• 기계와재료
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• v.18 no.2 s.68
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• pp.112-126
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• 2006

• Journal of the KSME
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• v.49 no.2
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• pp.32-35
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• 2009

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.28-34
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• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.35-41
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• 2021

A rotor is a crucial component in various mechanical assemblies. Additionally, high-speed and high-efficiency components are required in the automotive industry, manufacturing industry, and turbine systems. In particular, the failure of high-speed rotating bearings has catastrophic effects on auxiliary systems. Therefore, bearing reliability and fault diagnosis are essential for bearing maintenance. In this work, we performed failure mode and effect analysis on bearing rotors and determined that corrosion is the most critical failure type. Furthermore, we conducted experiments to extract vibration characteristic data and preprocess the vibration data through principle component analysis. Finally, we applied a machine learning algorithm called support vector machine to diagnose the failure and observed a classification performance of 98%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.205-209
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• 2007

생산 현장에서 기기의 운영과 관리는 제품의 품질 및 기업의 수익성과 직결된다. 그러나 정상적인 작동을 하고 있는 시스템에서 고장의 시점과 고장의 종류를 예측하기 곤란하며 따라서 잔여 가동 시간이 얼마인지도 예측하기 힘들다. 본 논문에서는 산업용 기계, 공정과 의료기기 등 신뢰성이 요구되는 Brushless DC 모터의 상태 변화의 추이를 관찰하여 진단의 특징점으로 사용한다. 본 논문에서 제안한 상태천이 모텔은 고장의 시점과 고장의 종류를 예측할 수 있으며 유지보수의사결정에 도움을 줄 수 있다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Journal of Animal Environmental Science
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• v.16 no.1
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• pp.13-20
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• 2010

Manure compost is a main product from animal wastes in Korea. Solid manure is usually treated by aerobic composting at manure composting facilities for land reinforcement. Agricultural use of manure compost as organic fertilizer resources, mainly manure compost, is now recommended in Korea. This study investigated the evaluation of durability about the manure composting machinery and structures which was controlled by aeration and periodic agitating. The questionnaire addressed three main topics as follows: operating practices, machinery and maintenance of the manure composting facilities are being operated. A total of the 22 manure composting facilities in an agricultural cooperative were surveyed. The results obtained in this survey were summarized as follow: The major causes of manure composting apparatus trouble were corrosion and wear, overloading and foreign matter etc. The highly trouble frequency of the agitator, packer and conveyor were chain, agitating blade and shaft, motor and screw vane, respectively. These analytical results can be used as basic information to establish the maintenance control methods and durability standard of manure composting facility.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.85-91
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• 2021

In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.6
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• pp.731-738
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• 2013

This paper describes a fault-tolerant driving control strategy for an independent steer-by-wire system in sixwheel-drive/six-wheel-steering vehicles. An algorithm has been designed to realize vehicle maneuverability that is as close as possible to that of non-faulty vehicles by inducing high slip ratio of the wheel through a faulty steer-by-wire system in order to reduce the lateral tire force, which is resistant to the yaw motion. Considering the transition of the longitudinal tire force of a wheel with a faulty steer-by-wire component, the longitudinal tire forces are optimally distributed to the other wheels. Fault-tolerant driving performance has been investigated via computer simulations. Simulation studies show that the proposed algorithm can significantly improve the maneuverability of a vehicle with a faulty steer-by-wire system as compared to the optimal traction distribution method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.7
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• pp.799-804
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• 2011

A thermal cycling test was conducted on a chip resistor solder joint with real-time failure monitoring. In order to study the failure mechanism of the chip resistor solder joint during the test, the resistance between both ends of the resistor was monitored until the occurrence of failure. It was observed that the monitored resistance first fluctuated linearly according to the temperature change. The initial variation in the resistance occurred at the time during the cycle when there was a decrease in temperature. A more significant change in the resistance followed after a certain number of cycles, during the time when there was an increase in the temperature. In order to explain the failure patterns of the solder joint, a mechanism for the solder failure was suggested, and its validity was proved through FE simulations. Based on the explained failure mechanism, it was shown that prognostics for the solder failure can be implemented by monitoring the resistance change in a thermal cycle condition.