early stages requires complex and computationally expensive signal processing approaches that are not always suitable for industrial applications. Furth...
Chapter
Bearings are used to reduce inertia in numerous utilizations. Lately, anomaly detection and identification in the bearing using acoustic emission signals has received attention. In this work, the combination of the machine learning and adaptive-backstepping digital twin approach is recommended for bearing anomaly size identification. The proposed a...
Chapter
Condition monitoring can avoid sudden breakdown and ensure the reliable and safe operation of rotating machinery used in the industry. The early detection of fault signatures and accurately classifying them in time will ensure efficient maintenance operation and reduce the possibility of losses due to uncertain breakdown. In the fault diagnosis mec...
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This paper proposes a reliable technique for pipeline leak detection using acoustic emission signals. The acoustic emission signal of a pipeline contains leak-related information. However, the noise in the signal often obscures the leak-related information, making traditional acoustic emission features, such as count and peaks, less effective. To o...
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Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and moto...
Article
Bearing fault diagnosis is essential in manufacturing systems to avoid problems such as downtime costs. Convolutional neural network (CNN) models have enabled a new generation of intelligent bearing fault diagnosis methods for smart manufacturing owing to their capability to extract features for 2-dimensional (2D) representations, such as signals r...
Cover Page
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Dear Colleagues, Machinery and mechanical structures in the industry suffer from inevitable degradation and performance degradation during operation. By collecting and processing data using a variety of sensors, timely diagnosis of symptoms of deterioration and reliable estimation of future health conditions are essential for industrial productivi...
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This study proposes a fault diagnosis method (FD) for multistage centrifugal pumps (MCP) using informative ratio principal component analysis (Ir-PCA). To overcome the interference and background noise in the vibration signatures (VS) of the centrifugal pump, the fault diagnosis method selects the fault-specific frequency band (FSFB) in the first s...
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Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-domain, frequency-domain, or time-frequency domain statistical parameters. Vibration signals of bearin...
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Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features to attain...
Conference Paper
Pipelines are used to transport liquids and gases between different places. In recent years, identifying the location and size of cracks in transmission pipes has received much attention. Thus, in this research, the combination of fuzzy digital twin (FDT), support vector machine (SVM), and backstepping (BS) observer is suggested for leak detec...
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Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and entropy, have been suggested to detect leaks. However, background noise in AE signals makes these featu...
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Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in...
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In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure's failure before its occurrence, thus possibly prolong its service life and minimizing the risk of accidental damage. The deterioration process is portrayed by the health indicator (HI), which is a...
Article
A rub-impact fault is a complex, nonstationary, and nonlinear fault that occurs in turbines. Extracting features for diagnosing rubbing faults at their early stages requires complex and computationally expensive signal processing approaches that are not always suitable for industrial applications. In this article, a hybrid approach that uses a comb...
Article
Full-text available
In this paper, we propose a three-stage lightweight framework for centrifugal pump fault diagnosis. First, the centrifugal pump vibration signatures are fast transformed using a Walsh transform, and Walsh spectra are obtained. To overcome the hefty noise produced by macro-structural vibration, the proposed method selects the fault characteristic co...
Article
This paper presents a data-driven prognostic framework for rolling-element bearings (REBs). This framework infers a bearing’s health index by defining a degree-of-defectiveness (DD) metric in the frequency domain of bearing raw signal, named DD-based health index (DDHI). Then, we systematically apply least-square support vector machines (LSSVMs) in...
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Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and lo...
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This paper proposes a Gaussian mixture model-based (GMM) bearing fault band selection (GMM-WBBS) method for signal processing. The proposed method benefits reliable feature extraction using fault frequency oriented Gaussian mixture model (GMM) window series. Selecting exclusively bearing fault frequency harmonics, it eliminates the interference of...
Conference Paper
Bearing failure is considered as one of the major problems in induction motor, which can result a huge mechanical damage if is not monitored from the initial stage. A complete fault classification method is presented in this paper by combining wavelet-based signal processing technique and deep learning method for fault classification. Vibration sig...
Article
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Bearings prevent damage caused by frictional forces between parts supporting the rotation and they keep rotating shafts in their correct position. However, the continuity of work under harsh conditions leads to inevitable bearing failure. Thus, methods for bearing fault diagnosis (FD) that can predict and categorize fault type, as well as the level...
Chapter
Induction motors are consumed around 80% of energy in heavy industries, that approximately 20% of this energy consumption is because of mechanical failures. Moreover, the bearing failure with about 69% is the principal constituent of mechanical defects. In this study, the self-tuning intelligence digital twin is presented for bearing pattern recogn...
Chapter
In this research, advanced technology is used to monitoring chaotic time-series signals. The combination of autoregressive with adaptive network-fuzzy algorithms is suggested for chaotic signal prediction. The autoregressive prediction algorithm is recommended for chaotic time-series prediction. This technique is linear, and the modeling prediction...
Chapter
In this research, the combination of the smart digital twin (SDT) and the machine learning technique is prescribed to have a reliable fault pattern recognition in this effort. In the first stage, the SDT for the bearing is designed by the dynamical system modeling, updated using the data-driven autoregression approach, and estimate the performance...
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This paper proposes an accurate and stable gearbox fault diagnosis scheme that combines a localized adaptive denoising technique with a wavelet-based vibration imaging approach and a deep convolution neural network model. Vibration signatures of a gearbox contain important fault-related information. However, this useful fault-related information is...
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In this research, the aim is to investigate an adaptive digital twin algorithm for fault diagnosis and crack size identification in bearings. The main contribution of this research is to design an adaptive digital twin (ADT). The design of the ADT technique is based on two principles: normal signal modeling and estimation of signals. A combination...
Article
Pipelines are a nonlinear and complex component to transfer fluid or gas from one place to another. From economic and environmental points of view, the safety of transmission lines is incredibly important. Furthermore, condition monitoring and effective data analysis are important to leak detection and localization in pipelines. Thus, an effective...
Article
Pipelines are a nonlinear and complex component to transfer fluid or gas from one place to another. From economic and environmental points of view, the safety of transmission lines is incredibly important. Furthermore, condition monitoring and effective data analysis are important to leak detection and localization in pipelines. Thus, an effective...
Article
Full-text available
Bearing is one of the most vital components of industrial machinery. The failure of bearing causes severe problems in the machinery. Therefore, continuous monitoring for the bearings is essential rather than regular manual checking, with the requirement for accuracy of prediction and efficiency. This paper proposes a novel intelligent bearing fault...
Article
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In this paper, an explainable AI-based fault diagnosis model for bearings is proposed with five stages, i.e., (1) a data preprocessing method based on the Stockwell Transformation Coefficient (STC) is proposed to analyze the vibration signals for variable speed and load conditions, (2) a statistical feature extraction method is introduced to captur...
Article
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Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonline...
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In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated...
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Centrifugal pumps are important types of electro-mechanical machines used for fluid and energy conveyance. Mechanical faults in centrifugal pumps lead to abnormal impacts in the vibration signal of the system. Those impacts induce nonstationarity in vibration signals and hence complex time-frequency domain signal analysis techniques are required to...
Article
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Centrifugal pumps are the most vital part of any process industry. A fault in centrifugal pump can affect imperative industrial processes. To ensure reliable operation of the centrifugal pump, this paper proposes a novel automated health state diagnosis framework for centrifugal pump that combines a signal to time-frequency imaging technique and an...
Article
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Bearings are complex components with onlinear behavior that are used to mitigate the effects of inertia. These components are used in various systems, including motors. Data analysis and condition monitoring of the systems are important methods for bearing fault diagnosis. Therefore , a deep learning-based adaptive neural-fuzzy structure technique...
Article
This paper introduces a novel approach to crack detection and localization in a pipeline transporting fluid under high pressure. From acoustic emission signals acquired by two R15i-AST sensors at two ends of a fluid pipeline, the proposed method scans peaks in the individual signal channels in the time-frequency domain and filters out noise to obta...
Chapter
This paper proposes a reliable fault identification model of multi-level gearbox defects by applying adaptive noise control and a genetic algorithm-based feature selection for extracting the most related fault components of the gear vibration characteristic. The adaptive noise control analyzes the gearbox vibration signals to remove multiple noise...
Chapter
Bearing failure generates burst signals when the rolling elements touch a crack on the outer race or inner race of the bearing. Over a decade, the acoustic emission (AE) has been used for detecting low-speed bearing failures. Since high sampling rate of the AE signal makes it difficult to design and extract good features, deep neural network-based...
Article
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This paper introduces a technique using a k-nearest neighbor (k-NN) classifier and hybrid features extracted from acoustic emission (AE) signals for detecting leakages in a gas pipeline. The whole algorithm is embedded in a microcontroller unit (MCU) to detect leaks in real-time. The embedded system receives signals continuously from a sensor mount...
Chapter
. Rolling element bearing (REB) represent a class of nonlinear and multiple-degrees-of-freedom rotating machines that have pronounced coupling effects and can be used in various industries. The challenge of understanding complexity in a bearing’s dynamic behavior, coupling effects, and sources of uncertainty presents substantial challenges regardin...
Chapter
Inner, outer, and ball faults are complex non-stationary and non-linear faults that occurs in rotating machinery such as bearings. Designing an effective procedure for fault diagnosis (FD) is essential to safe operation of bearings. To address fault diagnosis issue, a robust, hybrid technique based on the ARX-Laguerre fuzzy-sliding proportional int...
Article
Full-text available
Bearings are complex components with nonlinear behavior that are used to reduce the effect of inertia. They are used in applications such as induction motors and rotating components. Condition monitoring and effective data analysis are important aspects of fault detection and classification in bearings. Thus, an effective and robust hybrid techniqu...
Chapter
Inner, outer, and ball faults are complex non-stationary and non-linear faults that occurs in rotating machinery such as bearings. Designing an effective procedure for fault diagnosis (FD) is essential to safe operation of bearings. To address fault diagnosis issue, a robust, hybrid technique based on the ARX-Laguerre fuzzy-sliding proportional int...
Chapter
Feature analysis plays an important role in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for the health state classification of spherical tanks in this paper. The proposed HBoF is com...
Chapter
Condition monitoring of induction motors plays a significant role in avoiding unexpected breakdowns and reducing excessive maintenance costs. In the majority of cases, bearing faults are found to be an issue in the failure of induction motors. The detection and valuation of irregularities at an early stage can help prevent disastrous failures. In t...
Article
Bearings are complex components with nonlinear behavior that are used to reduce the effect of inertia. They are used in applications such as induction motors and rotating components. Condition monitoring and effective data analysis are important aspects of fault detection and classification in bearings. Thus, an effective and robust hybrid techniqu...
Chapter
This paper establishes a methodology to exploit the characteristics of burst waveform in acoustic emission (AE) signals and combine with the signal analysis process to enhance the accuracy of multi-level leak detection in steel pipelines. The AE bursts are signal waveforms that contain the continual imbrication transients with variable strengths in...
Article
Full-text available
Gearbox fault diagnosis based on the analysis of vibration signals has been a major research topic for a few decades due to the advantages of vibration characteristics. Such characteristics are used for early fault detection to guarantee the enhanced safety of complex systems and their cost-effective operation. There exist many fault diagnosis mode...
Article
Full-text available
Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequency, or time-frequency domain data. These engineered features are susceptible to variations under i...
Article
Full-text available
This paper proposes a three-stage fault diagnosis strategy for multistage centrifugal pumps. First, the proposed method identifies and selects fault characteristic modes of vibration to overcome the substantial noise produced by other unrelated macro-structural vibrations. In the second stage, raw hybrid statistical features are extracted from the...
Article
Full-text available
A new method is established to construct the 2-D fault diagnosis representation of multiple bearing defects from 1-D acoustic emission signals. This technique starts by applying envelope analysis to extract the envelope signal. A novel strategy is propounded for the deployment of the continuous wavelet transform with damage frequency band informati...
Article
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Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time–frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has a...
Article
Robotic manipulators represent a class of nonlinear and multiple-degrees-of-freedom robots that have pronounced coupling effects and can be used in various applications. The challenge of understanding complexity in a system’s dynamic behavior, coupling effects, and sources of uncertainty presents substantial challenges regarding fault estimation, d...
Article
This study aims at characterizing crack types for reinforced concrete beams through the use of acoustic emission burst (AEB) features. The study includes developing a solid crack assessment indicator (CAI) accompanied by a crack detection method using the k-nearest neighbor (k-NN) algorithm that can successfully distinguish among the normal conditi...
Article
Full-text available
A blade rub-impact fault is one of the complex and frequently appearing faults in turbines. Due to their nonlinear and nonstationary nature, complex signal analysis techniques, which are expensive in terms of computation time, are required to extract valuable fault information from the vibration signals collected from rotor systems. In this work, a...