• Transactions of the Korean hydrogen and new energy society
• /
• v.31 no.1
• /
• pp.73-81
• /
• 2020

For the fault tree analysis (FTA) analysis of the packaged hydrogen filling station, the composition of the charging station was analyzed and the fault tree (FT) diagram was prepared. FT diagrams were created by dividing the causes of events into external factors and internal factors with the hydrogen event as the top event. The external factors include the effects of major disasters caused by natural disasters and external factors as OR gates. Internal factors are divided into tube tailer, compressor & storage tank, and dispenser, which are composed of mistakes in operation process and causes of accidents caused by parts leakage. In this study, the purpose was to improve the hydrogen station. The subjects of this study were domestic packaged hydrogen stations and FTA study was conducted based on the previous studies, failure mode & effect analysis (FMEA) and hazard & operability study (HAZOP). Top event as a hydrogen leaking event and constructed the flow of events based on the previous study. Refer to "Off shore and onshore reliability data 6th edition", "European Industry Reliability Data Bank", technique for human error rate prediction (THERP) for reliability data. We hope that this study will help to improve the safety and activation of the hydrogen station.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.19 no.9
• /
• pp.47-52
• /
• 2020

With the 4th industrial revolution, condition monitoring using machine learning techniques has become popular among researchers. An overload due to complex operations causes several irregularities in MOSFETs. This study investigated the acquired voltage to analyze the overcurrent effects on MOSFETs using a failure mode effect analysis (FMEA). The results indicated that the voltage pattern changes greatly when the current is beyond the threshold value. Several features were extracted from the collected voltage signals that indicate the health state of a switched-mode power supply (SMPS). Then, the data were reduced to a smaller sample space by using a principal component analysis (PCA). A robust machine learning algorithm, the support vector machine (SVM), was used to classify different health states of an SMPS, and the classification results are presented for different parameters. An SVM approach assisted by a PCA algorithm provides a strong fault diagnosis framework for an SMPS.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.18 no.9
• /
• pp.52-57
• /
• 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.

• Journal of the Korean Society of Industry Convergence
• /
• v.24 no.5
• /
• pp.537-546
• /
• 2021

Remanufacturing is one of the most important resource recycling technology in response to resource depletion and environmental pollution. Domestic remanufacturing industry don't invigorate compared to other advanced countries because of low price and reliability of remanufactured product. In this study, remanufactured hydraulic pump and solenoid valve were evaluated durability by accelerated life test. In order that standard remanufacturing process was developed by core analysis and failure mode and effect analysis. And cores were remanufactured by standard remanufacturing process. For accelerated life test, the evaluation item and criteria were deduced by results of FMEA, reliability standards and enterprise interior criteria. To evaluate durability of remanufactured product, the remanufactured hydraulic pump and solenoid valve were evaluated performance after accelerated life test and the results were satisfied with criteria. This study showed that remanufactured products have a similar level of durability to new products by definition of remanufacturing.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.12
• /
• pp.85-91
• /
• 2021

In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.

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

• International Journal of Internet, Broadcasting and Communication
• /
• v.13 no.3
• /
• pp.169-177
• /
• 2021

Recently, the use of electric and electronic control systems is increasing in the automobile industry. This increase in the electric and electronic control system greatly increases the complexity of designing a vehicle, which leads to an increase in the malfunction of the system, and a safety problem due to the malfunction is becoming an issue. Based on IEC 61508 relating to the functional safety of electrical/electronic/programmable electronics, the ISO 26262 standard specific to the automotive sector was first established in 2011, and a revision was published in 2018. Malfunctions due to system failure are covered by ISO 26262, but ISO/PAS 21448 is proposed to deal with unintended malfunctions caused by changes in the surrounding environment. ISO 26262 sets out safety-related requirements for the entire life cycle. Functional safety analysis includes FTA (Fault Tree Analysis), FMEA (Failure Mode and Effect Analysis), and HAZOP (Hazard and Operability). These analysis have limitations in dealing with failures or errors caused by complex interrelationships because it is assumed that a failure or error affecting the risk occurs by a specific component. In order to overcome this limitation, it is necessary to apply the STPA (System Theoretic Process Analysis) technique.

• The KIPS Transactions:PartD
• /
• v.13D no.3 s.106
• /
• pp.415-424
• /
• 2006

There are increasing numbers of foreign and domestic organizations that are using CMM/CMMI to establish their processes and keep improving them. CMMI and IDEAL models of SEI provide the best practices of processes and guide the organization using them based on processes maturity levels. However, they do not deal with their tools or methods that describe how to implement the processes in the organization. Therefore, in this paper, we developed a method in which various tools and statistical methodology of 6 sigma are applied to identify the process areas to be improved, to extract problems in those areas and to prioritize them. We expect this paper can contribute to the organizations that are searching for practical way of implementing CMMI based software process improvement and of identifying improvement items systematically. Also this method will be used to understand the result of improvement activities quantitatively.

• Journal of the Korean Society of Industry Convergence
• /
• v.26 no.1
• /
• pp.95-103
• /
• 2023

The domestic remanufacturing industry is concentrated in auto parts, so it is necessary to expand into various industries. In the domestic aluminum industry, the extrusion process accounts for more than 40% of the total, but the old and management of the extrusion equipment is not done properly. In particular, the extruder has a structure in which equipment is not replaced until major parts are damaged or worn, so there are problems such as lower process precision, productivity and production efficiency compared to new equipment, and high maintenance costs. In this study, the old extruder was remanufactured for major high-risk parts through Failure Mode and Effect Analysis(FMEA), and the process level and performance of the extruder were evaluated before and after remanufacturing. Compared to the existing extruder, the standard deviation of the remanufacture extruder was reduced by 93.5%, 57.9%, and 70.0%, respectively, in major process control items such as container temperature, billet temperature, and ram speed, keeping performance constant. In addition, it was possible to produce products with complex shapes that could not be produced before due to problems such as dimensional deviation within tolerances. In this study, remanufacturing guidelines were presented by analyzing the effect of failure modes of the old extruder, and the performance improvement of the extruder was confirmed.

• Journal of the Korean Society of Radiology
• /
• v.11 no.7
• /
• pp.627-635
• /
• 2017

Brachytherapy is generally performed in conjunction with external radiation therapy, and the treatment course is very complicated, which can lead to radiation accidents. In order to solve this problem, we designed the process map by applying the failure mode and effects analysis (FMEA) method to the Brachytherapy and scored the risk priority number (RPN) for each treatment course based on this process map. The process map consisted of five steps, Patient consulting", "Brachytherapy simulation", "CT simulation", "Brachytherapy treatment planning" and "Treatment". In order to calculate the RPN, doctor, medical physicist, dose planners, therapist, and nurse participated in the study and evaluated occurrence, severity, and lack of detectability at each detail step. Overall, the process map is preceded by a patient identification procedure at each treatment stage, which can be mistaken for another patient, and a different treatment plan may be established to cause a radiation accident. As a result of evaluating the RPN for the detailed steps based on the process map, overall "Patient consulting" and "Brachytherapy treatment planning" step were evaluated as high risk. The nurses showed a tendency to be different from each other, and the nurses had a risk of 55 points or more for all the procedures except "Treatment", and the "Brachytherapy simulation" step was the highest with 88.8 points. Since the treatment stage differs somewhat for each medical institution performing radiotherapy, it is thought that the risk management should be performed intensively by preparing the process map for each institution and calculating the risk RPN.