Anomaly detection for each industrial machine is recognized as one of the essential techniques for machine condition monitoring and preventive maintenance. Anomaly detection of industrial machinery relies on various diagonal data from equipped sensors, such as temperature, pressure, electric current, vibration, and sound, to name a few. Among these data, sound data are easy to collect in the factory due to the relatively low installation cost of microphones to existing facilities. We develop a real time anomalous sound detection (ASD) system with the use of Autoencoder (AE) models in the industrial environments. The proposed processing pipeline makes use of the audio features extracted from the streaming audio signal captured by a single-channel microphone. The pipeline trains AE model by the collected normal sound. In real factory applications, the reconstruction error generated by the trained AE model with new input sound streaming is calculated to measure the degree of abnormality of the sound event. The sound is identified as anomalous if the reconstruction error exceeds the preset threshold. In our experiment on the CNC milling machining, the proposed system shows 0.9877 area under curve (AUC) score.

When developing a new motor, a high-speed load test is performed using dynamo equipment to calculate the efficiency of the developed motor using the collected dynamo data. When connecting the test motor and the dynamo used as a load, abnormal noise and vibration may occur in the test equipment rotating at high speed due to misalignment of the connecting shaft and looseness of the connection, which may lead to a safety accident. In this study, three vibration sensors are attached to the surface of bearing parts of the test motor to measure the vibration value, and statistics such as kurtosis, skewness, and percentiles are obtained in order to clearly express the pattern of the measurement data. With these statistics, machine learning models are developed. The developed model in this way can be used as a diagnostic system that can detect abnormal conditions of the motor test equipment through monitoring the motor vibration data during the motor test.

한국 천일염 생산 지역의 인구는 빠르게 고령화되고 있어 생산 노동자가 줄고 있는 추세이다. 소금 포집 작업은 천일염 생산 과정에서 가장 많은 노동력을 필요로 한다. 기존의 포집 장치는 사람의 작동 및 운전이 필요하여 상당한 노동력이 필요해서, 천일염 무인 포집장치를 개발하여 생산 노동자의 노동력을 감소시키고자 한다. 천일염 포집장치는 색상 검출을 통해 소금의 포집 상황과 염전에서의 위치를 파악하도록 설계되었기 때문에, 포집장치의 색상 검출 성능이 중요한 요소이다. 그래서 색상 검출 성능 향상을 위해 이미지 처리 를 이용한 알고리즘을 연구하였다. 알고리즘은 입력 이미지를 크기 재조정, 회전 및 투시 변환을 이용하여 around-view 이미지를 생성하고, RoI를 설정하여 해당 영역만 HSV 색상 모델로 변환하고 논리곱 연산을 통해 색상 영역을 검출한다. 검출 된 색상영역은 형태학적 연산을 이용하여 검출 영역을 확장하고 노이즈를 제거하여 컨투어와 이미지 모멘트를 이용하여 검출영역의 면적을 계산하고 설정된 면적과 비 교하여 염판에서 포집장치의 위치 경우를 결정한다. 성능 평가는 알고리즘을 적용한 최종 검출 색상의 계산 면적과 알고리즘의 각 단계 의 검출 색상의 면적을 비교하여 평가하였다. 평가 결과 소금을 검출하는 흰색의 경우 최소 25%에서 최대 99% 이상, 빨간색의 경우 최소 44%에서 최대 68%, 파란색과 녹색은 평균적으로 각각 7%와 15% 검출면적 증가가 있어 색상 검출 성능이 향상되었음을 확인할 수 있었으 며, 이를 무인 천일염 포집장치의 무인작업 수행을 위한 위치 확인에 적용 가능할 것으로 사료된다.

시간영역반사계(TDR)는 케이블의 물리적 결함을 검사하는 기법이며 누수 탐지 분야로의 응용영역을 확대하고 있다. 본 연구는 시간영역반사계 기법을 활용하여 선박 기관실 해수 배관의 누설 감지용 케이블형 센서를 개발하였다. 케이블 센서의 형상은 꼬임형상과 흡습부재를 이용하여 제작하였으며 개발된 센서의 누수 감지 여부와 위치 탐지 가능성을 확인하였다. 개발된 센서는 실제 배관 시험 장치 에 부착하여 평가하였으며 해수 누설에 따른 다양한 TDR 신호를 취득하였다. 센서는 꼬임횟수, 피복 두께를 변수로 하여 제작하였으며 TDR 신호에 미치는 효과를 분석하였다. 실험 결과, 꼬임형 센서는 평행한 띠 형상의 센서에 비해 평활한 신호 취득이 가능하였으며 최적 꼬임 횟수는 단위길이 당 10회 이상인 것으로 나타났다. 절연 피복두께의 경우 적정 민감도 확보가 가능한 절연 피복부재의 두께는 도선 직경의 80%~120%로 확인되었다. 누수 위치 추정을 위해 회귀분석 실시 결과, 결정계수는 0.9998로 실제 누설 위치와 높은 상관관계를 나타 내었다. 결과적으로 제안된 TDR 기반의 누수 감지용 꼬임형 센서는 해수 배관 시스템의 누수 감시 센서로의 충분한 적용성을 확인하였다.

강풍, 폭우 등 이상기후의 대형화와 빈도 증가로 인해 나무가 부러지거나 쓰러지는 훼손이 증가하고 있으나 나무 내부의 공동, 부후 등 구조적 결함은 육안조사로 판별이 어렵기 때문에 예측을 통한 사전대응에 한계가 있다. 비파괴 음파단층촬영은 나무에 미치는 물리적 훼손을 최소화하면서 내부결함을 추정하는 방법으로 내부결함 진단에 효율적이 나 수종별 정확도에 차이가 발생하기 때문에 현장적용 전 측정결과의 신뢰성 분석이 선행되어야 한다. 이번 연구는 우리나라 대표 수종인 소나무와 은행나무 노거수를 대상으로 음파단층촬영의 신뢰성 검증을 위해 침입성 드릴저항 측정을 교차 적용하여 목재 내부결함을 측정하고 평가결과를 비교하였다. 두 집단 간 결함부 측정 평균값에 대한 t검정 결과 소나무는 통계적으로 유의한 차이가 없는 반면, 은행나무는 유의성에 차이가 있었다. 선형회귀분석 결과 두 수종 모두 드릴저항그래프의 결함이 증가할 때 음파단층영상 결함이 증가하는 양의 상관관계를 보였다.

As the decommissioning and decontamination (D&D) of nuclear power plants (NPPs) has actively proceeded worldwide, the management of radiation exposure of workers has become more critical. Radioactive aerosol is one of the main causes of worker exposure, contributing to internal exposure by inhalation. It occurs in the process of cutting radioactive metal structures or melting radioactive wastes during D&D, and its distribution varies according to decommissioning strategies and cutting methods. Among the dominant radionuclides in radioactive aerosols, Fe-55 is known to be the most abundant. Fe-55, which decays by electron capture, is classified as a difficult-to-measure (DTM) radionuclide because its emitted X-rays have too low energy to measure directly from outside of the container. Generally, for measuring DTM nuclides, the liquid scintillation counting (LSC) method and the scaling factor (SF) method are used. However, these methods are not suitable for continuous monitoring of the D&D workplace due to the necessity of sampling and additional analysis. The radiation measurement system that can directly measure the radionuclides collected at the aerosol filter could be more useful. In this study, as preliminary research on developing the radioactive aerosol monitoring system, we fabricated a gamma-ray spectrometer based on a NaI (Tl) scintillator and measured the energy spectrum of Fe-55. A beryllium window was applied to the scintillator for X-ray transmission, and the Fe-55 check source was directly attached to the scintillator assuming that the aerosol filter was equipped. 5.9 keV photopeak was clearly observed and the energy resolution was estimated as 44.10%. Also, the simultaneous measurement with Cs-137 was carried out and all the peaks were measured.

Organic scintillator is easy to manufacture a large size and the fluorescence decay time is short. However, it is not suitable for gamma measurement because it is composed of a low atomic number material. Organic scintillation detectors are widely used to check the presence or absence of radiation. The fluorescence of organic scintillators is produced by transitions between the energy levels of single molecules. In this study, an organic scintillator development study was conducted for use in gamma measurement, alternative materials for secondary solute used in basic organic scintillators were investigated, and the availability of alternative materials, detection characteristics, and neutron/gamma identification tests were performed. In other words, a secondary solute showing an improved energy transfer rate than the existing material was reported, and the performance was evaluated. 7-Diethylamino -4-methylcoumarin (DMC), selected as an alternative material, is a benzopyrone derivative in the form of colorless crystals, has high fluorescence and high quantum yield in the visible region, and has excellent light stability. In addition, it has a large Stokes shift characteristic, and solubility in solvent is good. Through this study, it was analyzed that the absorption wavelength range of DMC coincided with the emission wavelength range of PPO, which is the primary solute. Through this study, it was confirmed that the optimal concentration of DMC was 0.04wt%. As a result of performing gamma and neutron measurement tests using a DMC-based liquid scintillator, it showed good performance (FOM=1.42) compared to a commercial liquid scintillator. Therefore, the possibility of use as a secondary solute was demonstrated. Based on this, if studies on changes in the composition of secondary solute or the use of nanoparticles are conducted, it will be possible to manufacture and utilize a scintillator with improved efficiency compared to the existing scintillator.

The safe, efficient and cost-effective decommissioning and dismantling of radioactive facilities requires the accurate characterization of the radionuclide activities and dose rate environment. And it is critical across many nuclear industries to identify and locate sources of radiation accurately and quickly. One of the more challenging aspects of dealing with radiation is that you cannot see it directly, which can result in potential exposure when working in those environments. Generally, semiconductor detectors have better energy resolution than scintillation detectors, but the maximum achievable count rates are limited by long pulse signals. Whereas some high pure germanium detectors have been developed to operate at high count rates, and these HPGe detectors could obtain gamma-ray spectra at high count rates exceeding 1 Mcps. However, HPGe detectors require cooling devices to reduce the leak currents, which becomes disadvantageous when developing portable radiation detectors. Furthermore, chemicalcompound semiconductor detectors made of cadmium telluride and cadmium zinc telluride are popular, because they have good energy resolution and are available at room temperature. However, CdTe and CZT detectors develop irradiation-induced defects under intense gamma-ray fields. In this Review, we start with the fundamentals of gamma rays detection and review the recent developments in scintillators gamma-ray detectors. The key factors affecting the detector performance are summarized. We also give an outlook on the field, with emphasis on the challenges to be overcome.

The detector response was simulated to design a fork detection system for verifying the characteristics of spent fuel. The fork detection system currently used consists of two fission chamber and an ion chamber, and it is nuclear safeguard equipment that measures the gross neutrons and gross gamma rays emitted from the spent fuel assembly to identify the characteristics of the spent fuel and verify the authenticity of the operation history. In order to improve the current fork detection system, we are developing a system that applies CZT, a room temperature semiconductor detector, and a stilbene detector, which is an organic scintillator. Depletion calculations were performed using the ORIGEN code to determine the radiological characteristics emitted from spent nuclear fuel assembly. The flux of radiation emitted from the spent nuclear fuel assembly was calculated by changing the conditions such as initial enrichment, burnup, and cooling time, which are major variables of spent fuel assembly. The calculated result is used as the source term of the particle transport code. Considering the general operating conditions of the pressurized light water reactor, the conditions were changed in the range of 3-5% for initial enrichment and 30-72 GWD/MTU for burnup, and the cooling time was given within 10 years. MCNP 6.2, a Monte Carlo simulation code, was used to simulate the detector response to radiation emitted from spent nuclear fuel assembly. According to the shape, size, and position of the CZT detector, the gamma counts incident on the detector were calculated and derived the initial design of our fork detection system.

Recently, extreme terrorist attacks have frequently occurred around the world and are threatening the international community. It is no longer a safe zone for terrorism in our country. Therefore, domestic nuclear facilities as the highest level of national security facilities have established a physical protection system to protect facilities and lives against terrorist attacks. In addition, security search and access control are conducted for controlled items and unauthorized person. However, with the development of science and technology, disguised weapons or homemade explosives used in terrorism are becoming very sophisticated. Therefore, nuclear facilities need to strengthen security search of weapons or homemade explosives. Since these disguised weapons or homemade explosives are difficult to find only through security search, it is also necessary to actively identify unspecified people who possess disguised weapons or do abnormal behavior. For this reason, the “Abnormal Behavior Detection Method”, which is very effective in preemptive response to potential terrorist risks, has been introduced and operated in aviation security field. Korea Institute of Nuclear Nonproliferation and Control (KINAC) has established a “Practice Environment for Identifying Disguised Weapons” in 2020 for trainees to recognize the dangers of controlled items and to use for physical protection education. This Practice environment has not only the basic explanation of the controlled items of nuclear facilities, but also various actual disguised weapons were displayed. It also introduces actual terrorist incidents using homemade explosives such as attempted bombing of a cargo plane bound for Chicago and the Boston Marathon bombing. And then a model of the disguised explosives actually used is displayed and used for education. In addition, in 2022, the “Abnormal behavior detection method” education module was developed and used for physical protection education. In this module, the outline and introduction of the “Abnormal Behavior Detection Method” and “Behavior Detection Officer (BDOs)” are explained. In this way, the access control and security search system of nuclear facilities require the overall monitoring system, not only for dangerous goods but also for identification of persons possess and carrying them. This study describes the development of the Curriculum for “Disguised Weapon Identification” and “Abnormal Behavior Detection Method” to enhance the effectiveness of physical protection education.

Fescues, which are widely cultivated as grasses and forages around the world, are often naturally infected with the endophyte, Epichloë. This fungus, transmitted through seeds, imparts resistance to drying and herbivorous insects in its host without causing any external damage, thereby contributing to the adaptation of the host to the environment and maintaining a symbiosis. However, some endophytes, such as E. coenophialum synthesize ergovaline or lolitrem B, which accumulate in the plant and impart anti-mammalian properties. For example, when livestock consume excessive amounts of grass containing toxic endophytes, problems associated with neuromuscular abnormalities, such as convulsions, paralysis, high fever, decreased milk production, reproductive disorders, and even death, can occur. Therefore, pre-inoculation with non-toxic endogenous fungi or management with endophyte-free grass is important in preventing damage to livestock and producing high-quality forage. To date, the diagnosis of endophytes has been mainly performed by observation under a microscope following staining, or by performing an immune blot assay using a monoclonal antibody. Recently, the polymerase chain reaction (PCR)-based molecular diagnostic method is gaining importance in the fields of agriculture, livestock, and healthcare given the method’s advantages. These include faster results, with greater accuracy and sensitivity than those obtained using conventional diagnostic methods. For the diagnosis of endophytes, the nested PCR method is the only available option developed; however, it is limited by the fact that the level of toxic alkaloid synthesis cannot be estimated. Therefore, in this study, we aimed to develop a triplex real-time PCR diagnostic method that can determine the presence or absence of endophyte infection using DNA extracted from seeds within 1 h, while simultaneously detecting easD and LtmC genes, which are related to toxic alkaloid synthesis. This new method was then also applied to real field samples.