Rule-based ranking

ID Component Failure Mode RPN Fuzzy rule base Grey Theory Ranking (RPN) Ranking (Rule base) Ranking (Grey theory)... 

Isomeric alkenes may be either constitutional isomers or stereoisomers There is a sizable barrier to rotation about a carbon-carbon double bond which corresponds to the energy required to break the rr component of the double bond Stereoisomeric alkenes are configurationally stable under normal conditions The configurations of stereoisomeric alkenes are described according to two notational systems One system adds the prefix CIS to the name of the alkene when similar substituents are on the same side of the double bond and the prefix trans when they are on opposite sides The other ranks substituents according to a system of rules based on atomic number The prefix Z is used for alkenes that have higher ranked substituents on the same side of the double bond the prefix E is used when higher ranked substituents are on opposite sides... 

Cahn-Ingold-Prelog notation (Section 7.6) System for specifying absolute configuration as / or S on the basis of the order in which atoms or groups are attached to a chirality center. Groups are ranked in order of precedence according to rules based on atomic number. 

Tab. 18.1. Comparison of rule-based ranking to mechanistic simulation-based ranking of 138 common drugs.

The amounts of excipients to be used are calculated by formulae with rule-based mechanisms for selecting the appropriate formula. A rule-based mechanism is also used to determine the appropriate function for predicting the property of the intermediate formulation. This is necessary for checking whether or not the original specifications have been satisfied and the action is successful. If negative, the chosen action is rejected and the next action in the ranking is tried. It is possible that none of the ranked actions is successful. 

In a properly constructed ranking analysis, each assumption has to be documented. A sensitivity analysis can be performed, investigating the impacts of ranking decisions upon the final outcome. Uncertainties can also be quantified and data gathered to make the ranking more based upon data-derived rules. 

Table 17.1 Table of 266 common drugs used in the comparison of rule-based ranking with mechanistic simulation-based ranking. 

An approximate ranking of the relative importance of the genes marked with a star ( ) in Table 3.7 to the onset of phospholipidosis is given in Table 3.13. The ranking is based on the observed expression levels of the genes, their frequency of occurrence in the rules, and the strengths of the corresponding mles (vide supra). 

To get a feel of the fuzzy definition set, detection likelihood, as shown in Fig. lV/2.2.3-1, has been transformed into a fuzzy definition. A typical fuzzy membership is shown in Fig. lV/2.6.4-2. Actual fuzzy values are derived based on the fuzzy rule set. Fuzzy inputs are evaluated using a rule-based set, so that criticality and RPN calculations can be made. In the fuzzification process, with help of crisp ranking, set S O D is converted into fuzzy representation so that these can be matched with the rule base. Here, the if then rule has two parts an antecedent (which is compared to input) and consequence (which is the result). On the other hand, in the defuzzification process, the reverse takes place. It is possible to automate FMEA using fuzzy logic and rule-based systems. The rule allows quantitative data such as occurrence to be easily combined with judgmental and quantitative data (such as severity and detectability) very easily and uniformly. The rule based on the linguistic variables is more expressive and useful (for further reading see Ref. [11]). 

The marine industry is recognising the need for powerful techniques that can be used to perform risk analysis of marine systems. One technique that has been applied in both national and international marine regulations and operations is Failure Mode and Effects Analysis (FMEA). This risk analysis tool assumes that a failure mode occurs in a system/component through some failure mechanism. The effect of this failure is then evaluated. A risk ranking is produced in order to prioritise the attention for each of the failure modes identified. The traditional method utilises the Risk Priority Number (RPN) ranking system. This method determines the RPN by finding the multiplication of factor scores. The three factors considered are probability of failure, severity and detectability. Traditional FMEA has been criticised to have several weaknesses. These weaknesses are addressed in this Chapter. A new approach, which utilises the fuzzy rules base and grey relation theory, is presented. 

A traditional FMEA using the RPN ranking system is carried out in the first instance. This analysis is summarised in Table 7.9. In Table 7.9,5/represents the probability of occurrence, S the severity, and Sj the detectability. The values for Sf, S and Sd are obtained by using the values detailed in Tables 7.1, 7.2 and 7.3, respectively. The same pool of experts that carried out the analysis for the fuzzy rule based method and grey theory method is used for the traditional FMEA analysis. This ensures the consistency in the opinion of each expert. 

The traditional FMEA, the fuzzy rule based method and the grey theory approach may complement each other to produce a risk ranking with confidence. 

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