• Journal of the Korean Society for Railway
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• v.12 no.6
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• pp.921-926
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• 2009

The RPN provides information which includes the risk level and the priority order of maintenance tasks for components. However, if there is no sufficient historical failure data, the historical failure data from other sources can be applied to the target system. And if we use historical data from other sources without any process, there will be concomitant problems according to a discord of each system characteristic, a difference between the present and the date of failure data, etc. In this paper, a new methodology is proposed to model the failure rate as a fuzzy function to resolve these problems. Taking advantage of this result, the RPN can be calculated by using the fuzzy operation. The proposed method is applied to the substation system.

• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.125-138
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• 2022

Purpose: Failure Mode and Effect Analysis (FMEA) is a widely utilized technique to measure product reliability by identifying potential failure modes. Even though FMEA techniques have been studied, the form of Risk Priority Number (RPN) used to evaluate risk priority in FMEA is still questionable because of its shortcomings. In this study, we suggest common RPN(cRPN) to resolve shortcomings of the traditional RPN and show the extensibility of cRPN. Methods: We suggest cRPN which is based on Cobb-Douglas production function, and represent the various application on weighting risk factors, weighted RPN in a mathematical way, and show the possibility of statistical approach. We also conduct numerical study to examine the difference of the traditional RPN and cRPN as well as the potential application from the analysis on marginal effects of each risk factor. Results: cRPN successfully integrates previously suggested approaches especially on the relative importance of risk factors and weighting RPN. Moreover, we analyze the effect of corrective actions in terms of econometric analysis using cRPN. Since cRPN is rely on the reliable mathematical model, there would be numerous applications using cRPN such as smart factory based on A.I. techniques. Conclusion: We propose a reliable mathematical model of RPN based on Cobb-Douglas production function. Our suggested model, cRPN, resolves various shortcomings such as consideration of the relative importance, the effect of combinations among risk factors. In addition, by adopting a reliable mathematical model, quantitative approaches are expected to be applied using cRPN. We find that cRPN can be utilized to the field of industry because it is able to be applied without modifying the entire systems or the conventional actions.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.97-108
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• 2019

Purpose: The purpose of this study is to propose a new Risk Priority Number(RPN) evaluation method which analyzes value of product functions by mining customer opinions in Social Network Service(SNS). Methods: A traditional RPN is measured by three evaluation standards (Severity, Occurrence, Detection) which are analyzed by manufacturing engineers and researchers. On the other hand, these standards are analyzed by customers' viewpoints through SNS opinion mining in this research. In order to extract customer feedbacks from textual data sets, the methodology in this paper implies natural language processing, hereby collecting product related data sets and analyzing the opinions automatically. An emotional polarity of an opinion indicates severity, while the number of negative opinion shows occurrence, and the entire number of customer opinion refers to detection. Results: The results of this study are as follows; As a result of the CRPN evaluation, it is confirmed that the features evaluated as risky are highly likely to be improved in the next series. Therefore, CRPN is an effective risk assessment model that reflects customer feedback. Conclusion: Reflecting customer feedback is a useful tool for risk assessment of the product as well as for developing new products and improving existing products.

• Journal of the Korean Society of Safety
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• v.30 no.5
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• pp.108-113
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• 2015

Plastic injection moulding machine is widely used for many industrial field. It is classified into mandatory safety certification machinery in Industrial Safety and Health Act because of its high hazard. In order to prevent industrial accidents by plastic injection moulding machine, it is necessary for designer to identify hazardous factors and assess the failure modes to mitigate them. This study tabulates the failure modes of main parts of plastic injection moulding machine and how their failure has affect on the machine being considered. Failure Mode & Effect Analysis(FMEA) method has been used to assess the hazard on plastic injection moulding machine. Risk and risk priority number(RPN) has been calculated in order to estimate the hazard of failures using severity, probability and detection. Accidents caused by plastic injection moulding machine is compared with the RPN which was estimated by main regions such as injection unit, clamping unit, hydraulic and system units to find out the most dangerous region. As the results, the order of RPN is injection unit, clamping unit, hydraulic unit and system units. Barrel is the most dangerous part in the plastic injection moulding machine.

• Journal of the Korean Society of Safety
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• v.31 no.2
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• pp.10-17
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• 2016

The purpose of this paper is to deduct components that are in the group of highest risk(top 10%). the group is conducted for classification into groups by values according to risk priority through risk priority number(RPN) of FMEA(Failure modes and effects analysis) sheet. Top 10% of failure mode among total potential failure modes(72 failure modes) of ESS included 5 BMS(battery included) failure modes, 1 invert failure mode, and 1 cable connectors failure mode in which BMS was highest. This is because ESS is connected to module, try, and lack in the battery part as an assembly of electronic information communication and is managed. BMS is mainly composed of the battery module and communication module. There is a junction box and numerous connectors that connect these two in which failure occurs most in the connector part and module itself. Finally, this paper proposes RPN by each step from the starting step of ESS design to installation and operation. Blackouts and electrical disasters can be prevented beforehand by managing and removing the deducted risk factors in prior.

• Journal of Korean Society for Quality Management
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• v.40 no.3
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• pp.306-315
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• 2012

Purpose: To ensure good quality munitions, we require quantitative risk management and optimal risk management of system characteristics. Methods: Failure mode and effects analysis (FMEA) is a widely used technique to assess or to improve reliability of products at early stage of design and development. Traditionally, the prioritization of failures for corrective actions is performed by developing a risk priority number (RPN). Results: This paper reestablishes an effective methodology for prioritization of failure modes in FMEA procedure. Revised evaluation criteria of RPN are devised. Conclusion: To verify the proposed methodology, it is applied to RPN evaluation for K21 infantry combat vehicle.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.495-504
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• 2009

As the Reliability Centered Maintenance(RCM) is being studied, maintenance tasks can be performed effectively through the Risk Priority Number(RPN) evaluation about the components in the system. The RPN is usually calculated through arithmetical operations of three values, Severity, Occurrence, and Detection for each facility. This RPN provides information that includes risk level of the facility and the priority order of maintenance tasks for facility. However, if there is no sufficient historical failure data, it is difficult to calculate the RPN. In this case, historical failure data from other sources can be used and apply this data to korean railway system. In this paper, it is proposed that a new methodology to model the failure rate as a fuzzy membership function. This method is based on failure data from other sources by means of the fuzzy theory and the expert opinion system. And considering assessment tendency of each expert, distortions that happened when the failure rate of facilities is estimated were minimized. This results determine Occurrence values of facilities. Taking advantage of this result., the RPN can be calculated with Severity and Detection of facilities by using the fuzzy operation. The proposed method is applied the rail-way power substation.

• Journal of Korean Society for Quality Management
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• v.42 no.1
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• pp.43-61
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• 2014

Purpose: This study identifies preventive measures for VOC management by analyzing the causes and effects of factors that contribute to high risk service failure using FMEA on KORAIL VOC data. Methods: Two research methods were used. First, a Risk Priority Number (RPN) was assigned to each KORAIL VOC based on Failure Mode and Effect Analysis (FMEA). Second, multiple regression analysis was run with RPN factors that include severity, occurrence, and detection as the independent variables and customer dissatisfaction as the dependent variable. Results: Multiple regression analysis showed that RPN factors including severity, occurrence, and detection had significantly positive relationship with customer dissatisfaction. Based on these results, an FMEA was performed on VOC categories with high RPN for railroad stations including platform, ticketing, ticket verification, parking, and escalator, and VOC categories with high RPN for trains including entrance doors, cafes, air quality, announcement, and ticket verification. Conclusion: This study has practical implications to service failure management. A priority order using FMEA was established for the list of customer dissatisfactions that should be addressed to actively manage service failure, and strategies for tackling this priority list are offered.

• Journal of the Korean Dietetic Association
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• v.21 no.1
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• pp.25-36
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• 2015

In this study, we attempted to improve hospital food delivery service quality and customer satisfaction by using FMEA (Failure Mode and Effect Analysis), which is applied to the quality control of products in manufacturing plants. Subjective food delivery service quality improvement was judged based on a 5-point likert scale. Traditional FMEA uses an RPN (Risk priority number) to evaluate the risk level of a component or process. The RPN index was determined by calculating the product of severity, occurrence, and detection indexes. In our results, total RPN value (P<0.01) significantly decreased after FMEA introduction, whereas customer satisfaction (P<0.001) and food delivery service quality (P<0.001) significantly increased. Specifically, foodservice errors (P<0.01) and loss cost (P<0.01) were significantly improved by FMEA introduction. Taken together, we suggest that FMEA reduces critical activities and errors in foodservice delivery caused by simple priority selection.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.379-391
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• 2015

Several risk assessment techniques have been presented and investigated in previous research, focusing mainly on the failure mode and effect analysis (FMEA). FMEA can be employed to determine where failures can occur within industrial systems and to assess the impact of such failures. This research proposes a novel methodology for hazard analysis and risk assessments that integrates FMEA with the bow-tie model. The proposed method has been applied and evaluated in a real industrial process, illustrating the effectiveness of the proposed method. Specifically, the bowtie diagram of the critical equipment in the adopted plant in the case study was built. Safety critical barriers are identified and each of these is assigned to industrial process with an individual responsible. The detection rating to the failure mode and the values of risk priority number (RPN) are calculated. The analysis shows the high values of RPN are 500 and 490 in this process. A global corrective actions are suggested to improve the RPN measure. Further managerial insights have been provided.