• Proceedings of the Korean Reliability Society Conference
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• 2000.04a
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• pp.177-183
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• 2000

As monitoring, testing, and measuring techniques develop, predictive control of components and complete systems have become more practical and affordable. In this paper we develop a statistics-based approach assuming nonlinear degradation paths and time-dependent standard deviation. This approach can be extended to provide reliability estimates and limit value determination in the censoring case fur predictive maintenance policy.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2001.11a
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• pp.31-35
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• 2001

Recent trends require that access to the device/equipment information be provided from several locations or anywhere in the enterprise. One example is virtual machine/manufacturing system (VMS) where predictive maintenance is performed both on factory floor and in remote site through internet ［1］. Internet access is increasingly available and affordable, and along with the "internet" is the backbone of modern enterprise data networks. Typical functions of such a system includes monitoring and control for diagnosis and remedy action in realizing preventive maintenance.(omitted)

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.271-280
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• 2019

Smart factory, a critical part of digital transformation, enables data-driven decision making using monitoring, analysis and prediction. Predictive maintenance is a key element of smart factory and the need is increasing. The purpose of this study is to analyze the degradation characteristics of a galvanizing kettle for the steel plating process and to predict the remaining useful life(RUL) for predictive maintenance. Correlation analysis, multiple regression, principal component regression were used for analyzing factors of the process. To identify the trend of degradation, a proposed rolling window was used. It was observed the degradation trend was dependent on environmental temperature as well as production factors. It is expected that the proposed method in this study will be an example to identify the trend of degradation of the facility and enable more consistent predictive maintenance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.396-398
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• 2006

Predictive maintenance is not an unfamiliar concept because it has been used to predict the failures of electrical equipment such as transformers, motors and so on. By thoroughly monitoring the status of individual equipment and tracing how the various characteristics change over time, we can be aware of its exact condition and prevent the impending failure by taking appropriate actions. In this paper, I will extend the concept of predictive maintenance for individual electrical equipment to the power distribution system and show how to use the data obtained from power quality monitors to improve the power system.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2058-2060
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• 2002

The demand for safety and reliability of Nuclear Power Plants (NPPs) has been constantly increasing and economical operation is also an important issue. Developing and adopting predictive maintenance technology for the major systems or equipment is considered as one way to achieve these goals. This paper suggests the predictive maintenance methodology that can be applied to NPPs and describes a sample application of the Rod Control System (RCS) to verify the effectiveness of the methodology. It is expected that the same methodology can be adopted for other systems of NPPs and general industry fields when its effectiveness is verified.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.846-854
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• 1996

In this paper, we propose a solution to the software maintenance problem that is a primary factor of software crisis. We surveyed and analyzed the current software maintenance problems through questionnaires and interviews. As a result, we defined the software maintenance management life cycle and established a fundamental strategies to solve the software maintenance problems efficiently. We also designed a software maintenance management support systems to construct an automated software maintenance management tool. Furthermore, tp improve the formalization and reliability of the software maintenance management procedure, we defined acost predictive model using a fixed-single parameter based on comprehensive program size for the source code and delivered effort(person/month). We elaborated the model by considering an experience level of maintainer, a skill- level defined by the manager, and a reliability level required by the model of maintenance management.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.150.3-150
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• 2001

Requirements for Distributed Monitoring and Control Networks (DMCN) differ greatly from those of typical data networks. Specifically, any DMCN technology which employs a fieldbus protocol is different from If network protocol TCP/IP. In general, one needs to integrate fieldbus protocol and TCP/IP to realize DMCN over IP network or internet Interoperability between devices and equipments is essential to enhance the quality and the performance of predictive maintenance (PM). This paper suggests a basic framework for LonWorks-based DMCN over IP network and a method to guarantee interoperability between devices and equipments.

• Vacuum Magazine
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• v.2 no.1
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• pp.31-34
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• 2015

This article introduces fundamentals of self-diagnosis and predictive (or preventive) maintenance technologies for dry vacuum pumps. The state variables of dry pumps are addressed, such as the pump and motor body temperatures, consumption currents of main and booster pumps, mechanical vibration, and exhaust pressure, etc. The adaptive parametric models of the state variables of the dry pump are exploited to provide dramatic reduction of data size and computation time for self-diagnosis. Two indicators, the Hotelling's $T^2$ and the sum of squares residuals (Q), are illustrated to be quite effective and successful in diagnosing dry pumps used in the semiconductor processes.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.453-456
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• 2007

The purpose of this study is to explain the importance of Vibration Monitoring Device by introducing an example of Predictive Maintenance System using Condition Monitoring System of Hydro-turbine generator. Confirming vibration of generation equipment is commissioning procedure during equipment completion for checking guaranteed items. Data from Generator output range are used to determine output band to continue the performance of equipment. The Vibration Monitoring System is not absolute method of maintenance, but if it is used well with expert, it will be visible, data-analyzed, scientific maintenance more than others. And also, Condition Monitoring System is very important for remote controlled small hydro-power plant although most of it is installed in Large hydro-power plant.