• International Journal of Reliability and Applications
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• v.10 no.1
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• pp.1-15
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• 2009

Even though the impact of manufacturing quality to reliability is not considered much as well as that of design area, a major cause of an early failure of the product is known as manufacturing problem. This research applies two different types of neural network algorithms, the Back propagation (BP) algorithm and Learning Vector Quantization (LVQ) algorithm, to identify and classify the nonrandom variation pattern on the control chart based on knowledge-based diagnosis of dimensional variation. The performance and efficiency of both algorithms are evaluated to choose the better pattern recognition system for auto body assembly process. To analyze hundred percent of the data obtained by Optical Coordinate Measurement Machine (OCMM), this research considers an application in which individual observations rather than subsample means are used. A case study for analysis of OCMM data in underbody assembly process is presented to demonstrate the proposed knowledge-based pattern recognition system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• Journal of the Korean Institute of Gas
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• v.2 no.3
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• pp.54-59
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• 1998

As distributed control systems (DCS) and plant information systems (PIS) are introduced into gas industries, process monitoring systems based on process data have attracted significant interests. However, these technologies have not been fully due to strong nonlinearities of batch processes. The multiway principal component analysis, which has been recently developed, has solved these problems and has been widely used in the industries. However, the lack of statistical background of process operators has been one of major obstacles for maximum utilization of the technology This paper introduces a real time monitoring system for batch gas manufacturing processes that offers a variety of tools that operators can understand and use without serious difficulties. The proposed integrated system covers the whole spectrum of monitoring and diagnosis that include data collection, monitoring and diagnosis. The developed system has been verified to be very effective for monitoring and diagnosis using its application to the construction of monitoring system for a typical industrial batch reactor.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.691-697
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• 2006

Induction motors are critical components in industrial process. So there are many research in the condition based maintenance, online monitoring system, and fault detection. This paper presents a scheme on the detection and diagnosis of the three-phase squirrel induction motor under unbalanced voltage, broken rotor bar, and a combination of these two faults. Actually one fault happen in operation, it influence other component in motor or cause another faults. Accordingly it is useful to diagnose and detect a combination fault in induction motor as well as each fault. The proposed fault detection and diagnosis algorithm is based on the stator currents from the squirrel induction motor and simulated with the aid of Matlab Simulink.

• Journal of the Korea Safety Management & Science
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• v.25 no.3
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• pp.55-62
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• 2023

Public institutions have a responsibility to ensure the safety of their employees and the public. One way to do this is to implement a systematic safety inspection system based on risk assessments and continuous improvements. This study developed a systematic safety inspection system for public institutions that are ordered construction projects. The proposed system in this study consists of a three-step process: (1) developing safety grade evaluation tables, (2) preparing and conducting safety inspections, and (3) evaluating and improving safety management grades. The first step is to develop safety grade evaluation tables by analysis and diagnosis of the construction site's work type, disaster statistics, and related laws. The second step is to conduct safety inspections using the developed evaluation tables. The third step is to determine the safety management grade based on the results of the safety inspection, and to improve risk factors found during the safety evaluation. The proposed system was implemented in highway construction projects carried out by public institutions. The results showed that the proposed system has two major effects: (1) reducing accident-related deaths and injuries, (2) improving safety management levels by continuous evaluation and improvement. The proposed system can be utilized in construction projects ordered by public institutions to improve the level of occupational safety and health.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.227-239
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• 1997

PLSR is a powerful multivariate statistical tool that has been successfully applied to the quantitative analyses of data in spectroscopy, chemistry, and industrial process control. Data in spectorscopy is represented by spectrum matrix measured in many wavelengths. Problems of many kinds of noise in data and itercorrelation between wavelengths are quite common in such data. PLSR utilizes whole data set measured in many wavelengths to the analysis, and handles such problems through data compression method. We investigated the PLSR theory, and applied this method to the data for spectroscopic diagnosis of Total Hemoglobin in whole blood.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.99-114
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• 2021

With the Fourth Industrial Revolution based on new technology, the semiconductor manufacturing industry researches various analysis methods such as detecting process abnormalities and predicting yield based on equipment sensor data generated in the manufacturing process. The semiconductor manufacturing process consists of hundreds of processes and thousands of measurement processes associated with them, each of which has properties that cannot be defined by chemical or physical equations. In the individual measurement process, the actual measurement ratio does not exceed 0.1% to 5% of the target product, and it cannot be kept constant for each measurement point. For this reason, efforts are being made to determine whether to manage by using equipment sensor data that can indirectly determine the normal state of each step of the process. In this study, the Functional Data Analysis (FDA) was proposed to define a process abnormality detection process based on equipment sensor data and compensate for the disadvantages of the currently applied statistics-based diagnosis method. Anomaly detection accuracy was compared using machine learning on actual field case data, and its effectiveness was verified.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.858-864
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• 2011

There is no standard model for a Stated Period Check and a Precise Safety Diagnosis to remove electric fire and shock in the university Lab and institute. Especially, the research for the Stated Period Check and the Precise Safety Diagnosis of the Lab related to electrical field is very weak currently, and it is very necessary to build a detail safety plan. This paper informs the specific standard guideline of the safety check list, method and equipment and it shows the way to evaluate safety grade too. This paper also provides the information of R&D process through the analysis of electrical safety check list of ordinary R&D Lab. It shows a new detail guideline to R&D Lab, and the new guideline removes existing problem and deliver the effective standard model to each R&D Lab. The standard model developed in this research adopts the clear guideline of each check list for the electrical environment of current R&D Lab. This standard model can be applied for every R&D Lab to detect routine safety check and detail safety check immediately. This Research will generally improve not only the effective safety check, but also the safety level for R&D Lab to prevent the electrical accidents.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.111-116
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• 2022

The causes of motor burnout include overload, phase loss, restraint, interlayer short circuit, winding ground fault, instantaneous overvoltage, and the rotor contacting the stator, leading to insulation breakdown, leading to breakdown or electrical accidents. Therefore, equipment failure causes not only loss due to cost required for equipment maintenance/repair, but also huge economic loss due to productivity decrease due to process stop because the process itself including the motor is stopped. The current level of technology for diagnosing motor failures uses vibration, heat, and power analysis methods, but there is a limit to analyzing the problems only after a considerable amount of time has passed according to the failure. Therefore, in this paper, a device and algorithm for measuring insulation resistance using DC AMP signal was applied to an industrial motor to solve this problem. And by following the insulation resistance state value, we propose a diagnosis of deterioration and failure of the motor that cannot be solved by the existing method.