• Journal of the KSME
• /
• v.57 no.3
• /
• pp.34-37
• /
• 2017

이 글은 공학시스템 고장예지 및 건전성 관리 기술을 소개하고, 최근 주목받고 있는 인공지능기술인 딥러닝과 접목한 연구 사례를 제시한다.

• Journal of the KSME
• /
• v.53 no.7
• /
• pp.26-34
• /
• 2013

이 글에서는 최근 관심을 모으고 있는 고장예지 및 건전성관리(PHM: Prognostics and Health Management) 기술을 소개하고, 항공우주분야의 적용사례를 중심으로 PHM 기술을 어떻게 활용하고 있는지를 설명하고자 한다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.4
• /
• pp.939-949
• /
• 2017

Prognostics and health management(PHM) is actively utilized by industry as an essential technology focusing on accurately monitoring the health state of a system and predicting the remaining useful life(RUL). An effective PHM is expected to reduce maintenance costs as well as improve safety of system by preventing failure in advance. With these advantages, PHM can be applied to the battery system which is a core element to provide electricity for devices with mobility, since battery faults could lead to operational downtime, performance degradation, and even catastrophic loss of human life by unexpected explosion due to non-linear characteristics of battery. In this paper we mainly review a recent progress on various models for predicting RUL of battery with high accuracy satisfying the given confidence interval level. Moreover, performance evaluation metrics for battery prognostics are presented in detail to show the strength of these metrics compared to the traditional ones used in the existing forecasting applications.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.39 no.6
• /
• pp.625-629
• /
• 2015

In this study, we develop a prognostic method of assessing the stator windings of power generators against water absorption through statistical data analysis and degradation modeling. The 42 windings of the generator are divided into two groups: the absorption and normal groups. A degradation model of a winding is constructed using Fick's second law to predict the level of absorption. By analyzing data from the normal group, we can determine the distribution of the data of normal windings. The health index of a winding is estimated using the directional Mahalanobis distance (DMD) method. Finally, the probability distributions of the failure time of the windings are determined.

• Journal of KSNVE
• /
• v.25 no.1
• /
• pp.22-28
• /
• 2015

• Journal of the KSME
• /
• v.53 no.7
• /
• pp.40-43
• /
• 2013

이 글에서는 최근 관심을 모으고 있는 고장예지 및 건전성관리(PHM: Prognostics and Health Management) 기술이 자동차 전장부품에 어떻게 적용돼야 하는지에 대한 설명을 하고자 한다.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.4
• /
• pp.751-756
• /
• 2018

In smart factories and industrial IoT, all facilities in a factory are monitored over the Internet, thereby facility can reduce the downtime and increase the availiability by preventive maintenance before it breaks down. The abnormal conditions of the major facilities in the plant are caused by abnormal temperature rise, vibration, and variations in noise. Consequently, it is critical to develop a very small smart device that is easily installed in a small space to enable real-time monitoring of the vibration status of the facility. In this study, smart devices were developed for smart factory fault prediction and robustness management using ultra small micro-controllers with WiFi capabilities and MEMS acceleration sensors.

• Journal of the KSME
• /
• v.53 no.7
• /
• pp.35-39
• /
• 2013

이 글에서는 자동차산업에서 고장예지 및 건전성평가 기술을 적용하여 개발된 시스템 및 관련 특허 기술에 관한 내용 및 향후 연구 방향에 관해 서술한다.

• Journal of Aerospace System Engineering
• /
• v.13 no.6
• /
• pp.17-25
• /
• 2019

PHM (Prognostics and Health Management) of aircraft engines is applied to predict the remaining useful life before failure or the lifetime limit. There are two methods to establish a predictive model for this: The physics-based method and the data-driven method. The physics-based method is more accurate and requires less data, but its application is limited because there are few models available. In this study, the data-driven method is applied, in which a multi-layer perceptron based neural network algorithms is applied for the life prediction. The neural network is trained using the data sets virtually made by the C-MAPSS code developed by NASA. After training the model, it is applied to the test data sets, in which the confidence interval of the remaining useful life is predicted and validated by the actual value. The performance of proposed method is compared with previous studies, and the favorable accuracy is found.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.40 no.8
• /
• pp.721-724
• /
• 2016

Because of the inherent uncertainties caused by the manufacturing process variations, future loading conditions, and incomplete damage models, the lifetimes of mechanical structures under field conditions are significantly different from the results obtained in the laboratories. In this study, a dual sensor was developed to prognosticate the fatigue failure of structures under these uncertain conditions, and its effectiveness was demonstrated on a rectangular columnar structure under repeated uni-axial loading. The dual sensor is a slightly weaker structure embedded in the target structure, so that failure occurs in the sensor earlier than in the target structure. From the signal differences in the strain gauges in the embedded dual sensor, it is possible to differentiate between the normal status and warning status, even under variable loads.