• 문화기술의 융합
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• 제9권3호
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• pp.845-850
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• 2023

본 논문에서는 전이학습을 이용하여 볼베어링의 진동진단을 수행하는 방법을 제안한다. 고장을 진단하기 위해 진동신호를 시간-주파수로 분석할 수 있는 STFT을 CNN의 입력으로 이용하였다. CNN 기반의 딥러닝 인공신경망을 빠르게 학습하고 진단 성능을 높이기 위해 전이학습 기반의 딥러닝 학습 기법을 제안하였다. 전이학습은 VGG 기반의 영상 분류 모델을 이용하여 특징 추출기와 분류기를 선택적으로 학습하였고, 학습에 사용한 데이터 세트는 Case Western Reserve University 대학에서 제공하는 공개된 볼베어링 진동 데이터를 사용하였으며, 성능평가는 기존의 CNN 모델과 비교하는 방법으로 수행하였다. 실험 결과 전이학습이 볼베어링 진동 데이터에서 상태 진단에 유용하다는 것을 증명할 수 있을 뿐만 아니라 이를 통해 다른 산업에서도 전이학습을 사용하여 상태 진단을 개선할 수 있다.

• 지식경영연구
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• 제9권1호
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• pp.61-76
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• 2008

The purpose of this study is to examine the effects of lifelong learners' learned helplessness and failure tolerance on academic achievement and job performance. The result of this empirical study exhibits that learned helplessness has negative effects on their academic achievement and job performance. Failure tolerance, however, has positive effects on their academic achievement and job performance. In addition, their academic achievement has mediated between learned helplessness and/or failure tolerance, and job performance. This study suggests that lifelong learners' positive response to failures is helpful for both learning and working performances.

• 한국초등과학교육학회지:초등과학교육
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• 제33권3호
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• pp.597-606
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• 2014

The purpose of this study is to survey and analyze difficulties in teaching and learning elementary school science on the chapter titled 'electric circuit'. 28 elementary school teachers who teach 5th grade science and 73 5th grade students in elementary school were taken part in this survey. The pilot questionnaire was distributed to find out both the degree and the reason of difficulties in teaching and learning. The answers are analyzed with four areas to extract elements which make class difficult; Learner factors (L), Instruction factors (I), Curriculum & textbooks factors (C), and Environment factors (E). The results are as follows. (1) It can be seen that both students and teachers feel the highest difficulty in 7th lesson 'the direction of current', while they felt little difficulty in lesson 3 'conductor and nonconductor' and lesson 8 'the safety of electricity'. (2) The most mentioned reason of difficulties in teaching and learning was Learner factors (L). (3) Teachers felt many difficulties in experimental environment. On the other hands, students didn't think experimental failures as serious trouble. (4) Students felt many difficulties in new terms and hazy concepts or expressions. (5) Teachers felt a lot of difficulties in those from Curriculum & textbooks factors.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.188-192
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• 2021

In this paper, we apply data mining techniques and machine learning algorithms using R software, which is used to predict, here we applied a regression model to test some factor on the dataset for which we assumed that it effects student performance. Model was built on an existing dataset which contains many factors and the final grades. The factors tested are the attention to higher education, absences, study time, parent's education level, parent's jobs, and the number of failures in the past. The result shows that only study time and absences can affect the students' performance. Prediction of student academic performance helps instructors develop a good understanding of how well or how poorly the students in their classes will perform, so instructors can take proactive measures to improve student learning. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a student's data.

• 시스템엔지니어링학술지
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• 제16권2호
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• pp.16-26
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• 2020

Improving operator performance in complex and time-critical situations is critical to maintain plant safety and operability. These situations require quick detection, diagnosis, and mitigation actions to recover from the root cause of failure. One of the key challenges for operators in nuclear power plants is information management and following the control procedures and instructions. Nowadays Digital Twin technology can be used for analyzing and fast detection of failures and transient situations with the recommender system to provide the operator or maintenance engineer with recommended action to be carried out. Systems engineering approach (SE) is used in developing a digital twin for the CEDM system to support operator actions when there is a misalignment in the control element assembly group. Systems engineering is introduced for identifying the requirements, operational concept, and associated verification and validation steps required in the development process. The system developed by using a machine learning algorithm with a text mining technique to extract the required actions from limiting conditions for operations (LCO) or procedures that represent certain tasks.

• 한국산업융합학회 논문집
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• 제25권4_2호
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• pp.573-586
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• 2022

Corrosion can cause dangerous and expensive damage and failures of ship hulls and equipment. Therefore, it is necessary to maintain the vessel by periodic corrosion inspections. During visual inspection, many corrosion locations are inaccessible for many reasons, especially safety's point of view. Including subjective decisions of inspectors is one of the issues of visual inspection. Automation of visual inspection is tried by many pieces of research. In this study, we propose image preprocessing methods by image patch segmentation and thresholding. YOLOv5 was used as an object detection model after the image preprocessing. Finally, it was evaluated that corrosion detection performance using the proposed method was improved in terms of mean average precision.

• 대한인간공학회지
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• 제34권5호
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• pp.455-467
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• 2015

Objective: The present study suggested practical measures for successful patent registration based on a review of success and failure cases of patent application filed based on inventions obtained from physical ergonomics research. Background: The protection of intellectual property (IP) contributes to economic growth and competitiveness and facilitates innovation and creativity. IP rights are pursued on research findings for effective technology transfer and commercialization; however, a patent application can be rejected if patentability requirements such as patent eligible subject matter, utility for industrial application, novelty, or non-obviousness are not satisfied. Method: Three successful and three failed cases of patent applications based on physical ergonomics research were reviewed, critical reasons for their successes and failures were examined, and measures were proposed to avoid failures in patent registration. Results: The following measures were identified based on the patent application case review. First, abstract ideas including logical procedures and/or mathematical formulas need to include use of tangible apparatus and methods in idea realization. Second, the provision of grace period inventor disclosure exception needs to be properly followed in case an invention is disclosed before filing of patent application. Lastly, a comprehensive analysis of prior art published or publicly known anywhere in the world and a claim preparation of distinguished, non-trivial features compared to prior art solutions are needed to avoid possible violation of novelty and non-obviousness. Application: The proposed measures can help to prepare a patent application with patent eligibility.

• Journal of applied mathematics & informatics
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• 제41권6호
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• 산업경영시스템학회지
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• 제40권1호
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• 산업경영시스템학회지
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• 제43권1호
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• pp.123-131
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• 2020

There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.