• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2107-2119
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• 2022

Accurate remaining useful life (RUL) prediction for critical components of nuclear power equipment is an important way to realize aging management of nuclear power equipment. The electric gate valve is one of the most safety-critical and widely distributed mechanical equipment in nuclear power installations. However, the electric gate valve's extended service in nuclear installations causes aging and degradation induced by crack propagation and leakages. Hence, it is necessary to develop a robust RUL prediction method to evaluate its operating state. Although the particle filter(PF) algorithm and its variants can deal with this nonlinear problem effectively, they suffer from severe particle degeneracy and depletion, which leads to its sub-optimal performance. In this study, we combined the whale algorithm with regularized particle filtering(RPF) to rationalize the particle distribution before resampling, so as to solve the problem of particle degradation, and for valve RUL prediction. The valve's crack propagation is studied using the RPF approach, which takes the Paris Law as a condition function. The crack growth is observed and updated using the root-mean-square (RMS) signal collected from the acoustic emission sensor. At the same time, the proposed method is compared with other optimization algorithms, such as particle swarm optimization algorithm, and verified by the realistic valve aging experimental data. The conclusion shows that the proposed method can effectively predict and analyze the typical valve degradation patterns.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.12
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• pp.21-27
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• 2020

Lithium-ion batteries are the heart of energy-storing devices and electric vehicles. Owing to their superior qualities, such as high capacity and energy efficiency, they have become quite popular, resulting in an increased demand for failure/damage prevention and useable life maximization. To prevent failure in Lithium-ion batteries, improve their reliability, and ensure productivity, prognosticative measures such as condition monitoring through sensors, condition assessment for failure detection, and remaining useful life prediction through data-driven prognostics and health management approaches have become important topics for research. In this study, the residual useful life of Lithium-ion batteries was predicted using two efficient artificial recurrent neural networks-ong short-term memory (LSTM) and gated recurrent unit (GRU). The proposed approaches were compared for prognostics accuracy and cost-efficiency. It was determined that LSTM showed slightly higher accuracy, whereas GRUs have a computational advantage.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.1-11
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• 2021

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.1998-2008
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• 2020

Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.8
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• pp.1536-1544
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• 1992

A relatively simple prediction method is proposed for initially semicircular surface crack growth under axial loading. The method takes into account the difference in surface crack closure behavior at the depth point and at the surface intersection point, and also the relationship of crack closure for surface crack and through-thickness crack. The prediction method provides conservative estimation for fatigue life within factor of two, and the predicted crack geometry variations agree well with the observed results. As a result, the prediction method proposed here is considered to be useful for engineering application.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1355-1360
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• 2008

Stainless steel sheets are widely used as the structure material for the railroad cars and the commercial vehicles. These kinds structures used stainless steel sheets are commonly fabricated by using the gas welding. Gas welding is very important and useful technology in fabrication of an railroad car and vehicles structure. However fatigue strength of the gas welded joints is considerably lower than parent metal due to stress concentration at the weldment, fatigue strength evaluation of gas welded joints are very important to evaluate the reliability and durability of railroad cars and to establish a criterion of long life fatigue design. In this paper, ${\Delta}P-N_f$ curve were obtained by fatigue tests. Using these results, the accelerated life test (ALT) is conducted. From the experimental results, an acceleration model is derived and acceleration factors are estimated. So it is intended to obtain the useful information for the fatigue lifetime of plug and ring gas welded joints and data analysis by statistic reliability method, to save time and cost, and to develop optimum accelerated life prediction plans.

• Journal of Welding and Joining
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• v.28 no.1
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• pp.77-85
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• 2010

Stainless steel sheets are widely used as the structure material for the railroad cars and the commercial vehicles. These kinds structures used stainless steel sheets are commonly fabricated by using the gas welding. Gas welding is very important and useful technology in fabrication of a railroad car and vehicles structure.However fatigue strength of the gas welded joints is considerably lower than parent metal due to stress concentration at the weld, fatigue strength evaluation of gas welded joints are very important to evaluate the reliability and durability of railroad cars and to establish a criterion of long life fatigue design. In this paper, $({\Delta}{\sigma}_a)_R-N_f$ curve were obtained by fatigue tests. Using these results, the accelerated life test(ALT) was conducted. From the experimental results, an acceleration model was derived and acceleration factors are estimated. So it is intended to obtain the useful information for the fatigue lifetime of plug and ring gas welded joints and data analysis by statistic reliability method, to save time and cost, and to develop optimum accelerated life prediction plans.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.3
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• pp.1-8
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• 2011

Stainless steel sheets are widely used as the structure material for the railroad cars and the commercial vehicles. These kinds structures used stainless steel sheets are commonly fabricated by using the gas welding. Gas welding is very important and useful technology in fabrication of an railroad car and vehicles structure. However fatigue strength of the gas welded joints is considerably lower than parent metal due to stress concentration at the weldment, fatigue strength evaluation of gas welded joints are very important to evaluate the reliability and durability of railroad cars and to establish a criterion of long life fatigue design. In this paper, ${\Delta}-N_f$ curve were obtained by fatigue tests. Using these results, the accelerated life test (ALT) is conducted. From the experimental results, an acceleration model is derived and acceleration factors are estimated. So it is intended to obtain the useful information for the fatigue lifetime of plug and ring gas welded joints and data analysis by statistical reliability method, to save time and cost, and to develop optimum accelerated life prediction plans.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.18 no.1
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• pp.20-31
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• 2008

A respirator is useful to protect a worker from the harmful gases and vapors in the workplace, and the evaluation of respirator cartridge service life is important for the worker's health and safety. The performance of cartridge is effected by several factors such as concentration of gas and vapor, humidity, temperature, adsorbents and cartridge packing density. Adsorption model was applied to both sampling tube and respirator cartridge to predict the service life for organic vapors. The variables of the adsorption model were measured from the experiment with the sampling tube, and it was used to predict the service life of respirator cartridge. In the experiment, we used carbon tetrachloride as a organic vapor and activated carbon take out respirator cartridge as activated carbon. As a result, it was possible to predict the service life of respirator cartridge and predicted service life was quite correct. Breakthrough time decreased with increase of CCl4 concentration. In case of sampling tube, adsorbed amount of CCl4 was larger than respirator cartridge due to linear velocity. Also, rate constant of sampling tube was larger than respirator cartridge, because of, effect of flow rate, packing density. In the prediction of service life of respirator cartridge by using sampling tube, the time required for 50% contaminant breakthrough(${\tau}$) is more effective than the rate constant(k').