scholarly journals Time-Dependent Probabilistic Approach of Failure Mode and Effect Analysis

2019 ◽  
Vol 9 (22) ◽  
pp. 4939
Author(s):  
Jang ◽  
Min
Keyword(s):  
Failure Mode ◽  
Probabilistic Approach ◽  
Time Instant ◽  
Time Dependent ◽  
Occurrence Rate ◽  
Expected Loss ◽  
Effect Analysis ◽  
Automotive Manufacturing ◽  
Evaluation Techniques

Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks that may occur during product design and manufacturing phases. However, use of the risk priority number (RPN) in traditional FMEA results in difficulties being encountered with regard to quantification of the degree of risk involved. This study proposes the use of a probabilistic time-dependent FMEA (TD-FMEA) approach to overcome limitations encountered during implementation of traditional FMEA approaches. To this end, the proposed method defines the risk priority metric (RPM) as a priority decision value. RPM corresponds to the product of the expected loss and occurrence rate of the failure-cause. By assuming exponential and case functions for each occurrence and detection time instant, the expected loss corresponding to each failure-cause can be evaluated. Utility of the proposed approach has been described in the light of results obtained via its implementation during an automotive-manufacturing case study performed for the purpose of illustration.

scholarly journals Time-Dependent Probabilistic Model for Hierarchical Structure in Failure Mode and Effect Analysis

2019 ◽  
Vol 9 (20) ◽  
pp. 4265 ◽  
Author(s):  
Jang ◽  
Min
Keyword(s):  
Failure Mode ◽  
Hierarchical Structure ◽  
Probabilistic Model ◽  
Economic Loss ◽  
Time Instant ◽  
Time Dependent ◽  
Effect Analysis ◽  
Elapsed Time ◽  
The Hierarchical Structure ◽  
Cause Of Failure

Failure mode and effect analysis (FMEA) is a structured technique for identifying risks that may occur during a given stage of a system’s life cycle. However, the use of the risk priority number (RPN) in traditional FMEA results in difficulties with regard to quantification of the degree of risk in the hierarchical failure structure. This study proposes the use of a hierarchical time-dependent FMEA approach to overcome the limitations encountered during the implementation of traditional FMEA approaches. In place of the RPN, a probabilistic loss model is developed under a hierarchical structure considering the elapsed time from the failure-cause (FC) to the system failure. By assuming exponential and case functions for each occurrence and detection time instant, the expected loss corresponding to each FC can be evaluated. As a result of the practical application of the time-dependent probabilistic model through the numerical example, we could reasonably evaluate the risk from the cause of failure in the hierarchical structure in terms of economic loss.

scholarly journals Case Study of Expected Loss Failure Mode and Effect Analysis Model Based on Maintenance Data

2021 ◽  
Vol 11 (16) ◽  
pp. 7349
Author(s):  
Seungsik Min ◽  
Hyeonae Jang
Keyword(s):  
Risk Assessment ◽  
Failure Mode ◽  
Failure Detection ◽  
Quantitative Risk Assessment ◽  
Expected Loss ◽  
Effect Analysis ◽  
Assessment Indicator ◽  
Application Procedure ◽  
Record Data

Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks during the product design and manufacturing phases. Risk priority number (RPN), a risk assessment indicator used in FMEA, is widely used in the field due to its simple calculation process, but its limitations as an absolute risk assessment indicator have been pointed out. There has also been criticism of the unstructured nature and lack of systematicity in the FMEA procedures. This work proposes an expected loss-FMEA (EL-FMEA) model that organizes FMEA procedures and structures quantitative risk assessment metrics. In the EL-FMEA model, collectible maintenance record data is defined and based on this, the failure rate of components and systems and downtime and uptime of the system are calculated. Moreover, based on these calculated values, the expected economic loss is computed considering the failure detection time. It also provides an alternative coefficient to evaluate whether or not a detection system is installed to improve the expected loss of failure. Finally, a case study was conducted based on the maintenance record data, and the application procedure of the EL-FMEA model was presented in detail, and the practicality of this model was verified through the results.

Failure mode and effect analysis (FMEA) to improve collaborative project-based learning: Case study of a Study and Research Path in mechanical engineering

2021 ◽  
pp. 030641902199904
Author(s):  
Elena Bartolomé ◽  
Paula Benítez
Keyword(s):  
Active Learning ◽  
Failure Mode ◽  
Mechanical Engineering ◽  
Failure Modes ◽  
Collaborative Study ◽  
Project Based Learning ◽  
Educational Process ◽  
Effect Analysis ◽  
Questions And Answers

Failure Mode and Effect Analysis (FMEA) is a powerful quality tool, widely used in industry, for the identification of failure modes, their effects and causes. In this work, we investigated the utility of FMEA in the education field to improve active learning processes. In our case study, the FMEA principles were adapted to assess the risk of failures in a Mechanical Engineering course on “Theory of Machines and Mechanisms” conducted through a project-based, collaborative “Study and Research Path (SRP)” methodology. The SRP is an active learning instruction format which is initiated by a generating question that leads to a sequence of derived questions and answers, and combines moments of study and inquiry. By applying the FMEA, the teaching team was able to identify the most critical failures of the process, and implement corrective actions to improve the SRP in the subsequent year. Thus, our work shows that FMEA represents a simple tool of risk assesment which can serve to identify criticality in educational process, and improve the quality of active learning.

Application of failure mode and effects analysis to reduce microplastic emissions

2021 ◽  
pp. 0734242X2110031
Author(s):  
Ana Pires ◽  
Paula Sobral
Keyword(s):  
Failure Mode ◽  
Quantitative Methods ◽  
Policy Instruments ◽  
Effect Analysis ◽  
Advantages And Disadvantages ◽  
Heuristic Technique ◽  
Single Use ◽  
Regulatory Measures ◽  
Plastic Products

A complete understanding of the occurrence of microplastics and the methods to eliminate their sources is an urgent necessity to minimize the pollution caused by microplastics. The use of plastics in any form releases microplastics to the environment. Existing policy instruments are insufficient to address microplastics pollution and regulatory measures have focussed only on the microbeads and single-use plastics. Fees on the use of plastic products may possibly reduce their usage, but effective management of plastic products at their end-of-life is lacking. Therefore, in this study, the microplastic–failure mode and effect analysis (MP–FMEA) methodology, which is a semi-qualitative approach capable of identifying the causes and proposing solutions for the issue of microplastics pollution, has been proposed. The innovative feature of MP–FMEA is that it has a pre-defined failure mode, that is, the release of microplastics to air, water and soil (depending on the process) or the occurrence of microplastics in the final product. Moreover, a theoretical recycling plant case study was used to demonstrate the advantages and disadvantages of this method. The results revealed that MP–FMEA is an easy and heuristic technique to understand the failure-effect-causes and solutions for reduction of microplastics and can be applied by researchers working in different domains apart from those relating to microplastics. Future studies can include the evaluation of the use of MP–FMEA methodology along with quantitative methods for effective reduction in the release of microplastics.

An improving approach for failure mode and effect analysis under uncertainty environment: A case study of critical function component

2020 ◽  
Vol 36 (6) ◽  
pp. 2119-2145 ◽  
Author(s):  
Guangquan Huang ◽  
Liming Xiao ◽  
Wei Zhang ◽  
Jian Li ◽  
Genbao Zhang ◽  
...  
Keyword(s):  
Failure Mode ◽  
Critical Function ◽  
Effect Analysis ◽  
Uncertainty Environment
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