• Journal of Aerospace System Engineering
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• v.16 no.6
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• pp.129-137
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• 2022

Recently, the technology of aircraft drivers has been transitioning from the existing hydraulically-focused mechanical system to an all-electric one due to the high precision and ease of maintenance of electric drivers. Consequently, the failure of an aircraft's electric motor can have fatal consequences. To ensure aircraft safety, efficient and timely fault diagnosis methods are required prompting the active pursuit of research into fault diagnosis technology. This paper introduces and analyses the failure types and failure diagnosis technology trends of permanent magnet synchronous motors among electric motors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.194-202
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• 2019

Reliability analysis of the components frequently starts with the data that manufacturer provides. If enough failure data are collected from the field operations, the reliability should be recomputed and updated on the basis of the field failure data. However, when the failure time record for a component contains only a few observations, all statistical methodologies are limited. In this case, where the failure records for multiple number of identical components are available, a valid alternative is combining all the data from each component into one data set with enough sample size and utilizing the useful information in the censored data. The ROK Navy has been operating multiple Patrol Killer Guided missiles (PKGs) for several years. The Korea Multi-Function Control Console (KMFCC) is one of key components in PKG combat system. The maintenance record for the KMFCC contains less than ten failure observations and a censored datum. This paper proposes a Bayesian approach with a Dirichlet mixture model to estimate failure time density for KMFCC. Trends test for each component record indicated that null hypothesis, that failure occurrence is renewal process, is not rejected. Since the KMFCCs have been functioning under different operating environment, the failure time distribution may be a composition of a number of unknown distributions, i.e. a mixture distribution, rather than a single distribution. The Dirichlet mixture model was coded as probabilistic programming in Python using PyMC3. Then Markov Chain Monte Carlo (MCMC) sampling technique employed in PyMC3 probabilistically estimated the parameters' posterior distribution through the Dirichlet mixture model. The simulation results revealed that the mixture models provide superior fits to the combined data set over single models.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.9
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• pp.1281-1286
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• 2010

Power plants have many components and equipment. It is difficult for operators to know the time of failure or the equipment that fails. Plants incur heavy economic losses due to unexpected failure. The equipment in power plants is constantly monitored by various sensors and instruments. However, prevention of failure is very difficult. Therefore, engineers are developing many types of failure-alarm systems that can detect the abnormal functioning of equipment. Such failure-alarm systems inform only about the abnormal functioning of equipment and do not indicate the cause of failure or the parts that have failed. In this study, we have developed a failure-prediction system that can provide details on the cause of trouble and the maintenance method.

• Journal of Positioning, Navigation, and Timing
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• v.7 no.3
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• pp.155-163
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• 2018

This study examined methods to enhance navigation performance and reduce the divergence of navigation solutions that may occur in the event of global positioning system (GPS) failure by integrating the GPS/inertial navigation system (INS) with the three-dimensional (3D) magnetic vector measurements of a magnetometer. A magnetic heading aiding method that employs a magnetometer has been widely used to enhance the heading performance in low-cost GPS/INS navigation systems with insufficient observability. However, in the case of GPS failure, wrong heading information may further accelerate the divergence of the navigation solution. In this study, a method of integrating the 3D magnetic vector measurements of a magnetometer is proposed as a countermeasure for the case where the GPS fails. As the proposed method does not require attitude information for integration unlike the existing magnetic heading aiding method, it is applicable even in case of GPS failure. In addition, the existing magnetic heading aiding method utilizes only one-dimensional information in the heading direction, whereas the proposed method uses the two-dimensional attitude information of the magnetic vector, thus improving the observability of the system. To confirm the effect of the proposed method, simulation was performed for the normal operation and failure situation of GPS. The result confirmed that the proposed method improved the accuracy of the navigation solution and reduced the divergence speed of the navigation solution in the case of GPS failure, as compared with that of the existing method.

• Nuclear Engineering and Technology
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• v.36 no.1
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• pp.112-120
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• 2004

The purpose of this paper is to analyze the piping failure frequency for the main feedwater system in domestic nuclear power plants(NPPs) for the application to an in-service inspection(ISI), leak before break(LBB) concept, aging management program(AMP), and probabilistic safety analysis(PSA). First, a database was developed for piping failure events in domestic NPPs, and 23 domestic piping failure events were collected. Among the 23 events, 12 locations of wall thinning due to flow accelerated corrosion(FAC) were identified in the main feedwater system in 4 domestic WH 3-loop NPPs. Two types of the piping failure frequency such as the damage frequency and rupture frequency were considered in this study. The damage frequency was calculated from both the plant population data and damage(s) including crack, wall thinning, leak, and/or rupture, while the rupture frequency was estimated by using both the well-known Jeffreys method and a new method considering the degradation due to FAC. The results showed that the damage frequencies based on the number of the base metal piping susceptible to FAC ranged from $1.26{\times}10^{-3}/cr.yr\;to\;3.91{\times}10^{-3}/cr.yr$ for the main feedwater system of domestic WH 3-loop NPPs. The rupture frequencies obtained from the Jeffreys method for the main feedwater system were $1.01{\times}10^{-2}/cr.yr\;and\;4.54{\times}10^{-3}/cr.yr$ for the domestic WH 3-loop NPPs and all the other domestic PWR NPPs respectively, while those from the new method considering the degradation were higher than those from the Jeffreys method by about an order of one.

• Proceedings of the KSR Conference
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• 1998.05a
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• pp.194-202
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• 1998

This paper shows two models of the duplex system having a fault tolerant system characteristic used in airplane and railway system. The architecture of these systems is based on Mc68000, and we designed the single system, single duplex system, dual system and dual duplex system to evaluate the system characteristic. We calculate the failure rate of components using MIL-SPEC-2l7F and evaluate the reliability, avaliability, safety and MTTF(Mean Time To Failure) of the designed systems by Markov model. We choose our system depending on the developing system characteristic.

• Journal of the Korean Society of Safety
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• v.18 no.1
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• pp.116-122
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• 2003

Monte Carlo method is one of the powerful engineering tools especially to solve the complex non-linear problems. The Monte Carlo method gives approximate solution to a variety of mathematical problems by performing statistical sampling experiments on a computer. One of the methods to predict the time dependent failure probability of one of the bridge components or the bridge system is a lifetime function. In this paper, FORTRAN program is developed to predict the failure probability of bridge components or bridge system by using both system reliability and lifetime function. Monte Carlo method is used to generate the parameters of the lifetime function. As a case study, the program is applied to the concrete-steel bridge to predict the failure probability.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.175-177
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• 1995

The sensor signal validation and failure detection system must be able to detect, isolate, and identify sensor degradation as well as provide a reconstruction of the measurements. In this study, this is accomplished by combining the neural network, the Generalized Consistency Checking(GCC), and the Sequential Probability Ratio Test(SPRT) method in a decision estimator module. The GCC method is a computationally efficient system for redundant sensors, while the SPRT provides the ability to make decisions based on the degradation history of a sensor. The methodology is also extended to the detection of noise degradation. The acceptability of the proposed method is demonstration by using the simulation data in safety injection accident of nuclear power plants. The results show that the signal validation and sensor failure detection system is able to detect and isolate a bias failure and noise type failures under transient conditions. And also, the system is able to provide the validated signal by reconstructing the measurement signals in the failure conditions considered.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.47-55
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• 2020

In this case study, the brake line failure of air over hydraulic(AOH) brake system is described. AOH brake system is applied to commercial vehicles between 5 to 8 tons. It consists of a hydraulic system using compressed air and operates the air master to form hydraulic pressure to transfer braking power to the wheels. When the brake lines of the system applied to vehicles with high load capacity are damaged, the braking force of one shaft is lost, and the braking distance increases rapidly, leading to a big accident. Failure of the brake line occurs due to various causes such as road surface fragmentation, corrosion of the line, and aged deterioration of air brake hose. The braking force could be decreased even when a very small break in the form of a pin-hole occurs. However, it is difficult to find a part where the thickness of the line is thin due to stone pecking or corrosion generated in the pin-hole formed on the brake line located under the lower part of the vehicle by the sensory evaluation or the conventional braking force test. Accordingly, it is necessary to analyze the condition and cause of the failure of the brake line more precisely when the accident investigation of the heavy vehicles, and also to examine the necessity of the advanced test for the aged brake line.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.28-34
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• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.