• Journal of the Society of Naval Architects of Korea
• /
• v.59 no.4
• /
• pp.200-206
• /
• 2022

This study is to develop database by an experimental method for the development of condition based maintenance for auxiliary equipment in marine engine systems. Existing ships have been performing regular maintenance, so the actual measurement data development is very incomplete. Therefore, it is best to develop a database on land tests. In this paper, a database developed by an experimental method is presented. First, failure case analysis and reliability analysis were performed to select a failure mode. For the failure simulation test, a test bed for land testing was developed. The failure simulation test was performed based on the failure simulation scenario in which the failure simulation test plan was defined. A 1.5TB failure simulation database has been developed, and it is expected to serve as a basis for ship failure diagnosis and prediction algorithm model development.

• Journal of the Society of Naval Architects of Korea
• /
• v.60 no.2
• /
• pp.95-109
• /
• 2023

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.

• Journal of the Korean GEO-environmental Society
• /
• v.19 no.2
• /
• pp.37-44
• /
• 2018

Ballasted track has been widely used due to its flexibility. However, the plastic deformation of ballasted track causes the evolution of track geometrical errors, and hence it requires continuous maintenance; increase in number of trains, weight, and speed expedites maintenance frequency and cost as well. Ballast stiffness is well-known as an indicator of design and maintenance. In this regard, this paper aims to suggest the method to measure ballast track stiffness using light-weight deflectometer (LWD) and thus verify its applicability as a maintenance measure. Preliminary field tests determined simple field testing protocol to measure track stiffness. The apparent ballast stiffness by LWD shows good corelation with TQI (Track Quality Index) and maintenance length. That is, as average of apparent stiffness increase, TQI and tamping length decrease exponentially. Therefore, apparent stiffness can be used as an index for ballast condition assessment.

• Transactions of the Korean Society of Pressure Vessels and Piping
• /
• v.8 no.2
• /
• pp.26-32
• /
• 2012

The systems of the nuclear power plant are designed based on the User Requirement Document, and Korea Hydro & Nuclear Power Co. (KHNP) implements preventive maintenance activities to keep the specific design function of the system consistently. To monitor the preventive maintenance effectiveness, KHNP has also developed maintenance effectiveness monitoring (MR) program based on NUMARC 93-01 since 2003, and has implemented the program in all operating plants. Recently, KHNP has upgraded MR programs by reflecting implementing experiences ; reestablishing the performance monitoring level, improving analysis for standby function and performance criteria for passive components, reestablishing the availability performance criteria and the performance criteria for the same type of components. These upgraded MR programs will contribute to enhance safety and improve equipment reliability through monitoring maintenance effectiveness.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.4
• /
• pp.693-700
• /
• 2024

This study aims to analyze the concept and application of AI-based predictive maintenance in the railroad industry and draw implications from it. Focusing on the adoption of AI-based maintenance systems by the Korea Railroad Corporation and Seoul Metro, we examined how AI technology can improve the efficiency and safety of railroad operations. We also compared and analyzed the application of AI technology in the European railroad industry through the cases of Deutsche Bahn in Germany and SNCF in France. The study found that AI-powered predictive maintenance contributes to reducing the frequency of breakdowns, reducing maintenance costs, and increasing the reliability of railroad operations.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.14 no.6
• /
• pp.229-237
• /
• 2010

This study analyzes the factors that affect the safety condition level of road-bridges, one of the important infrastructures. Utilizing Binary Logit model, this report empirically identifies the key factors that has influenced the recent assessed safety condition level of the first and the second major types of road-bridges, managed by public agencies, and the changes of the safety level for last six years. As a result of the analysis, the most important factor that influences the safety condition level is not the physical characteristics, but the management quality. As road-bridges are getting older and older, the management quality tends to bring about more differentials in assessing the safety condition level. The safety condition level, C or D, is likely to be improved the level, A or B, is likely to become degraded. To achieve the goal that keep the safety condition level, A and B, more than 90%, it should be considered to make the degrading rate from B to C lower. However, this study includes the limitation on data. It is essential to collect structure data that are spread out in many agencies to complement the limitation for further research.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1999.04a
• /
• pp.451-452
• /
• 1999

• Journal of the Korean Professional Engineers Association
• /
• v.35 no.4
• /
• pp.19-23
• /
• 2002

Recently, deficit of water for daily We, industrial, agricultural use and Increasing water demand of river maintenance has increased gradually by the improvement of living condition of the Republic of Korea. Comprehensive measures for water deficit In the future are studied, based on the Investigated result of the actual condition of water use.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.21 no.1
• /
• pp.15-22
• /
• 2017

This study focuses on the investigation of bond strength of magnesium potassium phosphate cement mortar(MKPC) according to moisture condition of substrate. Tensile bond test, shear bond test and interfacial bond test are adopted for evaluating the adhesion characteristics of MKPC to conventional cement mortar substrate. The main experimental variables are test methods and moisture levels of substrate. Because the moisture condition of the substrate may be critical to achieving bond, optimum moisture condition for a conventional concrete substrate has evaluated in this study. The results are as follows ; The effects of moisture condition at substrate into the bonding of MKPC are less different than polymer cement mortar and epoxy mortar. But the saturated and surface dry condition is the most appropriate moisture level among the considered, followed by saturated condition and wet condition. Thus, an adequate moisture level of substrate for MKPC is essential for good bond strength.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.700-706
• /
• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.