An Assessment of RPN Prioritization in a Failure Modes Effects and Criticality Analysis

2004 ◽  
Vol 47 (1) ◽  
pp. 51-56 ◽  
Author(s):  
John Bowles
Keyword(s):  
Failure Modes ◽  
Measurement Scale ◽  
Risk Priority Number ◽  
Probability Of Occurrence ◽  
Ordinal Ranking ◽  
Potential Failure ◽  
Different Characteristics ◽  
Criticality Analysis

The Risk Priority Number methodology for prioritizing failure modes is an integral part of the Automobile Failure Modes and Effects Analysis (FMECA) technique. This technique consists of ranking potential failures from 1 to 10 with respect to their severity, probability of occurrence, and likelihood of detection in later tests, and multiplying the numbers. The result is a numerical ranking, called the RPN, on a scale from 1 to 1000. Potential failure modes having higher RPNs are assumed to have a higher design risk than those having lower values. Although this method is well documented and easy to apply, it is seriously flawed from a technical perspective, making the interpretation of the analysis results problematic. Problems with the methodology include: use of ordinal ranking numbers as numeric quantities; lack of continuity in the RPN measurement scale; duplicate RPN values with extremely different characteristics; and varying sensitivity to small changes. Recommendations for an improved methodology are provided.

A Systematized FMEA Practice for Magnet Relays

2010 ◽  
Vol 146-147 ◽  
pp. 757-769
Author(s):  
Ching Ming Cheng ◽  
Wen Fang Wu ◽  
Yao Hsu
Keyword(s):  
New Product Development ◽  
Failure Modes ◽  
New Product ◽  
Risk Priority Number ◽  
Detection Model ◽  
Regulation Function ◽  
Potential Failure ◽  
Weighting Rule ◽  
Rating Criterion

The Design Failure Modes and Effects Analysis (DFMEA) are generally applied to risk management of New Product Development (NPD) through standardization of potential failure modes and effect-ranking of rating criterion with failure modes. Typical 1 to 10 of effect-ranking are widely weighed the priority of classification, that framing effects and status quo senses might cause decision trap happening thus. The FMEA follows considerable indexes which are including Severity, Occurrence and Detection, and need be associated with difference between every two failures individually. However, we suspect that a more systematic construction of the analysis by which failure modes belong is necessary in order to make intellectual progress in this area. Two ways of such differentiation and construction are improvable effect-ranking and systematized indexes; here we resolve for attributes of failures with classification, maturity and experiance of indexes according to an existing rule. In Severity model, the larger differentiation is achieved by separating indexes to the classification of the Law & Regulation, Function and Cosmetic. Occurrence model has its characteristic a reliable ranking indexwhich assists decisionmakers to manage their venture. This is the model most closely associate with product maturity by grouping indexes to the new, extend and series product. Detection model offers a special perspective on cost; here the connections concerned with phase occasion of the review, verification and validation. Such differentiations will be proposed and mapped with the Life Cycle Profile (LCP) to systematize FMEA. Meanwhile, a more reasonable Risk Priority Number (RPN) with the new weighting rule will be worked out for effect-ranking and management system will be integrated systematiclly

Improving reliability in critical healthcare sector using FAMECA methodology

BMJ Leader ◽  
2017 ◽  
Vol 1 (4) ◽  
pp. 50-56
Author(s):  
Polinpapilinho Freeman Katina ◽  
Nina C Magpili-Smith
Keyword(s):  
Failure Modes ◽  
Methodological Approach ◽  
Well Being ◽  
Healthcare Systems ◽  
Healthcare Sector ◽  
Critical Infrastructures ◽  
Goods And Services ◽  
Mode Effects ◽  
Potential Failure ◽  
Criticality Analysis

BackgroundHealthcare systems are critical to the well-being of the society. In such a setting, the ability of the system to perform its intended mission/function during the designed period of time (ie, reliability) is essential. However, there remains a scarcity of literature, suggesting how the concept of reliability can be addressed in the context of critical healthcare infrastructure systems.MethodsWe recognise the importance of healthcare in the context of critical infrastructures. These systems produce goods and services essential for maintaining and sustaining public well-being. We suggest the use of failure mode, effects and criticality analysis (FAMECA) approach to increase reliability in critical healthcare systems. Phases of FAMECA are described.ResultsAfter reviewing the application of FAMECA and describing its basics, authors describe critical healthcare sector in terms of components, organisations, management and non-healthcare interdependent systems. The resulting application indicates applicability of the approach and articulates failure modes, effects and development of possible solutions to such modes and effects to increase reliability. The presented application, however, is very general and specific case applications are needed.ConclusionsA decision to suggest the FAMECA as a methodological approach in critical healthcare systems is pivotal to improving systems reliability and enhances the ability of the system to meet its intended missions during the designed period of time. The utility of FAMECA is found in its ability to identify potential failure modes, their effects and suggesting remedial efforts, including tools and technologies to address failure modes and their effects.

scholarly journals Controlling Risks in the Compounding Process of Individually Formulated Parenteral Nutrition: Use of the FMECA Method (Failure modes, effects, and Criticality Analysis)

2020 ◽  
Vol 4 (3-4) ◽  
pp. 105-112
Author(s):  
Mathilde Royer ◽  
Maïté Libessart ◽  
Jean-Marc Dubaele ◽  
Pierre Tourneux ◽  
Fréderic Marçon
Keyword(s):  
Intensive Care Unit ◽  
Functional Analysis ◽  
Intensive Care ◽  
Parenteral Nutrition ◽  
Neonatal Intensive Care ◽  
Failure Modes ◽  
Risk Priority Number ◽  
Multidisciplinary Working ◽  
Criticality Analysis

AbstractParenteral nutrition (PN) in the neonatal intensive care unit (NICU) involves a succession of risky processes. The objective was to identify and prioritize the risks associated with PN in order to improve the quality of the pathway. A failure modes, effects, and criticality analysis (FMECA) was used to identify potential PN pathway failure modes. A multidisciplinary working group conducted a functional analysis of the processes, then listed the failure modes (FM). The FM criticality was assessed on a scale from 1 to 5 for occurrence (O), severity (S), and detection (D). The risk priority number (RPN), ranging from 1 to 125, was calculated. The FMECA identified 99 FM (prescription (n=28), preparation (n=48), and administration (n=23)). The median RPN was 12, with scores ranging from 3 to 48. 25 % of the scores had an RPN>21.75.Among them, 12 were associated with prescription FM, 5 were associated with FM related to preparation and 8 were associated with a FM linked to administration. It allowed us to prioritize areas of potential quality improvement for parenteral nutrition of the preterm infant. The results demonstrated the need for the presence of a clinical pharmacist in the NICU to ensure the quality of PN process.

Failure Mode and Effects Analysis on Control Equipment Using Fuzzy Theory

2013 ◽  
Vol 837 ◽  
pp. 16-21
Author(s):  
Nadia Belu ◽  
Daniel Constantin Anghel ◽  
Nicoleta Rachieru
Keyword(s):  
System Design ◽  
Failure Mode ◽  
Failure Modes ◽  
Fuzzy Theory ◽  
Risk Level ◽  
Risk Priority Number ◽  
Control Equipment ◽  
Wide Range ◽  
Criticality Analysis ◽  
Made In

Failure Mode and Effects Analysis is a methodology to evaluate a system, design, process, machine or service for possible ways in which failures (problems, errors, risks and concerns) can occur and it has been used in a wide range of industries. Traditional method uses a Risk Priority Number to evaluate the risk level of a component or process. This is obtained by finding the multiplication of three factors, which are the severity of the failure (S), the probability/occurrence of the failure (O), and the probability of not detecting the failure (D). There are significant efforts which have been made in FMEA literature to overcome the shortcomings of the crisp RPN calculation. Fuzzy logic appears to be a powerful tool for performing a criticality analysis on a system design and prioritizing failure identified in analisys FMEA for corrective actions. In this paper we present a parallel between the typical and the fuzzy computation of RPNs, in order to assess and rank risks associated to failure modes that could appear in the functioning of control equipment.

Identification of Human–System Interaction Errors During Early Design Stages Using a Functional Basis Framework

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Nicolás F. Soria Zurita ◽  
Robert B. Stone ◽  
H. Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Design Process ◽  
Human Error ◽  
Failure Modes ◽  
Composite Function ◽  
Human System ◽  
Early Design ◽  
Functional Basis ◽  
Potential Failure ◽  
Early Design Stages ◽  
Criticality Analysis

Abstract Engineers have developed different design methodologies capable of identifying failure modes of engineering systems. The most common methods used in industry are failure modes and effects analysis, and failure modes effects and criticality analysis. Nevertheless, such methodologies have a significant limitation regarding incorporating the final user in the analysis and are not suited to identifying potential failure modes caused by physical human–system interactions. Engineering methods usually have a lack of sufficient attention to human–system interactions during the early design stages, even though introducing human factors principles is recognized as an essential analysis during the design process. As a result, designers rely on developing detailed and expensive physical or virtual prototypes to evaluate physical human–system interactions and identify potential failure modes caused by such interactions incorporating design modifications after a prototype is developed can be time-consuming, costly, and if significant changes are needed, the entire prototype requires to be constructed again. Identifying system–user interactions and possible failure modes associated with such interactions before developing a prototype can significantly improve the design process. In previous work, the authors introduced the function–human error design method (FHEDM), a tool capable of distinguishing possible human–system interaction failure modes using a functional basis framework. In this work, we examined the implementation of FHEDM within 148 products extracted from the design repository. The results are grouped in the composite function–user interaction error (FUIE) matrix, which can be used as a preliminary design database presenting information regarding the possible human error present in function-flow combinations.

scholarly journals Failure Mode and Effect Analysis (FMEA) for confectionery manufacturing in developing countries: Turkish delight production as a case study

2012 ◽  
Vol 32 (3) ◽  
pp. 505-514 ◽  
Author(s):  
Sibel Ozilgen
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Chemical Contamination ◽  
Risk Priority Number ◽  
Analysis Model ◽  
Production Potential ◽  
Effect Analysis ◽  
Potential Failure

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.

A STATE-OF-THE-ART REVIEW OF FMEA/FMECA

1994 ◽  
Vol 01 (04) ◽  
pp. 515-543 ◽  
Author(s):  
ABDELKADER BOUTI ◽  
DAOUD AIT KADI
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
State Of The Art ◽  
Analysis Procedure ◽  
Basic Principles ◽  
Mode Effects ◽  
System Service ◽  
Computer Codes ◽  
Potential Failure ◽  
Criticality Analysis

The Failure Mode and Effects Analysis (FMEA) documents single failures of a system, by identifying the failure modes, and the causes and effects of each potential failure mode on system service and defining appropriate detection procedures and corrective actions. When extended by Criticality Analysis procedure (CA) for failure modes classification, it is known as Failure Mode Effects and Criticality Analysis (FMECA). The present paper presents a literature review of FME(C)A, covering the following aspects: description and review of the basic principles of FME(C)A, types, enhancement of the method, automation and available computer codes, combination with other techniques and specific applications. We conclude with a discussion of various issues raised as a result of the review.

scholarly journals Failure Modes and Effects Analysis (FMEA) for evaluation of a sugarcane machine failure

2018 ◽  
Vol 204 ◽  
pp. 01012
Author(s):  
Hilma Raimona Zadry ◽  
Dendi Adi Saputra ◽  
Agung Budiman Tabri ◽  
Difana Meilani ◽  
Dina Rahmayanti
Keyword(s):  
Failure Modes ◽  
Design Stage ◽  
Preliminary Treatment ◽  
Risk Priority Number ◽  
Ergonomic Design ◽  
Machine Maintenance ◽  
Machine Failure ◽  
Potential Failure ◽  
Fmea Method ◽  
Causes Of Failure

The Failure Modes and Effects Analysis (FMEA) method has been widely recognized as a tool that systematically identifies the consequences and failures of the system or process, and reduces or eliminates the chances of the failure. This study applies that method to evaluate the causes of failure in the use of sugarcane machine that have been designed in the previous studies. FMEA approach anticipated the failures at the design stage, so that a more reliable and ergonomic design can be produced for future sugarcane machine. The potential failure identified from the machine consists of capacity issues, machine maintenance, preliminary treatment, and procedures of use. The study found that capacity issues are the priority problems that cause the machine failure. Then, this study proposed some actions to reduce the risk priority number (RPN) on 12 failures.

scholarly journals A Novel Approach to Support Failure Mode, Effects, and Criticality Analysis Based on Complex Networks

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1230 ◽  
Author(s):  
Lixiang Wang ◽  
Wei Dai ◽  
Guixiu Luo ◽  
Yu Zhao
Keyword(s):  
Complex Networks ◽  
Failure Mode ◽  
Failure Modes ◽  
Weighted Graph ◽  
Complex Network Theory ◽  
Mode Effects ◽  
Novel Approach ◽  
Potential Failure ◽  
The Impact ◽  
Criticality Analysis

Failure Mode, Effects and Criticality Analysis (FMECA) is a method which involves quantitative failure analysis. It systematically examines potential failure modes in a system, as well as the components of the system, to determine the impact of a failure. In addition, it is one of the most powerful techniques used for risk assessment and maintenance management. However, various drawbacks are inherent to the classical FMECA method, especially in ranking failure modes. This paper proposes a novel approach that uses complex networks theory to support FMECA. Firstly, the failure modes and their causes and effects are defined as nodes, and according to the logical relationship between failure modes, and their causes and effects, a weighted graph is established. Secondly, we use complex network theory to analyze the weighted graph, and the entropy centrality approach is applied to identify influential nodes. Finally, a real-world case is presented to illustrate and verify the proposed method.

scholarly journals A Framework for Creating a Function-Based Design Tool for Failure Mode Identification

2002 ◽  
Author(s):  
Srikesh G. Arunajadai ◽  
Robert B. Stone ◽  
Irem Y. Tumer
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Fault Tree Analysis ◽  
Design Tool ◽  
Tree Analysis ◽  
Mode Identification ◽  
Potential Failure ◽  
Failure Mode Identification ◽  
Criticality Analysis ◽  
Failure Identification

Knowledge of potential failure modes during design is critical for prevention of failures. Currently industries use procedures such as Failure Modes and Effects Analysis (FMEA), Fault Tree analysis, or Failure Modes, Effects and Criticality analysis (FMECA), as well as knowledge and experience, to determine potential failure modes. When new products are being developed there is often a lack of sufficient knowledge of potential failure mode and/or a lack of sufficient experience to identify all failure modes. This gives rise to a situation in which engineers are unable to extract maximum benefits from the above procedures. In this work we report on a new failure identification scheme and integrate it with a function-based failure identification methodology, which would act as a storehouse of information and experience, providing useful information about the potential failure modes for the design under consideration, as well as enhancing the usefulness of procedures like FMEA. As an example, the method is applied to 41 products and the benefits are illustrated.

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