• Title/Summary/Keyword: Failure Detection Rate

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A Method of Failure Detection Rate Calculation for Setting up of Guided Missile Periodic Test and Application Case (유도탄 점검주기 설정을 위한 고장 탐지율 산출 방안 및 적용 사례)

  • Choi, In-Duck
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.42 no.2
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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.

The Comparative Software Reliability Model of Fault Detection Rate Based on S-shaped Model (S-분포형 결함 발생률을 고려한 NHPP 소프트웨어 신뢰성 모형에 관한 비교 연구)

  • Kim, Hee Cheul;Kim, Kyung-Soo
    • Convergence Security Journal
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    • v.13 no.1
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    • pp.3-10
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    • 2013
  • In this paper, reliability software model considering fault detection rate based on observations from the process of software product testing was studied. Adding new fault probability using the S-shaped distribution model that is widely used in the field of reliability problems presented. When correcting or modifying the software, finite failure non-homogeneous Poisson process model was used. In a software failure data analysis considering the time-dependent fault detection rate, the parameters estimation using maximum likelihood estimation of failure time data and reliability make out.

TRUNCATED SOFTWARE RELIABILITY GROWTH MODEL

  • Prince Williams, D.R.;Vivekanandan, P.
    • Journal of applied mathematics & informatics
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    • v.9 no.2
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    • pp.761-769
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    • 2002
  • Due to the large scale application of software systems, software reliability plays an important role in software developments. In this paper, a software reliability growth model (SRGM) is proposed. The testing time on the right is truncated in this model. The instantaneous failure rate, mean-value function, error detection rate, reliability of the software, estimation of parameters and the simple applications of this model are discussed .

A Study on the Reliability Growth Trend of Operational S/W Failure Reduction

  • Che, Gyu-Shik;Kim, Yong-Kyung
    • Proceedings of the Korea Society of Information Technology Applications Conference
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    • 2005.11a
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    • pp.143-146
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    • 2005
  • The software reliability growth depends on the testing time because the failure rate varies whether it is long or not. On the other hand, it might be difficult to reduce failure rate for most of the cases are not available for debugging during operational phase, hence, there are some literatures to study that the failure rate is uniform throughout the operational time. The failure rate reduces and the reliability grows with time regardless of debugging. As a result, the products reliability varies with the time duration of these products in point of customer view. The reason of this is that it accumulates the products experience, studies the exact operational method, and then finds and takes action against the fault circumstances. I propose the simple model to represent this status in this paper.

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Risk Evaluation in FMEA when the Failure Severity Depends on the Detection Time (FMEA에서 고장 심각도의 탐지시간에 따른 위험성 평가)

  • Jang, Hyeon Ae;Yun, Won Young;Kwon, Hyuck Moo
    • Journal of the Korean Society of Safety
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    • v.31 no.4
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    • pp.136-142
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    • 2016
  • The FMEA is a widely used technique to pre-evaluate and avoid risks due to potential failures for developing an improved design. The conventional FMEA does not consider the possible time gap between occurrence and detection of failure cause. When a failure cause is detected and corrected before the failure itself occurs, there will be no other effect except the correction cost. But, if its cause is detected after the failure actually occurs, its effects will become more severe depending on the duration of the uncorrected failure. Taking this situation into account, a risk metric is developed as an alternative to the RPN of the conventional FMEA. The severity of a failure effect is first modeled as linear and quadratic severity functions of undetected failure time duration. Assuming exponential probability distribution for occurrence and detection time of failures and causes, the expected severity is derived for each failure cause. A new risk metric REM is defined as the product of a failure cause occurrence rate and the expected severity of its corresponding failure. A numerical example and some discussions are provided for illustration.

Estimation for Failure Rate of Railway Power Facility and Determination of Maintenance Priority Order using Fuzzy Theory and Expert System (퍼지이론과 전문가 시스템을 이용한 철도 전력 설비의 고장률 평가와 유지보수 우선순위의 결정)

  • Lee, Yun-Seong;Kwon, Ki-Ryang;Kim, Jin-O;Kim, Hyung-Chul
    • Proceedings of the KSR Conference
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    • 2009.05a
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    • pp.495-504
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    • 2009
  • As the Reliability Centered Maintenance(RCM) is being studied, maintenance tasks can be performed effectively through the Risk Priority Number(RPN) evaluation about the components in the system. The RPN is usually calculated through arithmetical operations of three values, Severity, Occurrence, and Detection for each facility. This RPN provides information that includes risk level of the facility and the priority order of maintenance tasks for facility. However, if there is no sufficient historical failure data, it is difficult to calculate the RPN. In this case, historical failure data from other sources can be used and apply this data to korean railway system. In this paper, it is proposed that a new methodology to model the failure rate as a fuzzy membership function. This method is based on failure data from other sources by means of the fuzzy theory and the expert opinion system. And considering assessment tendency of each expert, distortions that happened when the failure rate of facilities is estimated were minimized. This results determine Occurrence values of facilities. Taking advantage of this result., the RPN can be calculated with Severity and Detection of facilities by using the fuzzy operation. The proposed method is applied the rail-way power substation.

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Comparisons of Control Charts for Failure Rate with Fixed Inspection Interval

  • Lee, Jae-Man;Chang, Duk-Joon
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.4
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    • pp.793-801
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    • 2004
  • In this paper, we propose control charts for failure rate using the number of failures based on the fixed interval inspection with replacement. And we investigate the power of detection of the proposed control charts by the ARL.

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Automatic Detection of Congestive Heart Failure and Atrial Fibrillation with Short RR Interval Time Series

  • Yoon, Kwon-Ha;Nam, Yunyoung;Thap, Tharoeun;Jeong, Changwon;Kim, Nam Ho;Ko, Joem Seok;Noh, Se-Eung;Lee, Jinseok
    • Journal of Electrical Engineering and Technology
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    • v.12 no.1
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    • pp.346-355
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    • 2017
  • Atrial fibrillation (AF) and Congestive heart failure (CHF) are increasingly widespread, costly, deadly diseases and are associated with significant morbidity and mortality. In this study, we analyzed three statistical methods for automatic detection of AF and CHF based on the randomness, variability and complexity of the heart beat interval, which is RRI time series. Specifically, we used short RRI time series with 16 beats and employed the normalized root mean square of successive RR differences (RMSSD), the sample entropy and the Shannon entropy. The detection performance was analyzed using four large well documented databases, namely the MIT-BIH Atrial fibrillation (n=23), the MIT-BIH Normal Sinus Rhythm (n=18), the BIDMC Congestive Heart Failure (n=13) and the Congestive Heart Failure RRI databases (n=25). Using thresholds by Receiver Operating Characteristic (ROC) curves, we found that the normalized RMSSD provided the highest accuracy. The overall sensitivity, specificity and accuracy for AF and CHF were 0.8649, 0.9331 and 0.9104, respectively. Regarding CHF detection, the detection rate of CHF (NYHA III-IV) was 0.9113 while CHF (NYHA I-II) was 0.7312, which shows that the detection rate of CHF with higher severity is higher than that of CHF with lower severity. For the clinical 24 hour data (n=42), the overall sensitivity, specificity and accuracy for AF and CHF were 0.8809, 0.9406 and 0.9108, respectively, using normalized RMSSD.

Sensor Failure Detection and Accommodation Based on Neural Networks (신경회로망을 이용한 센서 고장진단 및 극복)

  • 이균정;이봉기
    • Journal of the Korea Institute of Military Science and Technology
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    • v.1 no.1
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    • pp.82-91
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    • 1998
  • This paper presents a neural networks based approach for the problem of sensor failure detection and accommodation for ship without physical redundancy in the sensors. The designed model consists of two neural networks. The first neural network is responsible for the failure detection and the second neural network is responsible for the failure identification and accommodation. On the yaw rate sensor of ship, simulation results indicates that the proposed method can be useful as failure detector and sensor estimator.

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Measurements of Dark Area in Sensing RFID Transponders

  • Kang, J.H.;Kim, J.Y.
    • Journal of Sensor Science and Technology
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    • v.21 no.2
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    • pp.103-108
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    • 2012
  • Radiofrequency(RF) signal is a key medium to the most of the present wireless communication devices including RF identification devices(RFID) and smart sensors. However, the most critical barrier to overcome in RFID application is in the failure rate in detection. The most notable improvement in the detection was from the introduction of EPC Class1 Gen2 protocol, but the fundamental problems in the physical properties of the RF signal drew less attention. In this work, we focused on the physical properties of the RF signal in order to understand the failure rate by noting the existence of the ground planes and noise sources in the real environment. By using the mathematical computation software, Maple, we simulated the distribution of the electromagnetic field from a dipole antenna when ground planes exist. Calculations showed that the dark area can be formed by interference. We also constructed a test system to measure the failure rate in the detection of a RFID transponder. The test system was composed of a fixed RFID reader and an EPC Class1 Gen2 transponder which was attached to a scanner to sweep in the x-y plane. Labview software was used to control the x-y scanner and to acquire data. Tests in the laboratory environment showed that the dark area can be as much as 43 %. One who wants to use RFID and smart sensors should carefully consider the extent of the dark area.