For Failure Mode And Effect Analysis Research Articles

Creating a Team for Failure Mode and Effects Analysis (FMEA)

Creating a Team for Failure Mode and Effects Analysis (FMEA)

Failure mode and Effect Analysis of personal fall arrest system under the intuitionistic fuzzy environment

Background and aimsIntuitionistic fuzzy sets (IFS) theory is more powerful than classic fuzzy sets theory in handling uncertainty. A new approach for Failure Mode and Effect Analysis (FMEA) was developed based on IFS and group decision-making (known as IF-FMEA) for investigating Personal Fall Arrest System (PFAS). MethodFMEA parameters, including occurrence, consequence, and detection, were re-defined based on a seven-point linguistic scale. Each linguistic term was associated with an intuitionistic triangular fuzzy set. Opinions on the parameters were gathered from a panel of experts, integrated using the similarity aggregation method, and defuzzified utilizing the center of gravity approach. ResultsNine failure modes were identified and analyzed using both FMEA and IF-FMEA. The risk priority numbers (RPNs) and prioritization obtained from the two approaches were different, highlighting the importance of using IFS. The highest RPN was associated with the lanyard web failure, while the failure of the anchor D-ring had the least RPN. Detection score was higher for metal parts of the PFAS, suggesting that failures in these parts are harder to detect. ConclusionIn addition to being economical in terms of calculations, the proposed method was efficient in handling uncertainty. Different parts of a PFAS create different levels of risk.

A likelihood‐based ORESTE method for failure mode and effect analysis (FMEA) based risk analysis problem under interval type‐2 fuzzy environment

AbstractThe failure mode and effect analysis (FMEA) model is an effective safety system analysis method to identify and analyze potential risks. Nevertheless, the traditional FMEA possesses deficiencies associating with questionable multiplication procedure, overlooking uncertainty and interaction relationships among risk factors. Besides, determining the risk priority considering the detailed interrelation among the failure modes is beyond the capability of current FMEA models, especially in the case of complex preference information. Thus, this paper proposed an extended FMEA by integrating ORESTE (organization, rangement et Synthèse de données relarionnelles, in French) method, Heronian Mean (HM) operator, and interval type‐2 fuzzy sets (IT2FSs). First, the IT2FSs are utilized to depict the complex risk preference information. Second, the HM operator, as an aggregated tool, is utilized to obtain collective evaluation information, which can fully reflect the overall correlations among individual risk assessment information. Then, an extended ORESTE method based on the likelihood of IT2FSs is utilized to obtain the priority ranking orders of failure modes. In this extended method, a new IT2FSs global preference score is constructed to determine the weak ranking. And, three new preference intensities based on the likelihood of IT2FSs are constructed to determine PIR (preference, indifference, and incomparability) structure which can show the detailed relationships between failure modes. Finally, an illustrative example of a nuclear reheat valve system is selected to verify its application and efficiency.

Implementing a new scale for failure mode and effects analysis (FMEA) for risk analysis in a radiation oncology department

Patients and staffs are endangered by different failure modes during clinical routine in radiation oncology and risks are difficult to stratify. We implemented the method of failure mode and effects analysis (FMEA) via questionnaires in our institution and introduced an adapted scale applicable for radiation oncology. Failure modes in physical treatment planning and daily routine were detected and stratified by ranking occurrence, severity, and detectability in aquestionnaire. Multiplication of these values offers the risk priority number (RPN). We implemented an ordinal rating scale (ORS) as acombination of earlier published scales from the literature. This scale was optimized for German radiation oncology. We compared RPN using this ORS versus use of arather subjective visual analogue rating scale (VRS). Mean RPN using ORS was 62.3 vs. 67.5 using VRS (p = 0.7). Use of ORS led to improved completeness of questionnaires (91 vs. 79%) and stronger agreement among the experts, especially concerning failure modes during radiation routine. The majority of interviewed experts found the analysis by using the ORS easier and expected asaving of time as well as higher intra- and interobserver reliability. The introduced rating scale together with aquestionnaire survey provides merit for conducting FMEA in radiation oncology as results are comparable to the use of VRS and the process is facilitated.

Risk Failure Deployment: A novel integrated tool to prioritize corrective actions in failure mode and effects analysis

AbstractIn this paper, a novel integrated tool for failure mode and effects analysis (FMEA), opportunely named Risk Failure Deployment (RFD), which is able to evaluate the most critical failure modes and provide analyst with a practical and step‐by‐step guidance by selecting the most effective corrective actions for removal/mitigation process of root causes, is presented. Thanks to the modification of the framework of the Manufacturing cost deployment (MCD) and to its well‐structured use of matrices, the novel RFD is able both to handle the dependencies and interactions between different and numerous failures and to evaluate the most critical ones on the basis of the risk priority number (RPN). Thereafter, the logical relationship between root causes and failure modes is assessed. Successively, the prioritization of corrective actions that are the most suitable for root causes is executed using not only the RPN but also other criteria, such as the economic aspect and the ease of implementation, that are unavoidable to execute a rational and effective selection of improvement activities. The effectiveness and usefulness in practice of the original tool for the prioritization of corrective actions to mitigate the risks due to failure modes collected during FMEA are presented in a case study.

An Improved Assessment Method for FMEA for a Shipboard Integrated Electric Propulsion System Using Fuzzy Logic and DEMATEL Theory

Shipboard integrated electric propulsion systems (IEPSs) are prone to suffer from system failures and security threats because of their complex functional structures and poor operational environments. An improved assessment method for failure mode and effects analysis (FMEA), integrating fuzzy logic and decision–making trial and evaluation laboratory (DEMATEL) theory, is proposed to enhance the system’s reliability and handle the correlation effects between failure modes and causes. In this method, information entropy and qualitative analysis are synthesized to determine the credibility weights of domain experts. Each risk factor and its relative importance are evaluated by linguistic terms and fuzzy ratings. The benchmark adjustment search algorithm is designed to obtain the alpha-level sets of fuzzy risk priority numbers (RPNs) for defuzzification. The defuzzified RPNs are regarded as the inputs for the DEMATEL technique to investigate the causal degrees of failure modes and causes. Accordingly, the risk levels of the failure modes are prioritized with respect to the causal degrees. The practical application to the typical failure modes of the propulsion subsystem is provided. The assessment results show that this system contributes to risk priority decision-making and disastrous accident prevention.

Performance analysis and quality improvement using fuzzy MCDM and lean tools in a paper industry

The paper and board manufacturing process is associated with a number of defects that hinder the quality of the product produced. This study proposes a methodology to prioritise the failures/defects and suggests suitable lean tools for process improvement. Initially, cause and effect diagram is used to identify the root causes of failures. Then, fuzzy Delphi method (FDM) is used to identify the critical failures modes. Fuzzy analytical hierarchical process (FAHP) is then used to arrive at the weights for failure mode and effect analysis (FMEA) criterion: severity, occurrence and detection using the expert's opinion. Fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) is then applied to rank the prioritised failures based on the weights calculated using FAHP. Based on FAHP and fuzzy TOPSIS-based FMEA analysis, five failure modes namely crease, felt impression, stiffness variation, blade line and fluff are identified as the most critical. The prioritised design requirements are identified using fuzzy quality function deployment (FQFD) and the improvement methods are suggested by using lean tools such as 5S and total productive maintenance (TPM).

Performance analysis and quality improvement using fuzzy MCDM and lean tools in a paper industry

The paper and board manufacturing process is associated with a number of defects that hinder the quality of the product produced. This study proposes a methodology to prioritise the failures/defects and suggests suitable lean tools for process improvement. Initially, cause and effect diagram is used to identify the root causes of failures. Then, fuzzy Delphi method (FDM) is used to identify the critical failures modes. Fuzzy analytical hierarchical process (FAHP) is then used to arrive at the weights for failure mode and effect analysis (FMEA) criterion: severity, occurrence and detection using the expert's opinion. Fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) is then applied to rank the prioritised failures based on the weights calculated using FAHP. Based on FAHP and fuzzy TOPSIS-based FMEA analysis, five failure modes namely crease, felt impression, stiffness variation, blade line and fluff are identified as the most critical. The prioritised design requirements are identified using fuzzy quality function deployment (FQFD) and the improvement methods are suggested by using lean tools such as 5S and total productive maintenance (TPM).

An integrated approach for failure mode and effect analysis based on uncertain linguistic GRA–TOPSIS method

This paper provides a novel risk priority approach for failure mode and effect analysis (FMEA), which can overcome some inherent drawbacks of the traditional risk priority number (RPN) method in imprecise risk evaluation, risk factor weighting and questionable RPN computation. Considering FMEA team members’ vagueness and uncertainty in their evaluations on failure modes, two-dimensional uncertain linguistic variables are advised to describe the risk evaluation result of a failure mode and the reliability of the evaluation result. The grey relation analysis–technique for order preference by similarity to ideal solution (GRA–TOPSIS) is applied for determining the risk ranking of the identified failure modes. In particular, a maximizing deviation method is employed for calculating the optimal weights of risk factors in an objective way. Via a practical healthcare risk analysis case, the new FMEA is proved to be appropriate and effective in coping with the risk evaluation problems with uncertain linguistic information. Furthermore, by comparing with existing methods, it is shown that the proposed integrated approach excels in the risk evaluation and prioritization of failure modes in FMEA.

Risk Assessment Integrated QbD Approach for Development of Optimized Bicontinuous Mucoadhesive Limicubes for Oral Delivery of Rosuvastatin.

Statins are widely prescribed for hyperlipidemia, cancer, and Alzheimer's disease but are facing some inherent challenges such as low solubility and drug loading, higher hepatic metabolism, as well as instability at gastric pH. So, relatively higher circulating dose, required for exerting the therapeutic benefits, leads to dose-mediated severe toxicity. Furthermore, due to low biocompatibility, high toxicity, and other regulatory caveats such as product conformity, reproducibility, and stability of conventional formulations as well as preferentially higher bioabsorption of lipids in their favorable cuboidal geometry, enhancement in in vivo biopharmaceutical performance of Rosuvastatin could be well manifested in Quality by Design (QbD) integrated cuboidal-shaped mucoadhesive microcrystalline delivery systems (Limicubes). Here, quality-target-product-profile (QTPPs), critical quality attributes (CQAs), Ishikawa fishbone diagram, and integration of risk management through risk assessment matrix for failure mode and effects analysis (FMEA) followed by processing of Plackett-Burman design matrix using different statistical test for the first time established an approach to substantiate the claims that controlling levels of only these three screened out independent process variables, i.e., Monoolein (B = 800-1100μL), Poloxamer (C = 150-200mg), and stirring speed (F = 700-1000rpm) were statistically significant to modulate and improve the biopharmaceutical performance affecting key attributes, viz., average particle size (Y1 = 1.40-2.70μ), entrapment efficiency (Y2 = 62.60-88.80%), and drug loading (Y3 = 0.817-1.15%), in QbD-enabled process. The optimal performance of developed Limicubes exhibited an average particle size of 1.8 ± 0.2μ, entrapment efficiency 80.32 ± 2.88%, and drug loading 0.93 ± 0.08% at the level of 1100μL (+ 1), 200mg (+ 1), and 700rpm (- 1) for Monoolein, Poloxamer, and stirring speed, respectively.

Failure Mode and Effects Analysis based on Z-numbers

AbstractThe main objective of this paper is to propose a new method for failure mode and effects analysis (FMEA) based on Z-numbers. In the proposed method, firstly, Z-numbers are used to perform the valuations (Z-valuation) of the risk factors like occurrence (O), severity (S) and detection (D). Secondly, the Z-valuations of the risk factors are integrated by fuzzy weighted mean method. A new risk priority number named as ZRPN is calculated to prioritize failure modes based on a modified method of ranking fuzzy numbers. Finally, a case study for the rotor blades of an aircraft turbine is performed to demonstrate the feasibility of the proposed method.

Supply chain risk assessment approach for process quality risks

PurposeThe purpose of this paper is to proactively analyse and mitigate the root causes of the product and security risks. The case study approach examines the effectiveness of the fuzzy logic approach for assessing the product and process-related failure modes within global supply chain context.Design/methodology/approachThe case study of a Printed Circuit Board Company in China is used as a platform for conducting the research. Using data triangulation, the data are collected and analyzed through interviews, questionnaires, expert opinions and quantitative modelling for some interesting insights.FindingsFuzzy logic approach for failure mode and effect analysis (FMEA) provides a structured approach for understanding complex behaviour of failure modes and their associated risks for products and processes. Today’s managers should conduct robust risk assessment during the design stage to avoid product safety and security risks such as recalls.Research limitations/implicationsThe research is based on the single case study and multiple cases from different industry sectors may provide some additional insights.Originality/valueThe study attempts to mitigate the root causes of product and processes using fuzzy approach to FMEA in supply chain network.

FMEA—A diagraph and matrix approach

Abstract This paper presents a method for failure mode and effects analysis (FMEA) of mechanical and hydraulic systems based on a diagraph and matrix approach. The method takes into account structural as well as functional interaction of the system. This is desirable as failures in these systems are not independent. A failure mode and effects diagraph, derived from the structure of the system, models the effects of failure modes of the system and consists of nodes, subnodes and edges. For efficient computer processing, matrices are defined to represent the diagraph. A function ( VCM -F me or VPF -F me ) characteristic of the system failure mode and effects is obtained from the matrix and this aids in the detailed analysis leading to the identification of various structural components of failure mode and effects. In addition, the number of tests for failure mode and effects are derived. An index I fme , a measure of failure mode and effects of the system, is obtained using VPF -F me . The methodology is applicable not only at the design stage during the operation stage also.

Computer-aided failure mode and effect analysis of electronic circuits

Most techniques for Failure Mode and Effect Analysis (FMEA) of the electronic equipment and circuits have up till now been based on manual examination of the circuit schemes. This technique has often been considered to be arduous and time consuming. No computer-based methods to perform the FMEA have been available, and it has become obvious that the usual circuit analysis programs are not suitable for this purpose. However, the extended use of electronic equipment in controlling automatic machines has increased the need to evaluate the safety and security of the system as effectively as possible. This is one of the main ressons for starting the generation of this computer program as a tool for FMEA of electronic circuits.