• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.428-434
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• 2020

The one-shot weapon system is destroyed after only one mission. So, the system requires high reliability. Guided missiles are one-shot weapon systems that have to be analyzed by storage reliability since they spend most of their life in storage. The analysis results depend on the model and the ratio of correct censored data. This study was conducted to propose a method to more accurately predict the future failure rate of Air force guided missiles. In the proposed method, the failure rate is predicted by both MTTF (Mean Time To Failure) and MTBF (Mean Time Between Failure) models and the model with a smaller error from the real failure rate is selected. Next, with the selected model, the ratio of correct censored data is selected to minimize the error between the predicted failure rate and the real failure rate. Based on real field data, the comparative result is determined and the result shows that the proposed sampling rate can predict the future failure rate more accurately.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.175-180
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• 2001

This paper presents the optimal fraction of validation set to obtain a prediction accuracy of software failure count or failure time in the future by a neuro-fuzzy system. Given a fixed amount of training data, the most popular effective approach to avoiding underfitting and overfitting is early stopping, and hence getting optimal generalization. But there is unresolved practical issues : How many data do you assign to the training and validation set\ulcorner Rules of thumb abound, the solution is acquired by trial-and-error and we spend long time in this method. For the sake of optimal fraction of validation set, the variant specific fraction for the validation set be provided. It shows that minimal fraction of the validation data set is sufficient to achieve good next-step prediction. This result can be considered as a practical guideline in a prediction of software reliability by neuro-fuzzy system.

• Korean small business review
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• v.42 no.3
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• pp.43-75
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• 2020

The main purpose of this study is to analyze research trend of 'business failure' from academic papers published in Asia Pacific Journal of Small Business. In this review, first, we reviewed research trend of failure, published in academic journals at abroad, explored the major topics, and set forth the framework of classification. Second, we selected and analyzed 16 Korean articles in a refined search from total 1,060 articles published in Asia Pacific Journal of Small Business from 1979 to 2019. Third, in order to understand overall research trend in Korea, additional publication search was done by online database system using keywords, and 24 other articles were selected. As a result, five research themes were identified and analyzed: (1) bankruptcy prediction, (2) emotion before and after failure, (3) costs of failure, (4) causes of failure, and (5) reentry determinants. We believe that this purposed review will offer future research issues regarding business failure.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1314-1323
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• 2018

For nuclear power plants (NPPs) to have long lifetimes, ageing is a major issue. Currently, ageing management for NPP systems is based on correlations built from generic experimental data. However, each system has its own characteristics, operational history, and environment. To account for this, it is possible to resort to prognostics that predicts the future state and time to failure (TTF) of the target system by updating the generic correlation with specific information of the target system. In this paper, we present an application of particle filtering for the prediction of degradation in steam generator tubes. With a case study, we also show how the prediction results vary depending on the uncertainty of the measurement data.

• Journal of radiological science and technology
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• v.45 no.5
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• pp.439-448
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• 2022

The purpose of this study was to analyze the results from statistical process control (SPC) to recommend upper and lower control limits for planning parameters based on delivery quality assurance (DQA) results and establish our institutional guidelines regarding planning parameters for helical tomotherapy (HT). A total of 53 brain, 41 head and neck (H & N), and 51 pelvis cases who had passing or failing DQA measurements were selected. The absolute point dose difference (DD) and the global gamma passing rate (GPR) for all patients were analyzed. Control charts were used to evaluate upper and lower control limits (UCL and LCL) for all assessed treatment planning parameters. Treatment planning parameters were analyzed to provide its range for DQA pass cases. We confirmed that the probability of DQA failure was higher when the proportion of leaf open time (LOT) below 100 ms was greater than 30%. LOT and gantry period (GP) were significant predictor for DQA failure using the SPC method. We investigated the availability of the SPC statistic method to establish the local planning guideline based on DQA results for HT system. The guideline of each planning parameter in HT may assist in the prediction of DQA failure using the SPC statistic method in the future.

• IE interfaces
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• v.24 no.2
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• pp.119-127
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• 2011

Most companies have been increasing temporary work projects to maximize the usage of their resources. They also have been developing the effective techniques for analyzing and managing the state of the projects. In order to monitor the state of a project in real-time and predict the project's future state more accurately, this paper suggests the Bayesian Network (BN) as a tool for discovering the causes of project risk and presenting the failure probability of the project. The proposed BN modeling method with consideration of the Earned Value Management (EVM) method shows how to induce the predictive and conditional probability of the risk occurrence in the future. The advantages of the suggested model are (1) that the cause of a project risk can be easily figured out via the BN, (2) that the future value of the project can be sufficiently increased by updating relevant components of the project, and (3) that more credible prediction can be made in the similar and future situation by using the data obtained in current analysis. A numerical example is also given.

• Structural Engineering and Mechanics
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• v.58 no.3
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• pp.459-473
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• 2016

Various vague and unstructured problems encountered the civil engineering/designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.1-12
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• 2017

Preventive maintenance is required for best performance of facilities in large scale industry. Ultimately, the efficiency of production is maximized by preventing the failure of facilities in advance. Typically, regular maintenance is conducted manually; however, it is hard to prevent repeated failures. Also, since measures to prevent failure depend on proactive problem-solving by the facility expert, they have limitations when the expert is absent or diagnosis error is made by an unskilled expert. Alarm system is used to aid manual facility diagnosis and early detection. However, it is not efficient in practice, since it is designed to simply collect information and is activated even with small problems. In this study, we designed and developed an automated preventive maintenance system based on expert's experience in detecting failure, determining the cause, and predicting future system failure. We also discussed the system structure designed to reuse the expert's knowledge and its applications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.138-142
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• 2018

This study used HILS to test an expansion joint, which is vulnerable to the water hammer effect. The operation data for the HIL simulator was the length rate of the expansion joint by the water hammer, which was used for life prediction based on the vibration durability. For the vibration durability test, the internal pressure of the expansion joint was assumed to be a factor of the durability life, and the lifetime prediction model equation was obtained by curve fitting the lifetime data at each pressure. During the test, the major failure modes of crack and water leakage occurred on the surface of the bellows part. The lifetime prediction model typically follows an inverse power law model. The pressure is a stress factor, and the model is effective in only a specific environment. Therefore, another stress factor such as temperature will be added and considered for a mixed lifetime prediction model in the future.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.886-890
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• 2007

Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Cao's method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results of CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers a potential for machine condition prognosis.