• 산업경영시스템학회지
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• 제42권2호
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• pp.28-35
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• 2019

Since guided missiles with the characteristics of the one-shot system remain stored throughout their entire life cycle, it is important to maintain their storage reliability until the launch. As part of maintaining storage reliability, period of preventive test is set up to perform preventive periodic test, in this case failure detection rate has a great effect on setting up period of preventive test to maintain storage reliability. The proposed method utilizes failure rate predicted by the software on the basis of MIL-HDBK-217F and failure mode analyzed through FMEA (Failure Mode and Effect Analysis) using data generated from the actual field. The failure detection rate of using the proposed method is applied to set periodic test of the actual guided missile. The proposed method in this paper has advantages in accuracy and objectivity because it utilizes a large amount of data generated in the actual field.

• Nuclear Engineering and Technology
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• 제54권6호
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• pp.2031-2036
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• 2022

Energetic particle strikes the device and induces data corruption in the configuration memory (CRAM), causing errors and even malfunctions in a system on chip (SoC). Software-based fault injection is a convenient way to assess device performance. In this paper, dynamic partial reconfiguration (DPR) is adopted to make fault injection on a Xilinx 16 nm FinFET Ultrascale+ MPSoC. And the reconfiguration module implements the Sobel and Gaussian image filtering, respectively. Fault injections are executed on the static and reconfiguration modules' bitstreams, respectively. Another contribution is that the failure modes and effects analysis (FMEA) method is applied to evaluate the system reliability, according to the obtained injection results. This paper proposes a software-based solution to estimate programmable device vulnerability.

• 시스템엔지니어링학술지
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• 제2권1호
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• pp.31-36
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• 2006

Development of launch vehicle needs a large-scale and complicated System Engineering discipline interfacing to small-quantity production with special manufacturing processes. In general, the System Engineering discipline of launch vehicle has its relationship with Production, Operations, Product Assurance and Management disciplines and its internal partitions into the functions of System Engineering Integration & Control, Requirements Engineering, Analysis, Design and Configuration and Verification. As a function of Product Assurance, reliability of launch vehicle plays an significant role in risk management, system safety, flight safety and launch certification through design assurance. Moreover, major functions of systems engineering are integrated by means of reliability in the phases of design and verification. Therefore, derailed identification of system engineering interfaces of reliability, and execution of tasks for reliability assurance is required for successful development of launch vehicle. This paper identifies specific pattern and mechanism of the interfaces between reliability and system engineering.

• 산업경영시스템학회지
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• 제24권68호
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• pp.85-94
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• 2001

Most of customers want to know how to develop the computer system they want to get to use according to the their requirements. This study presents a Computer Integration management System for Quality (CISQ) model for system-integration organizations, which need to demonstrate their capability to consistently provide computer integrated system that meets customer satisfaction and applicable regulatory requirements. The Plan.Do.Check.Act(PDCA) cycle called by Doming wheel expresses the basic concept of continuous improvement action in order to emphasize on achieving business goal. It is useful for providing full competence of a system-integration organization to integrate management systems based on the international management system, and to take an advantage in its market. This study specifies International Certification Network Business Excellence Concept(IBEC) approach to ensure a harmonized integration of the variety of management systems and thereby produce synergy effect. The end part of this study specifies a Assessment Model including an assessment concept adding to the compliance audit according to the CISQ for the continuous improvement. A simple application on Failure Modes and Effects Analysis(FMEA) in testing phase in project life cycle has been done.

• 대한안전경영과학회지
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• 제2권4호
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• pp.91-102
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• 2000

Nowadays, every kind of system is changed so complex and enormous, it is necessary to assure system reliability, product liability and safety. Fault tree analysis(FTA) is a reliability/safety design analysis technique which starts from consideration of system failure effect, referred to as “top event”, and proceeds by determining how these can be caused by single or combined lower level failures or events. So in fault tree analysis, it is important to find the combination of events which affect system failure. Minimal cut sets(MCS) and minimal path sets(MPS) are used in this process. FTA-I computer program is developed which calculates MCS and MPS in terms of Gw-Basic computer language considering Fussell's algorithm. FTA-II computer program which analyzes importance and function cost of VE consists. of five programs as follows : (l) Structural importance of basic event, (2) Structural probability importance of basic event, (3) Structural criticality importance of basic event, (4) Cost-Failure importance of basic event, (5) VE function cost analysis for importance of basic event. In this study, a method of initiation such as failure, function and cost in FTA is suggested, and especially the priority rank which is calculated by computer-aided decision analysis program developed in this study can be used in decision making determining the most important basic event under various conditions. Also the priority rank can be available for the case which selects system component in FMEA analysis.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.169-177
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• 2021

Recently, the use of electric and electronic control systems is increasing in the automobile industry. This increase in the electric and electronic control system greatly increases the complexity of designing a vehicle, which leads to an increase in the malfunction of the system, and a safety problem due to the malfunction is becoming an issue. Based on IEC 61508 relating to the functional safety of electrical/electronic/programmable electronics, the ISO 26262 standard specific to the automotive sector was first established in 2011, and a revision was published in 2018. Malfunctions due to system failure are covered by ISO 26262, but ISO/PAS 21448 is proposed to deal with unintended malfunctions caused by changes in the surrounding environment. ISO 26262 sets out safety-related requirements for the entire life cycle. Functional safety analysis includes FTA (Fault Tree Analysis), FMEA (Failure Mode and Effect Analysis), and HAZOP (Hazard and Operability). These analysis have limitations in dealing with failures or errors caused by complex interrelationships because it is assumed that a failure or error affecting the risk occurs by a specific component. In order to overcome this limitation, it is necessary to apply the STPA (System Theoretic Process Analysis) technique.

• 한국기계가공학회지
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• 제18권9호
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• pp.52-57
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• 2019

The key technology of the fourth industrial revolution is artificial intelligence and machine learning. In this study, FMEA was performed on fuel pumps used as key items in most systems to identify major failure components, and artificial neural networks were built using big data. The main failure mode of the fuel pump identified by the test was coil damage due to overheating. Based on the artificial neural network built, machine learning was conducted to predict the failure and the mean error rate was 4.9% when the number of hidden nodes in the artificial neural network was three and the temperature increased to $140^{\circ}C$ rapidly.

• 한국기계가공학회지
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• 제19권9호
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• pp.47-52
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• 2020

With the 4th industrial revolution, condition monitoring using machine learning techniques has become popular among researchers. An overload due to complex operations causes several irregularities in MOSFETs. This study investigated the acquired voltage to analyze the overcurrent effects on MOSFETs using a failure mode effect analysis (FMEA). The results indicated that the voltage pattern changes greatly when the current is beyond the threshold value. Several features were extracted from the collected voltage signals that indicate the health state of a switched-mode power supply (SMPS). Then, the data were reduced to a smaller sample space by using a principal component analysis (PCA). A robust machine learning algorithm, the support vector machine (SVM), was used to classify different health states of an SMPS, and the classification results are presented for different parameters. An SVM approach assisted by a PCA algorithm provides a strong fault diagnosis framework for an SMPS.

• 대한조선학회논문집
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• 제47권1호
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• pp.76-87
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• 2010

According to the new rules and regulations (New SOLAS), major safety critical systems are to be designed to be redundant, which is called 'Redundancy Design'. This paper was to quantitatively analyze the degree of influence of the redundancy design applied to major safety critical systems using IMO's FSA(formal Safety Assessment) method. For the purpose of this study, the diesel engine system, which is actually one of major safety critical systems, was dealt with FMEA, FTA and ETA technique. In addition, whether the redundancy was met or not was verified and the degree of safety, or redundancy, was represented in terms of reliability. In conclusion, the safety of propulsion systems is possibly assessed systematically by estimating the risk level in terms of frequency and fatality.

• 한국기계가공학회지
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• 제20권12호
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• pp.85-91
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• 2021

In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.