• 유공압시스템학회논문집
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• 제1권2호
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• pp.1-7
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• 2004

In-process diagnosis is essential to achieve predictive maintenance in industrial plants. An in- process diagnosis method was proposed for hydraulic servo systems, which was based upon leakage flow measurement. Leakage due to servovalve wear was analysed and modeled mathematically far computer simulation work. The key idea of diagnosis algorithm is that when monitoring signals, such as servovalve input and load displacement are in steady states, the return-line flow of hydraulic servo systems can be regarded as null-leakage of servovalve. Virtual experiments were performed to ensure effectiveness of the proposed diagnosis method.

• 산업경영시스템학회지
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• 제35권4호
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• pp.10-15
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• 2012

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is diagnosis of the spondylolisthesis from biomedical data that is derived from the shape and orientation of the pelvis and lumbar spine. The data set has six attributes including pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis and two class including normal and abnormal. From University of California at Irvine machine learning repository, 100 normal and 150 spondylolisthesis patient's data were used for this study. Mahalanobis Taguchi System (MTS) application process and the diagnosis results were described in this paper.

• 대한산업공학회지
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• 제43권2호
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• pp.100-111
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• 2017

Evaluating the performance of R&D Activity is complicated because it is hard to quantify the R&D results unlike the traditional manufacturing or service industries. Recently, to overcome this, process-focused evaluation methods applying the philosophy of quality into R&D environment have been introduced. However, these quality activities are mainly conducted without feedback system after the evaluation work is done. The aim of this study is to present a R&D quality diagnosis framework to obtain the improvement opportunities from R&D process perspective. The research is designed as follows : First, R&D standard process and R&D quality elements are derived from a literature review. Second, the diagnosis objects are obtained by investigating the R&D quality elements at each R&D steps. Third, a two-dimensional diagnosis model, which enables the objective measurement of the 'system compatibility' and 'accomplish level', is presented. The proposed method can provide an effective way to find opportunities for efficient quality improvement of R&D process.

• 대한전자공학회논문지
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• 제17권2호
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• pp.37-52
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• 1980

산업프로세스(industrial _Process)가 점차 복잡하여지고 자동화됨에 따라 계통(system)의 신뢰도를 높이고 인간의 한계능력을 해결하기 위하여 경보분석(alarm analysis)흑은 고장진단(fault diagnosis)의 필요성이 절실화 되어 가고 있다. 본 논문에서는 화학 반응기 (chemical reactor)의 고장진단을 위한 방법으로 시이퀸스 콤퓨터 프로그램밍(sequence computer programming )에 의한 "사전(dictionary)" 작성방법이 시도 되었고 실험을 통해 그 유용성이 입증 되었다. 그리고 점차 복잡되어가고 있는 경보 시스템(alarm system)을 단순화 시킬 수 있는 결과 시스템 설계에 대한 제안을 마련하였다.

• 한국신뢰성학회지:신뢰성응용연구
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• 제10권1호
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• pp.39-56
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• 2010

Statistical process control (SPC) is a powerful technique for monitoring, managing, analysing and improving the process performance. However, its has limitations such as lack of engineering, statistical skill and training, and lesser importance of activity. To solve the problems, this study proposes an intelligent SPC system using specified agents which are derived through analysis and evaluation of the SPC activities. The activities investigated by the relevant researches are categorized as collection, process analysis, diagnosis, detection, cause analysis and rule generation. Also, the evaluation criteria are established as feasibility of automation, frequency, level and time. The requirements of the agent functions are derived by the evaluation, and the types of customized agents are as data collection, store, analysis, diagnosis, monitoring, alarm and reporting. A prototype SPC system represents that the functions of the proposed agents are successfully validated.

• 대한안전경영과학회지
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• 제13권4호
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• pp.153-160
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• 2011

This thesis introduced a model of diagnosing a company's quality management, and a process of achieving quality innovation based on the model. As for study methods, books and theses related to quality and process innovation were collected for investigation, a survey on internal employees to investigate major issues of quality and standard consciousness was conducted, and 5DP was discussed for balanced process analysis.

• Industrial Engineering and Management Systems
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• 제8권4호
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• pp.247-256
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• 2009

In mass production industries such as steel making that have large equipment, sudden stops of production process due to machine failure can cause severe problems. To prevent such situations, machine diagnosis techniques play important roles. Many methods have been developed focusing on this subject. In this paper, we propose a method for the early detection of the failure on rotating machine, which is the most common theme in the machine failure detection field. A simplified method of calculating autocorrelation function is introduced and is utilized for ARMA model identification. Furthermore, an absolute deterioration factor such as Bicoherence is introduced. Machine diagnosis can be executed by this simplified calculation method of system parameter distance with weight. Proposed method proved to be a practical index for machine diagnosis by numerical examples.

• 한국가스학회지
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• 제4권4호
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• pp.42-49
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• 2000

대부분의 화학공정은 매우 크고 복잡하기 때문에 전체 공정에 대한 진단시스템을 만드는 것은 매우 어렵다. 따라서, 대형공정을 몇 개의 부공정으로 분할하여 진단하는 체계적인 방법이 필요하다. 이 논문에서는 이상-결과 트리모델에 기반하여 정성적 이상진단을 위한 공정분할전략을 제안하였다. 분할기준으로 유연한 진단, 지식베이스의 크기축소, 및 복잡한 지식베이스의 일관된 구축을 사용하였다 부공정간의 인과관계를 연결하기 위해 통로변수를 도입한 다음 오프라인 분석을 통해 통로변수의 이상-결과 트리모델을 구축하였다 계분할이 없는 경우와 같은 진단결과를 얻을 수 있도록 온라인 진단전략을 수립하였다 제안된 방법의 유용성을 대형 보일러 공정에 대한 이상진단시스템을 통해 보였다.

• KEPCO Journal on Electric Power and Energy
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• 제4권2호
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• 드라이브 ㆍ 컨트롤
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• 제18권1호
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• pp.24-30
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• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.