• Plant Journal
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• v.12 no.2
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• pp.31-35
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• 2016

A method of failure data management for Reliability-Centered Maintenance was shown for a boiler feedwater pump of a power plant. The major part of it is an analysis of failure mode, failure cause, and failure effects, which is the main component of a failure data base like OREDA(Offshore Reliability Data). Case study shows main element of the preventive maintenance planning such as the maintenance period can be statistically determined from the failure data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2052-2060
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• 2017

The SOLAS ship should carry at least 83 different types of equipment based on the SFI group codes and each of which consists of several to dozens of components. During ship operation, it is necessary to ensure the normal operation of such equipment, and remote maintenance is highly demanded for immediate repair in the event of a equipment fault. This study proposes to find suitable classification system and to derive database structure for remote maintenance of navigation and communication equipment. As a result of this study, the classification system of equipment should be layered into equipment type, model, and component, and main table in the database consists of FMEA, service history, case data through Q&A, Preventive Maintenance. A database was constructed for 140 navigation and communication equipment models and 750 components. In order to evaluate the practical effects, service engineer evaluated the usefulness using the cloud app.

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