• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.371-374
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• 2004

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motor applications. Preventive maintenance of induction motors has been a topic great interest to industry because of their wide range application of industry. Since the use of mechanical sensors, such as vibration probes, strain gauges, and accelerometers is often impractical, the motor current signature analysis (MACA) techniques have gained murk popularity as diagnostic tool. Fault tolerant control (FTC) strives to make the system stable and retain acceptable performance under the system faults. All present FTC method can be classified into two groups. The first group is based on fault detection and diagnostics (FDD). The second group is independent of FDD and includes methods such as integrity control, reliable stabilization and simultaneous stabilization. This paper presents the fundamental FDD-based FTC methods, which are capable of on-line detection and diagnose of the induction motors. Therefore, our group has developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. This paper presents its architecture. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module is checking stator current, voltage, temperatures, vibration and speed of the motor. The DSPs share information from each sensor or DSP through DPRAM with hardware implemented semaphore. And it communicates the motor status through field bus (CAN, RS485). From the designed system, we get primitive sensors data for the case of normal condition and two abnormal conditions of 3 phase induction motor control system is implemented. This paper is the first step to drive multi-motors with serial communication which can satisfy the real time operation using CAN protocol.

• Tuberculosis and Respiratory Diseases
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• v.71 no.1
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• pp.30-36
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• 2011

Background: Pneumonia is commonly seen in outpatient clinics. it is widely known as the most common cause of death from infectious disease. Pneumonia has been diagnosed by its typical symptoms, chest X-ray and blood tests. However, both chest X-rays and blood tests have limitations in diagnosis. Thus primary care clinicians usually have been constrained due to a lack of adequate diagnostic tools. Vibration response imaging (VRI) is a newly emerging diagnostic modality, and its procedure is non-invasive, radiation-free, and easy to handle. This study was designed to evaluate the diagnostic usefulness of the VRI test among pneumonia patients and to consider its correlation with other conventional tests such as Chest X-ray, laboratory tests and clinical symptoms. Methods: VRI was performed in 46 patients diagnosed with pneumonia in Konkuk University Medical Center. VRI was assessed in a private and quiet room twice: before and after the treatment. Sensors for VRI were placed on a patient's back at regular intervals; they detected pulmonary vibration energy produced when respiration occurred and presented as specific images. Any modifications either in chest X-ray, C-reactive protein (CRP), white blood cell count (WBC) or body temperature were compared with changes in VRI image during a given time course. Results: VRI, chest X-ray and CRP scores were significantly improved after treatment. Correlation between VRI and other tests was not clearly indicated among all patients. But relatively severe pneumonia patients showed correlations between VRI and chest X-ray, as well as between VRI and CRP. Conclusion: This study demonstrates that VRI can be safely applied to patients with pneumonia.

• Journal of KSNVE
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• v.6 no.3
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• pp.341-347
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• 1996

Vibration analysis is one of the most powerful tools available for the detection and isolation of incipient faults in mechanical systems. The methods of vibration analysis in use today and under continuous study are broad band vibration monitoring, time domain analysis, and frequency domain analysis. In recent years, great interest has been generated concerning the use of time- frequency repesentation and its application for a machinery diagnostics and condition monitoring system. The objective of the study described in this paper was to develop a new diagnostic tool for the rotating machinery. This paper introduces a new time frequency representation. Directional Winger-Ville Distribution, which analyese the time-frequency structure of the rotating machinery vibration.

• Proceedings of the KOR-BRONCHOESO Conference
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• 1993.05a
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• pp.72-72
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• 1993

Among the various diagnostic methods for the voice disorders, video laryngo-stroboscopy is one of the most practical techniques for clinical examination of the vocal fold vibration. It provides valuable informations about the nature of vocal folds' vibration, the extent of pathologic change and data recording for analysis. To obtain the stroboscopic characteristics of several voice disorders, and apply those informations to the diagnosis and management of disorders, we reviewed the stroboscopic findings obtained from the patients with voice disorders at Voice laboratory, the Institute of Logopedics and Phoniatrics form April 1992 to March 1993.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.827-838
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• 2023

Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and non-stationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%.

• Steel and Composite Structures
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• v.29 no.6
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.7
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• pp.556-560
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• 2008

As higher reliability and accuracy on production facilities are required to detect incipient faults, a diagnostic system for predictive maintenance of the facility is highly recommended. In this paper, we present a study on the application of vibration signals to diagnose faults for a Rotating Machinery using the Mahalanobis Distance-Taguchi System. RMS, Crest Factor and Kurtosis that is known as the Statistical Methods and the spectrum analysis are used to diagnose faults as parameters of Mahalanobis distance.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.663-668
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• 2024

This paper applies statistical analysis methods in the time domain to the fault diagnosis of wafer transfer robots, and proposes a methodology to discern the critical characteristics of vibration and torque signals. Subsequently, principal component analysis (PCA) is applied to diminish the data's dimensionality, followed by the development of a fault diagnosis algorithm utilizing Euclidean distance and Hotelling's T-square statistics. The algorithm establishes decision boundaries to categorize failure states based on the observed data. Our findings indicate that data classification incorporating velocity parameters enhances diagnostic accuracy. This approach serves to enhance the precision and efficacy of fault diagnosis.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.2
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• pp.205-210
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• 2014

Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.

• Annals of Clinical Neurophysiology
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• v.12 no.1
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• pp.21-26
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• 2010

Background: Although quantitative sensory test (QST) is being used with increasing frequency for measuring sensory thresholds in clinical practice and epidemiologic studies, there has been no age-matched normative data in Korean adults. The objective of this study is to evaluate the value of QST in diabetic polyneuropathy with normal range in Korean adults. Methods: The Computer Aided Sensory Examination IV 4,2 (WR Medical Electronics Co., Stillwater, Minnesota, U.S.A.), with 4,2,1 stepping algorithm was used to determine vibration and cold perception threshold in 70 normal controls and 19 patients with diabetic polyneuropathy aged from 21 to 79 years. The data were used to define age-matched upper and lower normal limits and normal range of side to side difference. We also evaluated the duration of diabetes, serum HbA1C level, and findings of nerve conduction study (NCS) and QST in patients with diabetic polyneuropathy. Results: In normal adults, sensory thresholds slightly increased with age, and a slight side-to-side difference was observed. The diagnostic sensitivity of QST was not higher than NCS in patients with diabetic polyneuropathy (36.8% vs. 42.1%, p=0.716), especially among elderly patients. Conclusions: QST might be used as a complementary test for NCS in the diagnosis of diabetic polyneuropathy. Although the QST is a simple method for the evaluation of peripheral nerve function, there are some limitations. Most of all, because the QST measuring is dependent on the subjective response of patients, the degree of concentration and cooperation of the patients can significantly affect the result. And thus, attention should be paid during the interpretation of QST results in patients with peripheral neuropathy.