Degradation of proton exchange membrane fuel cells (PEMFCs) can be accelerated by impurities in the air. In maritime environments in particular, sodium chloride (NaCl) can reduce the performance of membrane electrode assembly (MEA) in PEMFCs. In this context, we experimentally analyzed effect of flow channel depth on PEMFCs humidified with a NaCl solution at the cathode side. The analysis was conducted in serpentine flow channels with different depths of 0.4, 0.8, and 1.6 mm. The initial performance of unit cells was compared to their performance after applying a constant current for 10 hours. Results showed that the degradation rate correlated positively with the flow-channel depth. Channel depths of 0.4 and 1.6 mm resulted in 2.4% and 7.3% decreases in the maximum power density, respectively. For the 1.6 mm channel depth, the activation loss after 10 hours was larger than the initial loss.
In continuous-process systems, failures of rolling-element bearings typically cause accidents, reduced productivity, and production-related financial losses. Therefore, predicting both the lifespan of rolling-element bearings and their replacement time is crucial for preventing machine system failures. Accordingly, numerous studies have reported various machine and deep learning classifiers for predicting the lifespan of bearings. However, these studies did not consider degradation trends of bearings. Thus, this study aimed to develop an algorithm to predict the lifespan of a bearing by considering its degradation trend. A vibration dataset of bearings was obtained at low and high speeds. Using a second-order curve-fitting model, various degradation patterns in the dataset were classified. Appropriate time-domain or frequency-domain feature variables applicable to the design of a classifier were determined according to classified patterns. In addition, the classifier was trained using multiple bidirectional long short-term memories. Finally, the performance of the developed classifier was verified experimentally.
In mechanical braking systems, there are hot spots on the surface of a braking disc due to thermal deformation with a high thermal gradient. Controlling such hot spots is important for extending the life of a braking disc. In this study, surface temperatures of railway brake discs were monitored using infrared (IR) thermal imaging technique. A highspeed infrared camera with a maximum speed of 380 Hz was used to monitor surface temperature changes of the braking disc. Braking tests were performed with a full-scale dynamometer. During the braking test, the surface temperature change of the braking disc were monitored using a high-speed infrared camera. Hot spots and thermal damage observed on the surface of railway brake discs during braking tests were quantitatively analyzed using infrared thermographic images. Results revealed that monitoring disc surface temperature using IR thermographic technique can be a new method for predicting surface temperature changes without installing a thermocouple inside the disc.
Elderly monitoring systems are gaining significant attention in our increasingly aging society. Existing monitoring systems, which utilize RGB and infrared cameras, often encounter errors when recognizing human-like objects, photos, and videos as actual humans. Additionally, privacy concerns arise due to this issue. However, these challenges can potentially be overcome by employing thermal images. Thus, our study aimed to investigate the feasibility of identifying and categorizing human postures depicted in thermal images using deep learning models and algorithms. To conduct our experiment, we developed a system that utilizes a thermal pose algorithm and a convolutional neural network. As a result, we achieved an average accuracy of 88.3%, with the highest accuracy reaching 91.2%.
Damage to the units related to driving and running of the railway vehicle may cause an inevitable accident due to defects and malfunctions in operation. In order to prevent such an accident, a non-destructive diagnostic technology that detects the damage is required. Previous researchers have researched and developed a monitoring system of the infrared thermography method to diagnose the condition of the railway vehicle driving and driving units. A system for monitoring running of the railway vehicle and temperature condition of the drive unit at a vehicle speed of 30 to 100 km/h was constructed, and a study on its applicability was conducted. In this study, a system for diagnosing an abnormal condition of the driving and running units while the vehicle is running with an infrared thermography diagnostic system was installed in the depot and operation route, and evaluation of the abnormal condition of the driving and running units was performed. The results show that the diagnosis system using infrared thermography can be used to identify abnormal conditions in the driving and running units of a railway vehicle. The diagnosis system can effectively inspect the normal and abnormal conditions in operation of a railway vehicle.
There have been frequent fatal accidents of firefighters at fire scenes. A firefighting robot can be an alternative to humans at a fire scene to reduce accidents. As a critical function of the firefighting robot, it is mandatory to autonomously detect a fire spot and shoot water. In this research, a deep learning model called YOLOv7 was employed based on thermal images to recognize the shape and temperature information of the fire. Based on the results of the test images, which were not used for learning purposes, a recognition rate of 99% was obtained. To track the recognized fire spot, a 2-DOF pan-tilt actuation system with cameras was developed. By using the developed system, a moving target can be tracked with an error of 5%, and a variable target tracking test by alternately covering two target braziers showed that it takes about 1.5 seconds to track changing targets. Through extinguishment experiments with a water spray mounted on the pan-tilt system, it was observed that the temperature of the brazier dropped from 600 degrees to 13 degrees. Based on the obtained data, the feasibility of a robotic firefighting system using image recognition was confirmed.
Durability evaluations were conducted using polymer electrolyte membrane fuel cells in a marine environment. Deionised water and 3.5 wt% of NaCl solution were supplied to the cathode using an ultrasonic vibrator. Performance and electrochemical impedance spectroscopy of fuel cells were measured to evaluate the electrochemical behaviors. Additionally, long-term stability evaluations of PEMFCs were carried out at 0.65 V for 20 h. Following the experiments, scanning electron microscope analysis was conducted to confirm the presence of NaCl on membrane electrode assembly and micro porous layer of fuel cells.
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Effects of NaCl Solution on Proton Exchange Membrane Fuel Cell with Serpentine Flow Channel of Different Depths Dong Kun Song, Ho Jun Yoo, Jung Soo Kim, Ki Won Hong, Do Young Jung, George Ilhwan Park, Gu Young Cho Journal of the Korean Society for Precision Engineering.2025; 42(5): 399. CrossRef
Evaluation of Electrochemical Performance of PEMFCs with Decontamination Devices at Marine Environments Ye rim Kwon, Ho Jun Yoo, Byung Gyu Kang, Ki Won Hong, Sun Ki Kwon, Sanghoon Lee, Gu Young Cho Journal of the Korean Society for Precision Engineering.2025; 42(1): 57. CrossRef
A Study of Effects of the Repetition of Assembly and the Addition of Activation on Electrochemical Characteristics of PEMFCs Ji Woong Jeon, Gye Eun Jang, Young Jo Lee, Dong Kun Song, Ho Jun Yoo, Seung Hyeok Hong, Jung Soo Kim, Ye Rim Kwon, Da Hye Geum, Gu Young Cho Journal of the Korean Society for Precision Engineering.2023; 40(11): 867. CrossRef
A Study on Electrochemical Resistance Change through the Pressurization Process of MEA for PEMFC Ye Rim Kwon, Dong Kun Song, Ho Jun Yoo, Gye Eun Jang, Young Jo Lee, Jung Soo Kim, Ji Woong Jeon, Da hae Guem, Gu Young Cho Journal of the Korean Society for Precision Engineering.2023; 40(7): 539. CrossRef
A small wind power generator with Archimedes blades made of polypropylene has been developed for the effective generation of eco-friendly electronic energy. Despite the excellent structural characteristics of the higher performance of an Archimedes blade, its shape is complicated to manufacture, and presents difficulty in guaranteeing mechanical reliability in the outdoor operating environment. Especially, the UV-Light deterioration in a long-term of several years affects the mechanical properties of the polypropylene blade. To evaluate the change of strength depending on the amount of UV-Light irradiation in the outdoor environment, an accelerated UV-Light deterioration test is proposed and conducted using three types of blade materials, such as polypropylene with UV-Resistance material (C20 H25 N₃O) coated and mixed ones. Through the experimental tests, the UV resistant material coating on the blade showed the best properties for long-term exposure to UV light. Based on the test results of property changes, the Archimedes blade was analyzed using a finite element method to predict the reliability of the blade’s underused conditions. As a result of the analysis, the UV degradation resistance of Archimedes blades with UV coating improved by 2.4 times compared to the case without UV coating.
Lithium-ion batteries are one of the main parts of electrical devices and are widely used in various applications. To safely use lithium-ion batteries, fault diagnosis and prognosis are significant. This paper analyzes resistance parameters from electrochemical impedance spectroscopy (EIS) to detect the fault of lithium-ion batteries. The internal fault mechanisms of batteries are so complex; it is difficult to detect abnormalities by direct current-based methods. However, by using alternating-current-based impedance by EIS, the internal degradation processes of the batteries can be detected. Impedance variation from EIS is verified under accelerated degradation test conditions and normal cycling test conditions. The results showed a significant relationship between fault and increase in resistance.
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Research into the Detection of Faulty Cells in Battery Systems Using BMS Cell Balancing Counts Hyunjun Kim, Woongchul Choi Transaction of the Korean Society of Automotive Engineers.2025; 33(8): 637. CrossRef
PEDOT:PSS‐Based Prolonged Long‐Term Decay Synaptic OECT with Proton‐Permeable Material, Nafion Ye Ji Lee, Yong Hyun Kim, Eun Kwang Lee Macromolecular Rapid Communications.2024;[Epub] CrossRef
Lithium-Ion Batteries (LIBs) Immersed in Fire Prevention Material for Fire Safety and Heat Management Junho Bae, Yunseok Choi, Youngsik Kim Energies.2024; 17(10): 2418. CrossRef
The repeated thermal load on the railway wheel for tread brakes has been remarkably tightened due to increase in speed of trains and increase of operation frequency. As overheating and cooling between the wheel and brake block are continuously repeated, the railway wheel is damaged. To understand the process, thermal cracks for wheel tread can be experimentally reproduced under the condition of cyclic frictional heat from brake blocks, through bench experiments using a railway wheel. Thermal cracks generated in the wheel were investigated to observe the cracks’ initiation processes using full-scale brake dynamometer. Results show that as braking energy and braking temperature continued to accumulate, a hot spot appeared on the wheel surface and 2 mm of thermal crack occurred in the wheel rim.
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