• International Journal of Reliability and Applications
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• v.13 no.2
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• pp.81-90
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• 2012

Engineering systems are usually repairable. The reliability of a repairable system can be represented by failure intensity function. A type of shape of failure intensity function is called a failure pattern. Reliability-Centred Maintenance (RCM) presents six typical failure patterns but its definition is unclear. It is an open issue how to recognize the failure pattern of repairable systems. This paper first discusses the problems of RCM with the notion of failure pattern; then presents the method for failure pattern recognition; and finally proposes a flexible failure intensity function model. The appropriateness of the model is illustrated by a real-world example.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.325-329
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• 1992

The development of automatic production system have required intelligent diagnostic and monitoring function to repair system failure and reduce production loss by the failure. In order to perform accurate functions of intelligent system, inference about total system failure and fault analysis due to each mechanical component failures are required. Also the solution about repair and maintenance can be suggested from these analysis results. Generally, bearing is a essential mechanical component in the machinery. The bearing failure is caused by lubricant system failure, metallurgical defficiency, mechanical condition(vibration overloading misalignment), environmental effect. This study described roller bearing fault train due to stress variation and metallurgical defficiency from lubricant failure by using FTA.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4522-4533
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• 2022

For severe accident assessment of reactor pressure vessel (RPV), it is important to develop an accurate model that can predict transient thermo-mechanical behavior of the RPV lower head under the given condition. The present study revisits the lower head failure with two- and three-dimensional finite element models. In particular, we aim to give clear insight regarding the effect of the three-dimensionality present in the distribution of the thickness and thermal load of the lower head. For a rigorous validation of the result, both the OLHF-1 and the OLHF-2 tests are considered in this study. The result suggests that the three-dimensional effect is not negligible as far as the failure location is concerned. The non-uniformity of the thickness distribution is found to affect the failure location and time. The thermal load, which may not be axisymmetric in general, has the most significant effect on the failure assessment. We also observe that the creep property can affect the global deformation of the lower head, depending on the applied mechanical load.

• Structural Engineering and Mechanics
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• v.61 no.1
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• pp.161-169
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• 2017

This study aimed to develop a simple and effective method to facilitate the experimental research on mechanical properties of masonry under biaxial compressive stress. A series of tests on full-scale brick masonry panels under biaxial compression have been performed in limited principal stress ratios oriented at various angles to the bed joints. Failure modes of tested panels were observed and failure features were analyzed to reveal the mechanical behavior of masonry under biaxial compression. Based on the experimental data, the failure curve in terms of two orthotropic principal stresses has been presented and the failure criterion of brick masonry in the form of the tensor polynomial has been established, which indicate that the anisotropy for masonry is closely related to the difference of applied stress as well as the orientation of bed joints. Further, compared with previous failure curves and criteria for masonry, it can be found that the relative strength of mortar and block has a considerable effect on the degree of anisotropy for masonry. The test results demonstrate the validity of the proposed experimental method for the approximation of masonry failure under biaxial compressive stress and provide valuable information used to establish experimentally based methodologies for the improvement of masonry failure criteria.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.1
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• pp.127-138
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• 1994

The application of fault tree technique to the analysis of compressor failure is considered. The techniques involve the decomposition of the system into a form of fault tree where certain basic events lead to a specified top event which signifies the total failure of the system. In this paper, fault trees are made by using fault train of screw type air compressor failure. The fault trees are used to obtain minimal cut sets from the modes of system failure and, hence the system failure rate for the top event can be calculated. The method of constructing fault trees and the subsequent estimation of reliability of the system is illustrated through compressor failure. It is proved that FTA is efficient to investigate the compressor failure modes and diagnose system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.631-638
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• 2015

In this study, semiquantitative failure mode, effects, and criticality analysis (FMECA) for the reliability analysis of a solid rocket propulsion system is performed. The semiquantitative FMECA is composed of failure mode and effects analysis (FMEA) and criticality analysis (CA). To perform FMECA, the structure of the solid rocket propulsion system is divided into 43 parts down to the component level, and FMEA is conducted at the design stage considering 137 potential failure modes. CA is then conducted for each failure mode, during which the criticality number is estimated using the failure rate databases. The results demonstrate the relationship between potential failure modes, causes, and effects, and their risk priorities are evaluated qualitatively. Additionally, several failure modes with higher criticality and severity values are selected for high-priority improvement.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2124-2135
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• 2006

In this paper, the reliability estimation of pipelines is performed by employing the probabilistic method, which accounts for the uncertainties in the load and resistance parameters of the limit state function. The FORM (first order reliability method) and the SORM (second order reliability method) are carried out to estimate the failure probability of pipeline utilizing the FAD (failure assessment diagram). And the reliability of pipeline is assessed by using this failure probability and analyzed in accordance with a target safety level. Furthermore, the MCS (Monte Carlo Simulation) is used to verify the results of the FORM and the SORM. It is noted that the failure probability increases with the increase of dent depth, gouge depth, operating pressure, outside radius, and the decrease of wall thickness. It is found that the FORM utilizing the FAD is a useful and is an efficient method to estimate the failure probability in the reliability assessment of a pipeline. Furthermore, the pipeline safety assessment technique with the deterministic procedure utilizing the FAD only is turned out more conservative than those obtained by using the probability theory together with the FAD. The probabilistic method such as the FORM, the SORM and the MCS can be used by most plant designers regarding the operating condition and design parameters.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.12
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• pp.1063-1071
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• 2008

This paper provides a failure pressure evaluation model for local wall-thinned elbows. In this study, parametric finite element analyses are performed on the elbows containing local wall-thinning defect at their intrados and extrados, and the failure pressures are obtained from the analysis results by applying a local failure criterion that was validated by real-scale pipe tests. An evaluation model including the effects of thinning depth, length, circumferential angle, thinning location, and elbow geometries on the failure pressure is derived based on the evaluated failure pressures. The proposed model agrees well with the results of finite element analyses and reasonably estimates the dependence of failure pressure on the wall-thinning dimensions and elbow geometries. Also, the comparison with experimental data demonstrates that the proposed evaluation model can accurately predict the failure pressure of local wall-thinned elbows.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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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.