• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.151-160
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• 2022

The total system is composed of the main system (MS) and the failure detection equipment (FDE) which detects failures of MS. The analysis of system reliability is performed when the failure of FDE is possible. Several repair policies are considered to determine the order of repair of failed systems, which are sequential repair (SQ), priority repair (PR), independent repair (ID), and simultaneous repair (SM). The states of MS-FDE systems are represented by Markov models according to repair policies and the main purpose of this paper is to derive the system availabilities of the Markov models. Analytical solutions of the stationary equations are derived for the Markov models and the system availabilities are immediately determined using the stationary solutions. A simple illustrative example is discussed for the comparison of availability values of the repair policies considered in this paper.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.57-64
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• 2024

In the industrial field, robots are used to increase productivity by replacing labors with dangerous, difficult, and hard tasks. However, failures of individual industrial robots in the entire production process may cause product defects or malfunctions, and may cause dangerous disasters in the case of manufacturing parts used in automobiles and aircrafts. Although requirements for early diagnosis of industrial robot failures are steadily increasing, there are many limitations in early detection. This paper introduces methods for diagnosing robot failures using sound-based data and deep learning. This paper also analyzes, compares, and evaluates the performance of failure diagnosis using various deep learning technologies. Furthermore, in order to improve the performance of the fault diagnosis system using deep learning technology, we propose a method to increase the accuracy of fault diagnosis based on an inference window. When adopting the inference window of deep learning, the accuracy of the failure diagnosis was increased up to 94%.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8387-8390
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• 2016

Background: Treatment of biochemical failure after radical prostatectomy for prostate cancer is largely empirically based. The use of PSA kinetics has been used as a guide to determine local or systemic treatment of biochemical failure. We here compared PSA kinetics with detection of bone marrow micrometastasis as methods to determine local or systemic relapse. Materials and Methods: A transversal study was conducted of men with biochemical failure, defined as a serum PSA >0.2ng/ml after radical prostatectomy. Consecutive patients having undergone radical prostatectomy and with biochemical failure were enrolled and clinical and pathological details were recorded. Bone marrow biopsies were obtained from the iliac crest and touch prints made, micrometastasis (mM) being detected using anti-PSA. The clinical parameters of total serum PSA, PSA velocity, PSA doubling time and time to biochemical failure, age, Gleason score and pathological stage were registered. Results: A total of 147 men, mean age $71.6{\pm}8.2years$, with a median time to biochemical failure of 5.5 years (IQR 1.0-6.3 years) participated in the study. Bone marrow samples were positive for micrometastasis in 98/147 (67%) of patients at the time of biochemical failure. The results of bone marrow micrometastasis detected by immunocytochemistry were not concordant with local relapse as defined by PSA velocity, time to biochemical failure or Gleason score. In men with a PSA doubling time of < six months or a total serum PSA of >2,5ng/ml at the time of biochemical failure the detection of bone marrow micrometastasis was significantly higher. Conclusions: The detection of bone marrow micrometastasis could be useful in defining systemic relapse, this minimally invasive procedure warranting further studies with a larger group of patients.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.9
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• pp.678-684
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• 1987

The identification problem of instrument faults in PWR pressurizer is considered. The instrument failure detection technique in this paper consists of two filters, a normal-mode Kalman filter which estimates plant states in normal operation and a bias estimator which estimates the magnitudes and directions of bias faults. The concept of threshold based on the residual of a Kalman filter in normal operation is introduced. The bias estimator is driven when the absolute value of residual exceeds the threshold. The suggested failure detection algorithm is applied to a PWR pressurizer. Computer simulations show that the prompt detection of bias fault can be performed very successfully when there exist instrument faults.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.36-41
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• 2008

Fault detection and diagnosis(FDD) system is beneficial in equipment management by providing the operator with tools which can help find out a failure of the system. An experimental study has been performed on fault detection and diagnosis method for a water chiller. Bayes classifier, which is one of classical pattern classifiers, is adopted in deciding whether fault occurred or not. FDD algorithm can detect refrigerant leak failure, when 20% amount of charged refrigerant for normal operation leaks from the water chiller. The refrigerant leak failure caused COP reduction by 6.7% compared with normal operation performance. When two kinds of faults, such as a decrease in the mass flow rate of cooling water and temperature sensor fault of cooling water inlet, are detected, COP is a little decreased by these faults.

• 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.

• Journal of the Korean Society of Safety
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• v.33 no.3
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• pp.83-91
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• 2018

This paper suggests a weibull time delay model to evaluate failure risks in FMEA(failure modes and effects analysis). Assuming three types of loss functions for delayed time in failure cause detection, the risk of each failure cause is evaluated as its occurring frequency and expected loss. Since the closed form solution of the risk metric cannot be obtained, a statistical computer software R program is used for numerical calculation. When the occurrence and detection times have a common shape parameter, though, some simple results of mathematical derivation are also available. As an enormous quantity of field data becomes available under recent progress of data acquisition system, the proposed risk metric will provide a more practical and reasonable tool for evaluating the risks of failure causes in FMEA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11B
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• pp.900-910
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• 2004

Route failure is mainly caused by mobility of mobile host in ad hoc networks. Route failure, which may lead to sudden packet losses and delays, is losing the route from source to destination. In this situation, TCP assumes that congestion has occurred within the network and also initiates the congestion control procedures. Congestion control algorithm provides the means for the source to deal with lost packets. TCP performance in ad hoc environments will be degraded as TCP source cannot distinguish congestion from route failure. In this paper, we propose TCP-P as pre-detection approach to deal with route failure. TCP-P freezes TCP through pre-detection of route failure. Route failure information of the proposed mechanism is obtained not by routing protocol but by MAC protocol. The intermediated node, obtaining route failure information by its MAC layer, relays the information to TCP source and lets TCP source stop the congestion control algorithm. Results reveal that TCP-P responding with proactive manner outperforms other approaches in terms of communication throughput under the presence of node mobility.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.1063-1066
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• 2002

The wavelet transform is a popular tool for studying intermittent and localized phenomena in signals. In this study the wavelet transform of cutting force signals was conducted for the detection of a tool failure in turning process. We used the Daubechies wavelet analyzing function to detect a sudden change in cutting signal level. A preliminary stepped workpiece which had intentionally a hard condition was cut by the inserted cermet tool and a tool dynamometer obtained cutting force signals. From the results of the wavelet transform, the obtained signals were divided into approximation terms and detailed terms. At tool failure, the approximation signals were suddenly increased and the detailed signals were extremely oscillated just before tool failure.