A New Tool for Prioritising the Risk of Failure Modes for Marine Machinery Systems

Author(s):  
Ikuobase Emovon ◽  
Rosemary A. Norman ◽  
Alan J. Murphy

Failure Mode Effect and Analysis (FMEA) is one of the most powerful techniques for performing risk analysis for marine machinery systems, with risk being quantified through evaluating Risk Priority Numbers (RPNs) for all failure modes of the systems. The RPN is traditionally evaluated as the product of three risk criteria; occurrence (O), severity (S) and Detection (D). FMEA has several limitations such as the challenge of aggregating experts’ risk criteria rating that may be imprecise or incomplete. In this paper some of the limitations in the conventional FMEA technique are addressed using two approaches; AVeraging technique integrated with conventional Risk Priority Number (AVRPN) and AVeraging technique integrated with TOPSIS (AVTOPSIS). Both proposed techniques use a novel approach simple average in aggregating imprecise experts’ risk criteria ratings. A case study illustrates the suitability of both techniques for use in risk prioritisation jointly or independently as the results generated by both techniques are very similar. Furthermore, the AVRPN technique has been applied to an example from the literature and it has been demonstrated to be both computationally simple and capable of producing results which almost completely match those generated by modified Dempster-Shafer evidence theory techniques.

Author(s):  
Zuber Mujeeb Shaikh

Failure Mode and Effects Analysis (FMEA) is the process of reviewing as many components, assemblies, and subsystems as possible to identify potential failure modes in a system and their causes and effects. The study revealed that the Risk Priority Number (RPN) was initially 450 and it has decreased to 90 after implementing all the actions in FMEA.


2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Hang Zhou ◽  
Yuan-Jian Yang ◽  
Hong-Zhong Huang ◽  
Yan-Feng Li ◽  
Jinhua Mi

Abstract Due to the epistemic uncertainty, it is difficult for the experts to give precise parameter values in Risk Priority Number (RPN) evaluations. To overcome this drawback, a hybrid method is proposed by integrating the concepts of fuzzy set theory, weight analysis and similarity value measure of fuzzy numbers. The analysis process is divided into two phases to identify the hazard source. The first phase uses fuzzy Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA), then the main potential failure modes can be determined. The importance analysis of basic events can be calculated using fuzzy set theory and weight analysis. In the second phase, the multiple failure modes and component correlations are modelled using the Fuzzy Risk Priority Number (FRPN) evaluation and the Similarity Measure Value Method (SMVM). The proposed method has been applied to the risk analysis of a satellite propulsion system to show the effectiveness and applicability.


Author(s):  
Antônio Fernandes Costa Lima ◽  
Amanda Saba ◽  
Simone Berger ◽  
Silvia Sauaia Bianchini ◽  
Fernando Tobal Berssaneti

ABSTRACT This theoretical and reflexive study analyzed the risks related to the maintenance of patency of the Peripherally Inserted Central Catheter with the use of saline solution in comparison with saline-filled syringes, through the application of the Healthcare Failure Mode and Effect Analysis - HFMEA. The process was mapped, detailing the failure modes of each step. For the calculation of the Risk Priority Number, the severity and probability of the failure modes were analyzed. This analysis gave rise to the severity and probability matrix. Finally, actions to reduce the failure modes in the maintenance of patency were proposed, considering the use of saline-filled syringes in comparison to the use of saline ampoules. It was verified that the use of saline ampoules is associated with a greater risk, since it requires four stages more than saline-filled syringe does not, increasing the risk of contamination and the level of three different risks, which would result in additional hospital costs. The use of the saline-filled syringe would avoid risks that could negatively affect the patient’s health, the nursing professional and the health institution.


2018 ◽  
Vol 154 ◽  
pp. 01089
Author(s):  
Sri Indrawati ◽  
Kharina Novia Karunia Ningtyas ◽  
Alfina Budi Khoirani ◽  
Riadho Clara Shinta

Currently, electricity becomes basic needs for human’s life sustainability. Most of activities require electricity. Some power plant are demanded to be able to fulfil above necessity by distributing electricity as it required within time. Therefore, to accommodate good performance, it needs assessment on risk analysis, specifically at the warehousing division. A risk analysis is needed for assuring a good performance warehouse. A Modified FMEA method is used to analyse the risk. This method id done by identifying sources and root causes of a problem based on the value of risk priority number (RPN). The research is conducted in an Indonesian power plant, located in West Java. There are 10 types of failure modes. The result shows that the failure mode priority is inventory discrepancies. There are no difference ranking on the most impacted failure to be prioritized using FMEA and modified FMEA method.


2019 ◽  
Vol 27 (2) ◽  
pp. 144-154 ◽  
Author(s):  
Sahar AL Mashaqbeh ◽  
Jose Eduardo Munive-Hernandez ◽  
Mohammed Khurshid Khan

Failure modes and effect analysis (FMEA) is a proactive, highly structured and systematic approach for failure analysis. It has been also applied as a risk assessment tool, by ranking potential risks based on the estimation of risk priority numbers (RPNs). This article develops an improved FMEA methodology for strategic risk analysis. The proposed approach combines the analytic hierarchy process (AHP) technique with the exponential and weighted geometric mean method (EWGM) to support risk analysis. AHP is applied to estimate the weights of three risk factors: Severity (S), Occurrence (O) and Detection (D), which integrate the RPN for each risk. The EWGM method is applied for ranking RPNs. Combining AHP with EWGM allows avoiding repetition of FMEA results. The results of the developed methodology reveal that duplication of RPNs has been decreased, facilitating an effective risk ranking by offering a unique value for each risk. The proposed methodology not only focuses on high severity values for risk ranking but it also considers other risk factors (O and D), resulting in an enhanced risk assessment process. Furthermore, the weights of the three risk factors are considered. In this way, the developed methodology offers unique value for each risk in a simple way which makes the risk assessment results more accurate. This methodology provides a practical and systematic approach to support decision makers in assessing and ranking risks that could affect long-term strategy implementation. The methodology was validated through the case study of a power plant in the Middle East, assessing 84 risks within 9 risk categories. The case study revealed that top management should pay more attention to key risks associated with electricity price, gas emissions, lost-time injuries, bad odour and production.


Processes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 224 ◽  
Author(s):  
Sami Sader ◽  
István Husti ◽  
Miklós Daróczi

In this paper, multiclass classification is used to develop a novel approach to enhance failure mode and effects analysis and the generation of risk priority number. This is done by developing four machine learning models using auto machine learning. Failure mode and effects analysis is a technique that is used in industry to identify possible failures that may occur and the effects of these failures on the system. Meanwhile, risk priority number is a numeric value that is calculated by multiplying three associated parameters namely severity, occurrence and detectability. The value of risk priority number determines the next actions to be made. A dataset that includes a one-year registry of 1532 failures with their description, severity, occurrence, and detectability is used to develop four models to predict the values of severity, occurrence, and detectability. Meanwhile, the resulted models are evaluated using 10% of the dataset. Evaluation results show that the proposed models have high accuracy whereas the average value of precision, recall, and F1 score are in the range of 86.6–93.2%, 67.9–87.9%, 0.892–0.765% respectively. The proposed work helps in carrying out failure mode and effects analysis in a more efficient way as compared to the conventional techniques.


2012 ◽  
Vol 32 (3) ◽  
pp. 505-514 ◽  
Author(s):  
Sibel Ozilgen

The Failure Mode and Effect Analysis (FMEA) was applied for risk assessment of confectionary manufacturing, in whichthe traditional methods and equipment were intensively used in the production. Potential failure modes and effects as well as their possible causes were identified in the process flow. Processing stages that involve intensive handling of food by workers had the highest risk priority numbers (RPN = 216 and 189), followed by chemical contamination risks in different stages of the process. The application of corrective actions substantially reduced the RPN (risk priority number) values. Therefore, the implementation of FMEA (The Failure Mode and Effect Analysis) model in confectionary manufacturing improved the safety and quality of the final products.


2011 ◽  
Vol 317-319 ◽  
pp. 1837-1842
Author(s):  
Jian Feng Huang

The paper presents a case study: the review of the reliability centered maintenance (RCM) methodology realized in a hydrocracker. Applying of the reliability centered maintenance methodology, it finds 495 failure modes and ranks risk priorities for the 98 rotating machineries in the hydrocracker. According to the results of risk analysis, it provides customized maintenance strategies for each component of the hydrocracker, which would improve the safety of the hydrocracker effectively. The review provides helpful reference for other equipments in refinery factories.


2021 ◽  
pp. 728-741
Author(s):  
Tao Liu ◽  
Yuanzi Zhou ◽  
Junzhong Bao ◽  
Xizhao Wang ◽  
Pengfei Zhang

Sign in / Sign up

Export Citation Format

Share Document