• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.113-118
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• 2023

Maintenance of semiconductor equipment processes is crucial for the continuous growth of the semiconductor market. The process must always be upheld in optimal condition to ensure a smooth supply of numerous parts. Additionally, it is imperative to monitor the status of the robots that play a central role in the process. Just as many senses of organs judge a person's body condition, robots also have numerous sensors that play a role, and like human joints, they can detect the condition first in the joints, which are the driving parts of the robot. Therefore, a normal state test bed and an abnormal state test bed using an aging reducer were constructed by simulating the joint, which is the driving part of the robot. Various sensors such as vibration, torque, encoder, and temperature were attached to accurately diagnose the robot's failure, and the test bed was built with an integrated system to collect and control data simultaneously in real-time. After configuring the user screen and building a database based on the collected data, the characteristic values of normal and abnormal data were analyzed, and machine learning was performed using the KNN (K-Nearest Neighbors) machine learning algorithm. This approach yielded an impressive 94% accuracy in failure diagnosis, underscoring the reliability of both the test bed and the data it produced.

• Journal of Information Technology Services
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• v.20 no.3
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• pp.1-12
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• 2021

Reactor Coolant Pump (RCP) is core part of nuclear power plant to provide the forced circulation of reactor coolant for the removal of core heat. Properly monitoring vibration of RCP is a key activity of a successful predictive maintenance and can lead to a decrease in failure, optimization of machine performance, and a reduction of repair and maintenance costs. Here, we developed real-time RCP Vibration Analysis System (VAS) that web based platform using NoSQL DB (Mongo DB) to handle vibration data of RCP. In this paper, we explain how to implement digital signal process of vibration data from time domain to frequency domain using Fast Fourier transform and how to design NoSQL DB structure, how to implement web service using Java spring framework, JavaScript, High-Chart. We have implement various plot according to standard of the American Society of Mechanical Engineers (ASME) and it can show on web browser based on HTML 5. This data analysis platform shows a upgraded method to real-time analyze vibration data and easily uses without specialist. Furthermore to get better precision we have plan apply to additional machine learning technology.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.63-68
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• 2017

Although automated metro subway systems have the advantage of operating a train without a train driver, it is difficult to detect an immediate fault condition and take countermeasures when an unusual situation occurs. Therefore, it is important to construct a maintenance information system (MIS) that detects the vehicle failure/status information in real time and maintains it efficiently in the depot of the railway's vehicles. This paper proposes a conceptual design method that realizes the interface between the train control system (TCS), the operation control center train control monitoring system (OCC-TCMS) console, and the MIS using wireless communication network in real-time. To transmit a large amount of information on 800,000 occurrences per day during operation, data was collected in a 56 byte data table using a data processing algorithm. This state information was classified into 4 hexadecimal codes and transmitted to the MIS by mapping the status and the fault information on the vehicle during the main line operation. Furthermore, the transmission and reception data were examined in real time between the TCS and MIS, and the implementation of the failure information screen was then displayed.

• Computers and Concrete
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• v.25 no.1
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• pp.15-27
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• 2020

During the past decades, the application of acoustic emission techniques (AET) through the diagnosis and monitoring of the fracture process in materials has been attracting considerable attention. AET proved to be operative among the other non-destructive testing methods for various reasons including their practicality and cost-effectiveness. Concrete and rock structures often demand thorough and real-time assessment to predict and prevent their damage nucleation and evolution. This paper presents an overview of the work carried out on the use of AE as a monitoring technique to form a comprehensive insight into its potential application in brittle materials. Reported properties in this study are crack growth behavior, localization, damage evolution, dynamic character and structures monitoring. This literature review provides practicing engineers and researchers with the main AE procedures to follow when examining the possibility of failure in civil/resource structures that rely on brittle materials.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1012-1024
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• 2016

This study deals with an on-line software fault detection, localization, and system reconfiguration method for electrical system drives composed of three-phase AC/DC/AC converters and three-phase permanent magnet synchronous machine (PMSM) drives. Current sensor failure (outage), speed/position sensor loss (disconnection), and damaged DC-link voltage sensor are considered faults. The occurrence of these faults in PMSM drive systems degrades system performance and affects the safety, maintenance, and service continuity of the electrical system drives. The proposed method is based on the monitoring signals of "abc" currents, DC-link voltage, and rotor speed/position using a measurement chain. The listed signals are analyzed and evaluated with the generated residuals and threshold values obtained from a Sliding Mode Current-Speed-DC-link Voltage Observer (SMCSVO) to acquire an on-line fault decision. The novelty of the method is the faults diagnosis algorithm that combines the use of SMCSVO and adaptive thresholds; thus, the number of false alarms is reduced, and the reliability and robustness of the fault detection system are guaranteed. Furthermore, the proposed algorithm's performance is experimentally analyzed and tested in real time using a dSPACE DS 1104 digital signal processor board.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.147-154
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• 1998

As technical systems in building are being developed, the processes and systems get more difficult for the average operator to understand. When operating a complex facility, it is beneficial in equipment management to provide the operator with tools which can help in dicision making for recovery from a failure of the system. The main object of the study is to develop real-time automatic fault detection and diagnosis system for optimal operation of IBS building.

• 유체기계공업학회:학술대회논문집
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• 2001.11a
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• pp.315-321
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• 2001

The shaft system of turbine is composed of rotating shaft, blades, bearings which support the shaft, packing seal which prevent the leakage of steam, and couplings which connect the shaft. Shaft system component failure, incorrect assemblage or deflection by unexpected forces causes vibration problem. And every turbine has its own characteristics in dynamic response. In this paper we propose the three-bearing supported type rotor which is real equipment and being operated this time as commercial operation. From 1996 it has a high vibration problem and there are many kinds of trial to solve this problem. In resent outage we performed a special diagnosis and carried out appropriate work. We would like to introduce and explain about this case history.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1198-1205
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• 2003

Most domestic fossil power plant have exceeded 100,000 hours of operation with the severe operating condition. Among the critical components of fossil power plant, high temperature steam pipe system have had a many problems and damage from unstable displacement behavior because of frequent start up and shut down. In order to prevent the serious damage and failure of the critical pipe system in fossil power plant, 3-dimensional displacement measurement system were developed for the on-line monitoring system. 3-D Measurement system was developed with using the LVDT type sensor and rotary encoder type sensor, this system was installed and operated on the real power plant successfully. In the future time, network system of on-line diagnosis for critical pipe will be designed.

• Journal of Periodontal and Implant Science
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• v.42 no.6
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• pp.217-223
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• 2012

Purpose: Several parameters have been described for determining the success or failure of dental implants. The surface properties of transgingival implant components have had a great impact on the long-term success of dental implants. The purpose of this study was to compare the tendency of two periodontal pathogens to adhere to and colonize zirconia abutments and titanium alloys both in hard surfaces and soft tissues. Methods: Twelve patients participated in this study. Three months after implant placement, the abutments were connected. Five weeks following the abutment connections, the abutments were removed, probing depth measurements were recorded, and gingival biopsies were performed. The abutments and gingival biopsies taken from the buccal gingiva were analyzed using real-time polymerase chain reaction to compare the DNA copy numbers of Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, and total bacteria. The surface free energy of the abutments was calculated using the sessile water drop method before replacement. Data analyses used the Mann Whitney U-test, and P-values below 0.05 find statistical significance. Results: The present study showed no statistically significant differences between the DNA copy numbers of A. actinomycetemcomitans, P. gingivalis, and total bacteria for both the titanium and zirconia abutments and the biopsies taken from their buccal gingiva. The differences between the free surface energy of the abutments had no influence on the microbiological findings. Conclusions: Zirconia surfaces have comparable properties to titanium alloy surfaces and may be suitable and safe materials for the long-term success of dental implants.