• Journal of the Korean Society of Safety
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• v.21 no.4 s.76
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• pp.25-32
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• 2006

LonWorks over IP(LonWorks-IP) virtual device network(VDN) is an integrated form of LonWorks device network and IP data network. In especially real-time distributed servo applications on the factory floor, timely response is essential for predictive and preventive maintenance. The time delay in servo control on LonWorks-IP based VDN has highly stochastic nature. LonWorks-IP based VDN induced transmission delay deteriorates the performance and stability of the real-time distributed control system and can't give an effective preventive and predictive maintenance. In order to guarantee the stability and performance of the system, and give an effective preventive and predictive maintenance, LonWorks-IP based VDN induced time-varying uncertain time delay needs to be predicted and compensated. In this paper new Pill control scheme based on Smith predictor, disturbance observer and band pass filter is proposed and tested through computer simulation about position control of DC servo motor. It is shown that how can the proposed control scheme be designed to minimize the effects of uncertain varying time delay and model uncertainties. The validity of the proposed control scheme is compared and demonstrated with the comparison of internal model controllers(IMC) based on Smith predictor with and without disturbance observer.

• Journal of the Korean Society of Safety
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• v.17 no.4
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• pp.196-201
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• 2002

Web-based predictive maintenance (PM) utilizes Virtual Device Network (VDN). VDN inevitably the implementation of Distributed Monitoring and Control Networks (DMCN). In general, one needs to integrate fieldbus protocol and TCP/IP to realize DMCN over IP network or internet, which can be viewed as Virtual Device Network (VDN). Interoperability between devices and equipments is essential to enhance the quality and the performance of predictive maintenance (PM). This paper investigates the transmission characteristics of VDN and suggests a basic framework for web-based PM using DMCN over IP network.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.100-107
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• 2015

Recently as the manufacturers want competitiveness in dynamically changing environment, they are trying a lot of efforts to be efficient with their production systems, which may be achieved by diminishing unplanned operation stops. The operation stops and maintenance cost are known to be significantly decreased by adopting proper maintenance strategy. Therefore, the manufacturers were more getting interested in scheduling of exact maintenance scheduling to keep smooth operation and prevent unexpected stops. In this paper, we proposedan integrated maintenance approach in injection molding manufacturing line. It consists of predictive and preventive maintenance approach. The predictive maintenance uses the statistical process control technique with the real-time data and the preventive maintenance is based on the checking period of machine components or equipment. For the predictive maintenance approach, firstly, we identified components or equipment that are required maintenance, and then machine parameters that are related with the identified components or equipment. Second, we performed regression analysis to select the machine parameters that affect the quality of the manufactured products and are significant to the quality of the products. By this analysis, we can exclude the insignificant parameters from monitoring parameters and focus on the significant parameters. Third, we developed the statistical prediction models for the selected machine parameters. Current models include regression, exponential smoothing and so on. We used these models to decide abnormal patternand to schedule maintenance. Finally, for other components or equipment which is not covered by predictive approach, we adoptedpreventive maintenance approach. To show feasibility we developed an integrated maintenance support system in LabView Watchdog Agent and SQL Server environment and validated our proposed methodology with experimental data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.239-240
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• 2010

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.60-66
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• 2021

The importance of maintenance is leading the application of the maintenance strategy in various industries. The maritime industry is also a part of them, with changes in selecting and applying the maintenance strategy, but rather slowly, by retaining the old strategy. In particular, the ship is maintaining a previously used strategy. In the circumstance of the sea, ship requires a new suggestion for maintenance strategy. A ship predictive maintenance model predicts the breakdown or malfunction of machineries to secure maintenance time with preventive actions and treatments, thereby avoiding maintenance-related dangerous factors. This study focused on applying text analysis to an Liquefied Natural Gas Carriers dock indent document, and the analysis results were interpreted from the original document. The inter-relational patterns observed from the frequency of common maintenance combinations among different parts and equipment in ships will be applied to the development of ship predictive maintenance.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.6 no.1
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• pp.1-8
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• 2010

The existing maintenance program is focused on time-based maintenance to inspect and repair components according to maintenance period, rather than condition-based maintenance or predictive maintenance. The preventive maintenance template of the steam turbine has been developed for optimizing maintenance procedure and improving reliability and availability of the steam turbine of nuclear power plants based on EPRI PM template methodology and EPRI technical reports about preventive maintenance.

• Journal of KSNVE
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• v.4 no.4
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• pp.459-467
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• 1994

Various maintenance programs and techniques have been implemented for roating machineries, since machines were invented for commerical use. The earliest type of maintenance was run-to-failure, where the machine was run until a fault caused to fail in service. It was obviously an expensive approach due to the unpredictability of the machine condition. Another type is the periodic maintenance, where machines are disassembled and overhauled on regular schedules. With the advent of reliable data collectors including FFT analyzer and developing of versatile supporting software such as ExpertALERT system, the predictive maintenance is known to be the most feasible maintenance type these days. The vibration analysis enables for a maintenance crew to find the exact cause of fault on a machine and to make a proper maintenance schedule with a trend analysis. The predicitive maintenance is considered to be the most important part of pro-active maintenance.

• International Journal of Reliability and Applications
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• v.2 no.1
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• pp.57-71
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• 2001

Global competition to increase production output and to improve quality is spurring manufacturing companies to use condition monitoring and fault diagnostic systems for predictive maintenance. As monitoring, testing, and measuring techniques develop, predictive control of components and complete systems have become more practical and affordable. In this article, we will consider the computer based data acquisition system for condition monitoring and the condition parameter analysis techniques for fault detection and diagnostics in the machinery and briefly discuss reliability prediction and the limit value determination in condition monitoring.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.172-174
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• 2021

In the existing industry, maintenance was carried out in the form of preventive maintenance such as occurrence of unnecessary idle time due to limited monitoring and maintenance. However, with the advent of the Fourth Industrial Revolution, real-time monitoring is possible in many industries including mining, manufacturing, oil and gas, and commercial agriculture, and it is desired to minimize idle time due to maintenance. In particular, there is a growing interest in predictive maintenance that can reduce costs and maximize operational efficiency by predicting and maintaining a failure before equipment and equipment fail. In this study, we look at the predictive maintenance technology that can verify the abnormal condition of the equipment of the smart factory in advance and monitor the abnormal condition in real time.

• Journal of the Korean Society of Safety
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• v.16 no.4
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• pp.213-217
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• 2001

Requirements for Device Networks differ greatly from those of typical data networks. Specifically, any device network technology which employs a fieldbus protocol is different from IP network protocol TCP/IP. In general, one needs to integrate fieldbus protocol and TCP/IP to realize device network 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 web-based predicted maintenance and a method to guarantee interoperability between devices and equipments.