• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.1
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• pp.6-10
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• 2023

Only about 10% of selected equipment in nuclear power plants are monitored by wiring to address failures or problems caused by vibration. The purpose is primarily for preventive maintenance, not for predictive maintenance. This paper shows that vibration monitoring and diagnosis using Industrial 4.0 enables the complete predictive maintenance for all vibrating equipments in nuclear power plants with the convergence of internet of things; wireless technology, big data through periodic collection and artificial intelligence. Predictive maintenance using wireless technology is possible in all areas of nuclear power plants and in all systems, but it should satisfy regulatory guides on electromagnetic interference and cyber security.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.5
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• pp.497-502
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• 2010

Many forces are pressuring utilities to reduce operating and maintenance costs without cutting back on reliability or availability. Many utility managers are re-evaluating maintenance strategies to meet these demands. To utilities how to reduce maintenance costs and extent the effective operating life of equipment, predictive maintenance technique can be adapted. Predictive maintenance has three types program which arc in-house program, engineering company program and mixed program. We can approach successful predictive maintenance program with "smart trust" concept.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.53-59
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• 2021

The marine transport industry generally applies new technologies later than other transport industries, such as airways and railways. Vessels require efficient operation, and their performance and lifespan depend on the level of maintenance and management. Many studies have shown that corrective maintenance (CM) and time-based maintenance (TBM) have restrictions with respect to enabling efficient maintenance of workload and cost to improve operational efficiency. Predictive maintenance (PdM) is an advanced technology that allows monitoring the condition and performance of a target machine to predict its time of failure and helps maintain the key machinery in optimal working conditions at all times. This study presents the development of a marine predictive maintenance (MPdM; maritime predictive maintenance) method based on applying PdM to the marine environment. The MPdM scheme is designed by considering the special environment of the marine transport industry and the extreme marine conditions. Further, results of the study elaborates upon the concept of MPdM and its necessity to advancing marine transportation in the future.

• International Journal of Safety
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• v.5 no.2
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• pp.6-11
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• 2006

The time delay in servo control on LonWorks/IP Virtual Device Network (VDN) is highly stochastic in nature. LonWorks/IP VDN induced time delay deteriorates the performance and stability of the real-time distributed control system and hinders an effective preventive and predictive maintenance. Especially in real-time distributed servo applications on the factory floor, timely response is essential for predictive and preventive maintenance. In order to guarantee the stability and performance of the system for effective preventive and predictive maintenance, LonWorks/IP VDN induced time delay needs to be predicted and compensated for. In this paper position control simulation of DC servo motor using Zero Phase Error Tracking Controller (ZPETC) as a feedforward controller, and Internal Model Controllers (IMC) based on Smith predictor with disturbance observer as a feedback controller is performed. The validity of the proposed control scheme is demonstrated by comparing the IMC based on Smith predictor with disturbance observer.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.221-232
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• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational reliability of the ICT/Cold-Chain Unmanned Storage, a predictive maintenance system was implemented based on the LSTM model. In this paper, a server for data management, such as collection and monitoring, and an analysis server that notifies the monitoring server through data-based failure and defect analysis are separately distinguished. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on RabbitMQ, loading data in an InMemory method using Redis, and managing snapshot data DB in real time. The predictive maintenance platform can contribute to securing reliability by identifying potential failures and defects that may occur in the operation of the ICT/Cold-Chain Unmanned Storage in the future.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.215-225
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• 2024

Predictive maintenance has been considered fundamental in the industrial applications in the last few years. It contributes to improve reliability, availability, and maintainability of the systems and to avoid breakdowns. These breakdowns could potentially lead to system shutdowns and to decrease the production efficiency of the manufacturing plants. The present article aims to study how predictive maintenance could be planed into the production scheduling, through a systematic review of literature. . The review includes the research articles published in international journals indexed in the Scopus database. 165 research articles were included in the search using #predictive maintenance# AND #production scheduling#. Press articles, conference and non-English papers are not considered in this study. After careful evaluation of each study for its purpose and scope, 50 research articles are selected for this review by following the 2020 Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) statement. A benchmarking of predictive maintenance methods was used to understand the parameters that contributed to improve the production scheduling. The results of the comparative analysis highlight that artificial intelligence is a promising tool to anticipate breakdowns. An additional impression of this study is that each equipment has its own parameters that have to be collected, monitored and analyzed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.133-142
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• 2023

The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.123-128
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• 1997

A data base design framework for predictive a preventive-maintenance system is presented in this paper in order to effectively control machines and reduce accident rates in the workplace. The data base is designed to meet general management requirements to evaluate different maintenance strategies. There are seven data files: the equipment list maintenace pesonnel, maintenance history, maintenance specification, spare part, maintenance equipment, and maintenance schedules. Each data base file has several record based upon data acquisition.

• Journal of the Semiconductor & Display Technology
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• v.19 no.1
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• pp.1-5
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• 2020

In this paper, a predictive maintenance of the robot trouble using the machine learning method, so called MT(Mahalanobis Taguchi), was studied. Especially, 'MD(Mahalanobis Distance)' was used to compare the robot arm motion difference between before the maintenance(bearing change) and after the maintenance. 6-axies vibration sensor was used to detect the vibration sensing during the motion of the robot arm. The results of the comparison, MD value of the arm motions of the after the maintenance(bearing change) was much lower and stable compared to MD value of the arm motions of the before the maintenance. MD value well distinguished the fine difference of the arm vibration of the robot. The superior performance of the MT method applied to the prediction of the robot trouble was verified by this experiments.

• 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.