• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.61-68
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• 2001

A rotor system is composed of a rotating shaft with supporting bearings. The rotor system is widely used in every rotating machinery such as the turbine generator and the high precision machine tools. A negligible error or malfunction in the rotor, however, can cause a catastrophic failure in the system then result in the environmental and economic disasters. A diagnosis of the rotor system is important in preventing these kinds of failures and disasters. Up to now, many researchers have devoted in the development of diagnosing tools for the system. The basic principles behind the tools are to retrieve the data through the sensors for a specific state of the system and then to identify the specific state through the heuristic methods such as neural network, fuzzy logic, and decision matrix. The proper usage of the heuristic methods will enhance the performance of the diagnostic procedure when together used with the statistical signal processing. In this paper, the methodologies in using the above 3 heuristic methods for the diagnostics of the rotor system are established and also tested and validated for the data retrieved from the rolling element bearing and journal bearing supported system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.6
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• pp.556-561
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• 2009

The UAV(Unmanned Aerial Vehicle) that is remotely operating in various and long flight environments must have a very reliable propulsion system. Precise fault diagnosis of the turbo shaft engine for the Smart UAV that has the vertical take-off, landing and forward flight behaviors can promote reliability and availability. This work proposes a new diagnostic method that can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. The proposed diagnostic method can detect not only single fault but also multiple faults.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.522-526
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• 1994

There are great needs for checking machine operation status precisely in the iron and steel plants. Rotating machines such as pumps, compressors, and motors are the most important objects in the plant maintenance. In this paper back-propagation neural network is utilized in diagnosing rotating machines. Like the finger print or the voice print of human, the abnormal vibrations due to axis misalignment, shaft bending, rotor unbalance, bolt loosening, and faults in gear and bearing have their own spectra. Like the pattern recognition technique, characteristic. feature vectors are obtained from the power spectra of vibration signals. Then we apply the characteristic feature vectors to a back propagation neural net for the weight training and pattern recognition.

• Tribology and Lubricants
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• v.17 no.2
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• pp.153-161
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• 2001

The development of techniques in diagnosing the state of the system is one of the essential tools in establishing the automation and unmanned manufacturing system for the realization of CIM/FMS in the fields. In this paper, we developed various diagnostic schemes for the journal bearing supported rotor system. Up to now, vibration of the shaft, measurement of the displacement and the temperature have been used for diagnostic tools, however, the statistical features only could not differentiate the state from states. Thus, we identified the sensor data f3r the steady state in the signal processing and then applied the fuzzy c-mean technology to cope with the nonlinear characteristics of the system. This will, in return, establish a possible diagnostic system for the rotor system in the fields.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.9
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• pp.841-848
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• 2010

Previous experimental results show that the magnetostrictive transducer has the peculiar characteristic with relation to their reversible magnetization and its practical usage will be hindered by this phenomena. In this paper, the idea of anhysteretization is adopted in order to solve this problem. The experimental results reveal that the anhysteretization can get rid of the extraordinary phenomena which are occurred by the change of biasing magnetic field. The effects of two important parameters, which are the amplitude and the decaying time of this process, on the anhysteretization are investigated experimentally. Finally the best operating condition is proposed in order to maximize the sensitivity under the anhysteretization.

• Journal of KSNVE
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• v.5 no.4
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• pp.543-553
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• 1995

Modern steam turbine generators are being built as a higher power and larger system, experiencing more frequent starts and stops of operation due to a constant change of power demands. Hence, they are inevitably more vulnerable to various vibrations, and more often exposed to the danger of sudden vibration accidents than ever before. Even under the circumstances, in order to secure the system reliability of steampower plants and there by to supply safely the public electricity, it is important to prevent a sudden vibration accident in one hand and even when it happens, to raise an operating efficiency of the plants throught swift and precise treatments in the other. In this study, an interactive vibration diagnostic system has been developed to make the on-site vibration diagnosis of steam turbine generators possible and convenient, utilizing a note-book PC. For this purpose, at first the principal vibration phenomena, such as various unbalance and unstable vibrations as well as rubbing, misalignment, and shaft crack vibrations, have been systematically classified as grouped parameters of vibration frequencies, amplitudes, phases, rotating speeds at the time of accident, and operating conditions or condition changes. A new complex vibration diagnostic table has been constructed from the causal relations between the characteristic parameters and the principal vibration phenomena. Then, the diagnostic system has been developed to screen and issue the corresponding vibration phenomena by assigning to each user-selected combination of characteristic parameters a unique characteristic vector and comparing this vector with a diagnostic vector of each vibration phenomenon based on the constructed diagnostic table. Moreover, the diagnostic system has a logic whose diagnosis may be performed successfully by inputing only some of the corresponding characteristic parameters without having to input all the parameters. The developed diagnostic system has been applied to perform the diagnosis of several real cases of steam turbine vibration accidents. And the results have been quite satisfactory.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.6
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• pp.36-45
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• 2005

This paper addresses the application of motor current spectral analysis for the detection of rolling-element bearing damage in induction machines. We set the experimental test bed. They is composed of the normal condition bearing system, the abnormal rolling-element bearing system of 2 type induction motors with shaft deflection system by external force and a hole drilled through the outer race of the shaft end bearing of the four pole test motor. We have developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module is checking stator current The effects on the stator current spectrum are described and related frequencies are also determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. We utilized the FFT(Fast Fourier Transform), Wavelet analysis and averaging signal pattern by inner product tool to analyze stator current components. Especially, the analyzed results by inner product clearly illustrate that the stator signature analysis can be used to identify the presence of a bearing fault.

• Journal of Korean Foot and Ankle Society
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• v.2 no.2
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• pp.57-63
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• 1998

Stress or march fractures among military personnel, especially recruits, has been appreciated for many years. According to the classical references, the second metatarsal was one of the first sites identified as a focus for march fractures and radiological evidence of fracture appeared as late as several weeks. The purpose of this study was to document the clinical feature of march fractures in Korean infantry soldiers. From 1997 to 1998, at one infantry medical company of OO infantry corps in Korea, 15 (19cases) patients with march fracture were detected among infantry soldiers. There were some different finding in the fracture site and its clinical features from the previous foreign reports. 1. There were pain and local swelling in all cases as clinical manifestation. By physical examination, direct point tenderness on the location of the fractured metatarsal shaft was characteristic. 2. On roentgenographic examination, cortical fissuring or break was seen one week after onset of symptoms and external callus was seen from two weeks or at the least four weeks. Oblique view was more useful than AP view in the diagnosis of march fractures. 3. The third metatarsal was the most frequently involved site(7 cases, 48%). and the second metatarsal was Jess frequent(3 cases, 20%). This difference of frequent site with previous reports might be attributed to the relatively long shaft of the third metatarsal, but should be analyzed in further study. 4. The incidence of the development of march fracture was 1 per 104 infantry soldiers.

• Smart Structures and Systems
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• v.20 no.6
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• pp.683-696
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• 2017

In this study, the reliability of nonlinear ultrasonic modulation based fatigue crack detection is improved using a feature-level data fusion approach. When two ultrasonic inputs at two distinct frequencies are applied to a specimen with a fatigue crack, modulation components at the summation and difference of these two input frequencies appear. First, the spectral amplitudes of the modulation components and their spectral correlations are defined as individual features. Then, a 2D feature space is constructed by combining these two features, and the presence of a fatigue crack is identified in the feature space. The effectiveness of the proposed fatigue crack detection technique is experimentally validated through cyclic loading tests of aluminum plates, full-scale steel girders and a rotating shaft component. Subsequently, the improved reliability of the proposed technique is quantitatively investigated using receiver operating characteristic analysis. The uniqueness of this study lies in (1) improvement of nonlinear ultrasonic modulation based fatigue crack detection reliability using feature-level data fusion, (2) reference-free fatigue crack diagnosis without using the baseline data obtained from the intact condition of the structure, (3) application to full-scale steel girders and shaft component, and (4) quantitative investigation of the improved reliability using receiver operating characteristic analysis.

• Clinical and Experimental Pediatrics
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• v.46 no.4
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• pp.389-392
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• 2003

Netherton's syndrome is an unusual disorder which consists of triad of ichtyosiform dermatosis, multiple defects of hair shaft and an atopic diathesis. The finding of bamboo hair is pathognomic in Netherton's syndrome and the ichthyosiform dermatosis may consist of either ichtyosis linearis circumflexa or congenital ichthyosiform erythroderma. Often, variability in the clinical features leads to a delay in diagnosis in many cases. We report a case of Netherton's syndrome diagnosed in the neonatal period. The patient presented with severe ichthyosis and confirmed microscopically distinctive bamboo hair.