• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.1021-1024
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. In this paper, the vision system of the sensing methods of drill flank wear on the basis of image processing is used to detect the wear pattern by non-contact and direct method and get the reliable wear information about drill. In image processing of acquired image, median filter is applied for noise removal. The vision flank wear area of the drill was measured. Backpropagation neural networks (BPns) were used for no-line detection of drill wear. The neural network consisted of three layers: input, hidden and output. The input vectors comprised of spindle rotational speed, feed rates, vision flank wear, thrust and torque signals. The output was the drill wear state which was either usable or failure. Drilling experiments with various spindle rotational speed and feed rates were carried out. The learning process was peformed effectively by utilizing backpropagation. The detection of the abnormal states using BPNs achieved 96.4% reliability even when the spindle rotational speed and feedrate were changed.

• Journal of Welding and Joining
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• v.32 no.5
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• pp.50-57
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• 2014

Friction Stir Welding (FSW) is a complex process with several mutually interdependent parameters. A slight difference from known settings may lead to imperfections in the stirred zone. These inhomogeneities affect on the mechanical properties of the FSWed joints. In order to prevent the failure of the welded joint it is necessary to detect the most critical defects non-destructive. Especially critical defects are wormhole and lack of penetration (LOP), because of the difficulty of detection. Online thermography is used process-accompanying for defect detecting. A thermographic camera with a fixed position relating to the welding tool measures the heating-up and the cool down of the welding process. Lap joints with sound weld seam surfaces are manufactured and monitored. Different methods of evaluation of heat distribution and intensity profiles are introduced. It can be demonstrated, that it is possible to detect wormhole and lack of penetration as well as surface defects by analyzing the welding and the cooling process of friction stir welding by passive online thermography measurement. Effects of these defects on mechanical properties are shown by tensile testing.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.12
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• pp.1227-1236
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• 2012

The low speed machinery faults are usually caused by the bearing failure of the rolling elements. As the life time of the bearing is limited, the condition monitoring of bearing is very important to maintain the continuous operation without failures. A few monitoring techniques using time domain, frequency domain and fuzzy neural network vibration analysis are introduced to detect and diagnose the faults of the low speed machinery. This paper presents a method of fault detection for the rolling element bearing in the low speed machinery using the Wiener filtering and shock pulse counting techniques. Wiener filter is used for noise cancellation and it clearly makes the shock pulse emerge from the time signal with the high level of noise. The shock pulse counting is used to determine the various faults obviously from the shock signal with transient pulses not related with the bearing fault. Machine fault simulator is used for the experimental measurement in order to verify this technique is the powerful tool for the low speed machine compared with the frequency analysis. The test results show that the method proposed is very effective parameter even for the signal with high contaminated noise, speed variation and very low energy. The presented method shows the optimal tool for the condition monitoring purpose to detect the various bearing fault with high accuracy.

• Journal of Advanced Navigation Technology
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• v.24 no.4
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• pp.314-321
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• 2020

Logic automated stimulus and response (LASAR) software is an automatic test program development tool for logic function test and fault detection of avionics components digital circuit cards. LASAR software needs to the information for the logic circuit function and input and output of the device. If there is no component information, normal component modeling is impossible. In this paper, component modeling is carried out through reverse design of programmable logic device (PLD) device without element information. The developed LASAR program identified failure detection rates through fault simulation results and single-seated fault insertion methods. Fault detection rates have risen by 3% to 91% for existing limited modeling and 94% for modeling through the reverse design. Also, the 22 case of stuck fault with the I/O pin of EP310 PLD were detected 100% to confirm the good performance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.8
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• pp.131-137
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• 2005

The purpose of this paper is to evaluate the axial magnetic flux measurement could be used as a tool of the condition monitoring system for the induction motor and to develope the diagnostic algerian for the electric motors. The magnetic leakage flux signal is captured by the flux coil located at the end of motor without the disturbance of the operation. And the signal is analyzed both time and frequency bases to detect the failure of the motor. Specific signature can be described in time and frequency domain for each faults of the motor. The spectrum of the signal was found more useful for the monitoring purpose. The supply voltage imbalance and tin to turn failure of the stator winding could be detected by analysing the specific sidebands of the axial flux and sideband of the rotor bar pass frequency with the high resolution spectrum. The goal of this study verity that the axial flux measurement for the induction motor is a powerful tool for the diagnostic method and develope the algorithm to detect the fault.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.179-188
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• 2011

This paper outlines a systematic guideline for remanufacturing process using the Failure Mode and Effect Analysis (FMEA) method in order to estimate the reliability and quality of the remanufactured alternator. The method is just a tool to help, but the remanufacturer must determine the optimal remanufacturing process and specific inspection and production that will turn the alternator as-good-as new and place the product into the market with reliability and quality equal to a new product. FMEA is a method that is widely used in industry and has shown its value and effectiveness in the above remanufacturing case study. Actions taken often result in a lower severity, occurrence or detection rating. Redesign may result in lower severity and occurrence ratings while inserting validation controls and maintenance can reduce the detection rating. The revised ratings are recorded with the originals on the FMEA template form. After these corrective actions and revisions have been established, evaluation of the ranks can be repeated, until the redesign and control parameters comply with safety standards.

• Tuberculosis and Respiratory Diseases
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• v.77 no.4
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• pp.161-166
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• 2014

Since tuberculosis (TB) remains a major global health concern and the incidence of multi-drug resistant (MDR)-TB is increasing globally, new modalities for the detection of TB and drug resistant TB are needed to improve TB control. The Xpert MTB/RIF test can be a valuable new tool for early detection of TB and rifampicin resistance, with a high sensitivity and specificity. Late-generation fluoroquinolones, levofloxacin, and moxifloxacin, which are the principal drugs for the treatment of MDR-TB, show equally high efficacy and safety. Systemic steroids may reduce the overall TB mortality attributable to all forms of TB across all organ systems, although inhaled corticosteroids can increase the risk of TB development. Although fixed dose combinations were expected to reduce the risk of drug resistance and increase drug compliance, a recent meta-analysis found that they might actually increase the risk of relapse and treatment failure. Regarding treatment duration, patients with cavitation and culture positivity at 2 months of TB treatment may require more than 6 months of standard treatment. New anti-TB drugs, such as linezolid, bedaquiline, and delamanid, could improve the outcomes in drug-resistant TB. Nontuberculous mycobacterial lung disease has typical clinical and immunological phenotypes. Mycobacterial genotyping may predict disease progression, and whole genome sequencing may reveal the transmission of Mycobacterium abscessus. In refractory Mycobacterium avium complex lung disease, a moxifloxacin-containing regimen was expected to improve the treatment outcome.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.2
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• pp.96-103
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• 2005

The early detection of the bearing defects in rotating machinery is very important since the critical failure of bearing causes a machinery shutdown. However it is not easy to detect the vibration signal caused by the initial defects of bearing because of the high level of random noise. A signal processing technique, called the adaptive line enhancer(ALE) as one of adaptive filter, is used in this study. This technique is to eliminate random noise with little a prior knowledge of the noise and signal characteristics. Also we propose the optimal methods fir selecting the three main ALE parameters such as correlation length filter order and adaptation constant. Vibration signals f3r three abnormal bearings, including inner and outer raceways and ball defects, were acquired by Anderon(angular derivative of radius on) meter. The experimental results showed that ALE is very useful f3r detecting the bearing defective signals masked by random noise.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.3
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• pp.246-254
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• 2007

The scanning laser source (SLS) technique is a promising new laser ultrasonic tool for the detection of small surface-breaking defects. The SLS approach is based on monitoring the changes in laser-generated ultrasound as a laser source is scanned over a defect. Changes in amplitude and frequency content are observed for ultrasound generated by the laser over uniform and defective areas. The SLS technique uses a point or a short line-focused high-power laser beam which is swept across the test specimen surface and passes over surface-breaking or subsurface flaws. The ultrasonic signal that arrives at the Rayleigh wave speed is monitored as the SLS is scanned. It is found that the amplitude and frequency of the measured ultrasonic signal have specific variations when the laser source approaches, passes over and moves behind the defect. In this paper, the setup for SLS experiments with full B-scan capability is described and SLS signatures from small surface-breaking and subsurface flaws are discussed using a point or short line focused laser source.

• Steel and Composite Structures
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• v.14 no.4
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• pp.367-377
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• 2013

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.