FMECA Methodology: Failure Mode Effect And Criticality Analysis
3. FMECA Methodology
3.1 Failure mode effect & criticality analysis
FMEA is called as FMECA (failure mode, effects and criticality analysis) when it is used for criticality analysis. In general, FMECA is performed in two parts: (I) to identify the different failure modes and its effects by failure mode and effect analysis (FMEA); (ii) to Classify failure mode criticality analysis by probability of occurrence and its severity. (Bowles & Pelaez, 1995). FMEA is traditionally calculated by developing a risk priority number (RPN). When performing an FMEA, three key parameters are used, namely, Occurrence (O), this gives the particulars regarding the probability of occurrences of accident. Severity of the associated effects (S), this deals with …show more content…
Wang (Wang, 2009) pointed out that the relative importance among O, S and D is not taken into consideration in most of the FMEA analysis. Liu (Liu, 2013) identified that different combinations of O, S and D may produce exactly the same value of RPN, but their hidden risk implications may be totally different. Liu also pointed out that the three risk factors are difficult to be precisely evaluated. Liu highlighted that the mathematical formula for calculating RPN is questionable and debatable. RPN cannot be used to measure the effectiveness of corrective actions taken into account(Carmignani, 2009). Failures can often give same RPN number, making prioritisation …show more content…
This approach could take care of issues successfully and distinguish the potential failure modes and impacts expressly. Moreover, it could likewise create trust simultaneously. It permits the examiner to utilize linguistic terms in criticality evaluation for surveying the dangers connected with failure straightforwardly. Ambiguity, subjective information, or data including quantitative information, could be utilized as a part of the appraisal and organization reliably, yet not unequivocally. The structure of the blend of parameters, severity (S), occurrence (O), and detection (D), was more adaptable. This study focuses on the use of fuzzy in FMEA to analyse whether the results show a more accurate, reasonable ranking than the traditional method and thereby overcoming the shortcomings of traditional
3.1 Failure mode effect & criticality analysis
FMEA is called as FMECA (failure mode, effects and criticality analysis) when it is used for criticality analysis. In general, FMECA is performed in two parts: (I) to identify the different failure modes and its effects by failure mode and effect analysis (FMEA); (ii) to Classify failure mode criticality analysis by probability of occurrence and its severity. (Bowles & Pelaez, 1995). FMEA is traditionally calculated by developing a risk priority number (RPN). When performing an FMEA, three key parameters are used, namely, Occurrence (O), this gives the particulars regarding the probability of occurrences of accident. Severity of the associated effects (S), this deals with …show more content…
Wang (Wang, 2009) pointed out that the relative importance among O, S and D is not taken into consideration in most of the FMEA analysis. Liu (Liu, 2013) identified that different combinations of O, S and D may produce exactly the same value of RPN, but their hidden risk implications may be totally different. Liu also pointed out that the three risk factors are difficult to be precisely evaluated. Liu highlighted that the mathematical formula for calculating RPN is questionable and debatable. RPN cannot be used to measure the effectiveness of corrective actions taken into account(Carmignani, 2009). Failures can often give same RPN number, making prioritisation …show more content…
This approach could take care of issues successfully and distinguish the potential failure modes and impacts expressly. Moreover, it could likewise create trust simultaneously. It permits the examiner to utilize linguistic terms in criticality evaluation for surveying the dangers connected with failure straightforwardly. Ambiguity, subjective information, or data including quantitative information, could be utilized as a part of the appraisal and organization reliably, yet not unequivocally. The structure of the blend of parameters, severity (S), occurrence (O), and detection (D), was more adaptable. This study focuses on the use of fuzzy in FMEA to analyse whether the results show a more accurate, reasonable ranking than the traditional method and thereby overcoming the shortcomings of traditional