• Proceedings of the Korean Nuclear Society Conference
• /
• 2005.10a
• /
• pp.534-535
• /
• 2005

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2012.10a
• /
• pp.635-636
• /
• 2012

In the present work, investigations by high frequency resonance technique for diagnosis of defect frequencies of linear motion unit are reported. Raw vibration signature of the moving parts at different speeds of operation has been demodulated. Envelope detected spectrum is analyzed to evaluate various defect frequencies and their energy levels. Experimentally evaluated frequencies are compared with theoretically determined defect frequencies. These frequency values and their energy levels are used to monitor intrinsic condition of linear motion unit as well as to establish severity of existing/developed defects on the LM guide and inside the LM block. Relative comparisons of linear motion units of the same type are made at various operating speeds under identical conditions of operation on the basis of identified defect frequencies and severity of defects.

• Journal of the Korean Solar Energy Society
• /
• v.31 no.3
• /
• pp.17-22
• /
• 2011

Techniques for mismatch loss minimization to increase the PV system efficiency are under development recently. In this paper, a method to make diagnosis of PV module mismatch is presented, which uses a concept of operating point factor. The method is based on the fact that the ratio of the incremental conductance of a PV module to instantaneous conductance is 1 when the module is operating at its maximum power point. The variations of module voltage and current are taking place by the maximum power point tracker in the power conditioning units of PV system. The effectiveness of the method is verified through an application to a real PV system.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.969-970
• /
• 2007

In this thesis, an algorithm of fault detection and diagnosis during operation for induction motors under the condition of various loads and rates is investigated. For this purpose, the spectrum pattern of input cutterrents was used to monitor the state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrums pattern caused by faults were detected. For diagnosis of the fault detected, the fuzzy fault tree was designed, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, was solved. The solution of the fuzzy relation equation shows the possibility of each fault's occurring.

• Applied Chemistry for Engineering
• /
• v.30 no.3
• /
• pp.290-296
• /
• 2019

Lithium-ion batteries are one of the most interesting devices in a number of energy storage systems. In particular, the usage of energy storage devices is increasing due to an increase in demand for renewable energy as a distributed power supply source, stable supply of electric power, and expansion of electric vehicles. Of late, the recycling and restoration technology of waste lithium ion batteries due to the increase in its usage amount as the energy storage system is a socially and economically important research field. In this review, we intend to describe the performance diagnosis, recycling or restoration technology of lithium ion battery and its potential development.

• Proceedings of the Korean Nuclear Society Conference
• /
• 2005.05a
• /
• pp.1255-1256
• /
• 2005

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.16 no.9
• /
• pp.872-880
• /
• 2004

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this study, an air handling unit fault test apparatus was built and fault diagnosis algorithms were applied to diagnose various faults of an air handling unit. Test results showed the good diagnosis for applied faults. Therefore, these algorithms may be effectively used to develope the real time fault detection and diagnosis system for the air handling unit.

• Journal of Information Processing Systems
• /
• v.18 no.6
• /
• pp.822-829
• /
• 2022

According to existing study into the remote fault diagnosis procedure, the current diagnostic approach has an imperfect decision model, which only supports communication in a close distance. An Internet of Things (IoT)-based remote fault diagnostic approach for wind power equipment is created to address this issue and expand the communication distance of fault diagnosis. Specifically, a decision model for active power coordination is built with the mechanical energy storage of power generation equipment with a remote diagnosis mode set by decision tree algorithms. These models help calculate the failure frequency of bearings in power generation equipment, summarize the characteristics of failure types and detect the operation status of wind power equipment through IoT. In addition, they can also generate the point inspection data and evaluate the equipment status. The findings demonstrate that the average communication distances of the designed remote diagnosis method and the other two remote diagnosis methods are 587.46 m, 435.61 m, and 454.32 m, respectively, indicating its application value.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.6
• /
• pp.285-292
• /
• 2023

With the wide spread of smart farms and the advancements in IoT technology, it is easy to obtain additional data in addition to crop images. Consequently, deep learning-based crop disease diagnosis research utilizing multimodal data has become important. This study proposes a crop disease diagnosis method using multimodal supervised contrastive learning by expanding upon the multimodal self-supervised learning. RandAugment method was used to augment crop image and time series of environment data. These augmented data passed through encoder and projection head for each modality, yielding low-dimensional features. Subsequently, the proposed multimodal supervised contrastive loss helped features from the same class get closer while pushing apart those from different classes. Following this, the pretrained model was fine-tuned for crop disease diagnosis. The visualization of t-SNE result and comparative assessments of crop disease diagnosis performance substantiate that the proposed method has superior performance than multimodal self-supervised learning.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2007.11a
• /
• pp.415-416
• /
• 2007