• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.84-94
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• 2018

In recent years, the diminishing of operation and maintenance cost using advanced maintenance technology is attracting many companies' attention. Especially, the heavy machinery industry regards it as a crucial problem since a failure of heavy machinery requires high cost and long downtime. To improve the current maintenance process, the heavy machinery industry tries to develop a methodology to predict failure in advance and to find its causes using usage data. A better analysis of failure causes requires more data so that various kinds of sensor are attached to machines and abundant amount of product usage data is collected through the sensor network. However, the systemic analysis of the collected product usage data is still in its infant stage. Many previous works have focused on failure occurrence as statistical data for reliability analysis. There have been less works to apply product usage data into root cause analysis of product failure. The product usage data collected while failures occur should be considered failure cause analysis. To do this, this study proposes a methodology to apply product usage data into failure cause analysis. The proposed methodology in this study is composed of several steps to transform product usage into failure causes. Various statistical analysis combined with product usage data such as multinomial logistic regression, T-test, and so on are used for the root cause analysis. The proposed methodology is applied to field data coming from operated locomotive and the analysis result shows its effectiveness.

• Korean Journal of Construction Engineering and Management
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• v.18 no.5
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• pp.32-40
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• 2017

Korean construction industry is facing downtime mood and need more productivity. In order to successfully accomplish the construction project with more productivity, systematic and reasonable schedule management techniques should be developed and utilized. Therefore, in this study, first, a current status of the schedule management in the construction site is identified and the improvements in the schedule management are suggested. And, the effective schedule management technique in the construction site is proposed based on the in-site experience of the researcher. In order to find solutions, survey questionnaires designed to measure the understanding of process management and measure perception of the importance of schedule management. Total twenty nine field engineers who are performing schedule management of construction project at domestic civil engineering construction site were participated in the survey, and In-depth interviews were conducted. Eleven construction sites are inspected. Finally, a simple and effective schedule management technique including from WSB classification to schedule analysis, performance management, and performance improvement is proposed based on the inspection of current status of schedule management in civil engineering construction site.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1403-1412
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• 2019

Effect of damage and loss cost for downtime is huge, if physical devices such as turbines, pipe, and storage tanks are in the abnormal state originated from not only aging, but also cyber attacks on the control and monitoring system like PLC (Programmable Logic Controller). To improve availability and dependability of the physical devices, we design and implement an indirect health monitoring system which sense temperature, acceleration, current, etc. indirectly, and put sensor data into Influx DB in real-time. Then, the actual performance of detecting abnormal state is shown using the indirect health monitoring system. Analyzing data are acquired using the real-time indirect health monitoring system, abnormal state and security threats can be double-monitored and lower maintenance cost utilizing prognostics and health management.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.6
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• pp.1458-1464
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• 2007

Today, domestic and foreign manufacturing industries have to cope with obsolescence of manufacturing equipment because the shifting market trends drive the rapid changes in the production process resulting in stressful operation. Quality control process for manufacturing and production involves a familiar step - when the production process is completed, every item is subjected to various routine tests to determine that it meets the minimum quality standards. Typically, when a faulty product is found, the production line has to be stopped and the current batch is marked for further inspected and exhaustive testing. In this research, we propose a quality monitoring system for productivity enhancement. Our approach aims to reduces the operational down time in the production line of a car-component factory. The proposed monitoring system for productivity enhancement is designed to collect the data through testing at each phase of the assembly line and uses predictive methods on the collected data to achieve early detection of faults in the production process and minimize the number of products in a faulty batch thus minimizing the losses incurred from defective products. More importantly, this system aims to forecast and proactively detect faults and activate warnings when they are detected thus minimizing items in the defective batch, reducing the damage to manufacturing equipment and ultimately reducing the operational downtime or the delay in the resumption of normal factory operation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.6
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• pp.70-77
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• 2004

In many industrial applications, an unexpected fault of induction motor drive systems can cause serious troubles such as downtime of the overall system heavy loss, and etc. As one of methods to solve such problems, this paper investigates the fault diagnosis for open-switch damages in a voltage-fed PWM inverter for induction motor drive. For the feature extraction of a fault we transform the current signals to the d-q axis and calculate mean current vectors. And then, for diagnosis of different fault patterns, we propose a clustering based diagnosis algorithm The proposed diagnostic technique is a modified ANFIS(Adaptive Neuro-Fuzzy Inference System) which uses a clustering method on the premise of general ANFIS's. Therefore, it has a small calculation and good performance. Finally, we implement the method for the diagnosis module of the inverter with MATLAB and show its usefulness.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1320-1329
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• 2013

According to the comparing the quality and fatigue of Hands-only CPR with counting by middle-aged women who is most likely to witness the cardiac arrest. This paper wants to provide the basic data to establish a CPR education program for the role of the first responders. After conducted three hours of basic life support training, it divided into two 45-persons groups by assignment of probability. 2-minutes research conducted with dummy by dividing into Group-A that counting the number loudly during the Hands-Only CPR, And Group-B that does not counting the number during the Hands-Only CPR. Between the two groups, the quality of Hands-Only CPR does not showed its difference clearly and the downtime of Hands-Only CPR was reduced, Depending on the over time, the frequency that reduces the depth of Hands-Only CPR was also significantly lower. And after the Hands-Only CPR, the fatigability who felt themselves was also significantly lower.

• Smart Structures and Systems
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• v.13 no.2
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• pp.177-201
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• 2014

With the increased size and flexibility of the tower and blades, structural vibrations are becoming a limiting factor towards the design of even larger and more powerful wind turbines. Research into the use of vibration mitigation devices in the turbine tower has been carried out but the use of dampers in the blades has yet to be investigated in detail. Mitigating vibrations will increase the design life and hence economic viability of the turbine blades and allow for continual operation with decreased downtime. The aim of this paper is to investigate the effectiveness of Semi-Active Tuned Mass Dampers (STMDs) in reducing the edgewise vibrations in the turbine blades. A frequency tracking algorithm based on the Short Time Fourier Transform (STFT) technique is used to tune the damper. A theoretical model has been developed to capture the dynamic behaviour of the blades including the coupling with the tower to accurately model the dynamics of the entire turbine structure. The resulting model consists of time dependent equations of motion and negative damping terms due to the coupling present in the system. The performances of the STMDs based vibration controller have been tested under different loading and operating conditions. Numerical analysis has shown that variation in certain parameters of the system, along with the time varying nature of the system matrices has led to the need for STMDs to allow for real-time tuning to the resonant frequencies of the system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.869-874
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• 2019

Performing preventive maintenance on a system reduces unexpected downtime caused by system aging and increases its availability. In general, preventive maintenance can be largely divided into two broad categories: time-based maintenance policy and condition-based maintenance policy. In the time-based maintenance policy the preventive maintenance is triggered at scheduled time epochs with fixed time intervals, while in the condition-based maintenance policy the preventive maintenance is performed when system state is checked to satisfy a specific condition. Condition-based maintenance has some benefits in improving maintenance efficiency, compared to time-based one. This paper presents a stochastic model for analyzing a system with condition-based preventive maintenance, where the preventive maintenance is performed after a random time since the system aging occurs, and provides an analytical solution for the steady-state availability and the corresponding profit.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.201-206
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• 2019

Rejuvenating a system periodically during the most idle time of the system reduces unexpected downtime caused by software aging and increases its availability. In general, software rejuvenation can be largely divided into two broad categories: time-based rejuvenation policy and condition-based rejuvenation policy. In time-based rejuvenation policy the software rejuvenation is triggered at scheduled time epochs with fixed time intervals, while in condition-based rejuvenation policy the software rejuvenation is performed when system state is checked to satisfy a specific condition. Conditionbased policy adds extra cost to the system due to system monitoring and aging estimation. This paper presents a stochastic model for analyzing time-based software rejuvenation mechanism, where the rejuvenation is triggered at scheduled time epochs with fixed time intervals, and provides an analytical solution for the steady-state availability, the user-perceived availability, and the corresponding cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1610-1629
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• 2021

Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals' macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.