Prioritization Of Failure Modes Research Articles

A Healthcare Failure Mode and Effect Analysis on the Safety of Secondary Infusions

Secondary infusions are a common and convenient method to administer intermittent infusion unattended through a single IV access using infusion pump technology. Previous studies have indicated that clinicians have a high frequency of committing operation errors while administering secondary infusions. The purpose of this study was to evaluate the safety of secondary infusion practice by identifying and analyzing potential failure modes when delivering secondary infusions on five different smart infusion pumps. Healthcare Failure Mode and Effect Analysis (HFMEA) was used to investigate the potential outcomes of each identified failure mode, while prioritizing failure modes that are considered high risk for each pump. General categories of failure modes are described and mitigation strategies for some of the failure modes are discussed.

Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment

Failure mode and effects analysis (FMEA) is a widely used risk assessment tool for defining, identifying, and eliminating potential failures or problems in products, process, designs, and services. In traditional FMEA, the risk priorities of failure modes are determined by using risk priority numbers (RPNs), which can be obtained by multiplying the scores of risk factors like occurrence (O), severity (S), and detection (D). However, the crisp RPN method has been criticized to have several deficiencies. In this paper, linguistic variables, expressed in trapezoidal or triangular fuzzy numbers, are used to assess the ratings and weights for the risk factors O, S, and D. For selecting the most serious failure modes, the extended VIKOR method is used to determine risk priorities of the failure modes that have been identified. As a result, a fuzzy FMEA based on fuzzy set theory and VIKOR method is proposed for prioritization of failure modes, specifically intended to address some limitations of the traditional FMEA. A case study, which assesses the risk of general anesthesia process, is presented to demonstrate the application of the proposed model under fuzzy environment.

Cloud model-based failure mode and effects analysis for prioritization of failures of power transformer in risk assessment

SUMMARY The maintenance strategy of power transformer, which is determined by the operational condition, is very important to ensure safety for power system. But there exists prevalently the problem of lacking theoretical basis in preventive maintenance strategy to power transformer at present. Failure mode and effects analysis (FMEA) is a methodology for the analysis of potential failure modes within a system and has been extensively utilized for examining potential failures of power transformer. The critical issue of FMEA is the determination of risk priorities of potential failure modes. In order to provide the basis for risk assessment and preventive maintenance decision making, this work proposes the FMEA based on cloud model for prioritizing failure modes, which can combine randomicity with ambiguity and realize the transform between qualitative evaluation and quantitative numerical value, intending to overcome limitations of traditional FMEA. Two numerical examples are utilized to illustrate the potential applications of the proposed FMEA and the detailed computational process of the risk priority number based on cloud model. Copyright © 2012 John Wiley & Sons, Ltd.

A fuzzy linear programming model for risk evaluation in failure mode and effects analysis

In traditional approach, failure mode and effects analysis determines the risk priories of failure modes through the risk priority number which is determined by multiplication of three risk factors namely, failure occurrence (O), failure severity (S) and failure detection ability (D). In this approach, different weights of risk factors were not taken into consideration so that the three risk factors were assumed to have the same weight. This may not be realistic in real applications. In this paper we treat the risk factors as fuzzy variables and evaluate them using fuzzy linguistic terms and fuzzy ratings. As a result, fuzzy risk priority numbers (FRPNs) are proposed for prioritization of failure modes. The FRPNs are defined as fuzzy geometric means of the fuzzy ratings for O, S and D and can be computed using alpha-level sets and linear programming models. A numerical example is provided to examine the results of this model.

Criticality Assessment Models for Failure Mode Effects and Criticality Analysis Using Fuzzy Logic

Traditional Failure Mode and Effects Analysis (FMEA) has shown its effectiveness in defining, identifying, and eliminating known and/or potential failures or problems in products, process, designs, and services to help ensure the safety and reliability of systems applied in a wide range of industries. However, its approach to prioritize failure modes through a crisp risk priority number (RPN) has been highly controversial. This paper proposes two models for prioritizing failures modes, specifically intended to overcome such limitations of traditional FMEA. The first proposed model treats the three risk factors as fuzzy linguistic variables, and employs alpha level sets to provide a fuzzy RPN. The second model employs an approach based on the degree of match and fuzzy rule-base. This second model considers the diversity and uncertainty in the opinions of FMEA team members, and converts the assessed information into a convex normalized fuzzy number. The degree of match (DM) is used thereafter to estimate the matching between the assessed information and the fuzzy number characterizing the linguistic terms. The proposed models are suitably supplemented by illustrative examples.

Failure Mode and Effects Analysis

To empirically compare 2 different commonly used failure mode and effects analysis (FMEA) scoring procedures with respect to their resultant failure mode scores and prioritization: a mathematical procedure, where scores are assigned independently by FMEA team members and averaged, and a consensus procedure, where scores are agreed on by the FMEA team via discussion. A multidisciplinary team undertook a Healthcare FMEA of chemotherapy administration. This included mapping the chemotherapy process, identifying and scoring failure modes (potential errors) for each process step, and generating remedial strategies to counteract them. Failure modes were scored using both an independent mathematical procedure and a team consensus procedure. Almost three-fifths of the 30 failure modes generated were scored differently by the 2 procedures, and for just more than one-third of cases, the score discrepancy was substantial. Using the Healthcare FMEA prioritization cutoff score, almost twice as many failure modes were prioritized by the consensus procedure than by the mathematical procedure. This is the first study to empirically demonstrate that different FMEA scoring procedures can score and prioritize failure modes differently. It found considerable variability in individual team members' opinions on scores, which highlights the subjective and qualitative nature of failure mode scoring. A consensus scoring procedure may be most appropriate for FMEA as it allows variability in individuals' scores and rationales to become apparent and to be discussed and resolved by the team. It may also yield team learning and communication benefits unlikely to result from a mathematical procedure.

Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory

Failure mode and effects analysis (FMEA) is a methodology to evaluate a system, design, process or service for possible ways in which failures (problems, errors, etc.) can occur. The two most important issues of FMEA are the acquirement of FMEA team members’ diversity opinions and the determination of risk priorities of the failure modes that have been identified. First, the FMEA team often demonstrates different opinions and knowledge from one team member to another and produces different types of assessment information because of its cross-functional and multidisciplinary nature. These different types of information are very hard to incorporate into the FMEA by the traditional model and fuzzy logic approach. Second, the traditional FMEA determines the risk priorities of failure modes using the risk priority numbers (RPNs) by multiplying the scores of the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode. The method has been criticized to have several shortcomings. In this paper, we present an FMEA using the fuzzy evidential reasoning (FER) approach and grey theory to solve the two problems and improve the effectiveness of the traditional FMEA. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members’ diversity opinions and prioritize failure modes under different types of uncertainties.

Failure mode and effects analysis using a group-based evidential reasoning approach

Failure mode and effects analysis (FMEA) is a methodology to evaluate a system, design, process or service for possible ways in which failures (problems, errors, risks and concerns) can occur. It is a group decision function and cannot be done on an individual basis. The FMEA team often demonstrates different opinions and knowledge from one team member to another and produces different types of assessment information such as complete and incomplete, precise and imprecise and known and unknown because of its cross-functional and multidisciplinary nature. These different types of information are very difficult to incorporate into the FMEA by the traditional risk priority number (RPN) model and fuzzy rule-based approximate reasoning methodologies. In this paper we present an FMEA using the evidential reasoning (ER) approach, a newly developed methodology for multiple attribute decision analysis. The proposed FMEA is then illustrated with an application to a fishing vessel. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members’ diversity opinions and prioritize failure modes under different types of uncertainties.

Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean

Failure mode and effects analysis (FMEA) has been extensively used for examining potential failures in products, processes, designs and services. An important issue of FMEA is the determination of risk priorities of the failure modes that have been identified. The traditional FMEA determines the risk priorities of failure modes using the so-called risk priority numbers (RPNs), which require the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode to be precisely evaluated. This may not be realistic in real applications. In this paper we treat the risk factors O, S and D as fuzzy variables and evaluate them using fuzzy linguistic terms and fuzzy ratings. As a result, fuzzy risk priority numbers (FRPNs) are proposed for prioritization of failure modes. The FRPNs are defined as fuzzy weighted geometric means of the fuzzy ratings for O, S and D, and can be computed using alpha-level sets and linear programming models. For ranking purpose, the FRPNs are defuzzified using centroid defuzzification method, in which a new centroid defuzzification formula based on alpha-level sets is derived. A numerical example is provided to illustrate the potential applications of the proposed fuzzy FMEA and the detailed computational process of the FRPNs.

An Assessment of RPN Prioritization in a Failure Modes Effects and 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.