# A Comprehensive Failure Risk Assessment Method of
## 3 Case Application
According to the independent risk calculation equation of the machining center component, the independent risk value of each failure mode of the component can be obtained at any time. This paper takes 4000 h as an example for illustrate. According to Table 4 and Equation (3), the failure rate of each component of the machining center at 4000 h is shown in Table 5.  
In order to obtain the influence degree of the failure mode, the target layer and the control layer of the ANP model constructed in this paper are both the influence degree of the failure mode of the machining center. Each component is a cluster, and the elements in the cluster are the failure modes included in the component. According to the division result of the component and the failure mode, the component code and the failure mode code are combined to form the element code, and the Super Decision software is applied to obtain the ANP model of the failure mode, as shown in Figure 7. \begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline
**Code** & **Cause** & **Frequency** & **Frequency** & **Code** & **Cause** & **Frequency** & **Frequency** \\  & & **Number** & & & & & **Number** & **Frequency** \\ \hline
34 & Damaged parts & 33 & 0.3056 & 32 & CNC parameter error & 3 & 0.0278 \\
20 & Leakage & 16 & 0.1481 & 37 & Improper design & 3 & 0.0278 \\
33 & Damaged components & 8 & 0.0741 & 03 & Stuck & 2 & 0.0185 \\
36 & Improper adjustment & 8 & 0.0741 & 16 & Gap discomfort & 2 & 0.0185 \\
17 & Pressure and flow are not suitable & 6 & 0.0556 & 23 & Overheat & 2 & 0.0185 \\
14 & Loose & 4 & 0.0370 & 42 & Insufficient coolant & 2 & 0.0185 \\
04 & Burn out & 3 & 0.0278 & 01 & Fracture & 1 & 0.0093 \\
19 & Blockage & 3 & 0.0278 & 09 & Dislocation & 1 & 0.0093 \\
39 & Sensing parts malfunction & 3 & 0.0278 & 15 & Fall off & 1 & 0.0093 \\
30 & Futter & 3 & 0.0278 & 22 & Drift & 1 & 0.0093 \\
31 & Misoperation & 3 & 0.0278 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Frequency table of failure cause of the machining center. Figure 6: Frequency chart of failure causes of machining center. This paper selects engineers who have worked in the company for many years to score. The values obtained from the scoring table are sorted out and analyzed as the input to the pairwise judgment matrix of the failure mode. Any failure mode that has a mutual influence relationship should be compared in pairs. In the Assess/Compare menu of the SD software, you can enter the pairwise judgment matrix. In order to avoid the occurrence of abnormal situations, the judgment matrix needs to be checked for consistency. When the inconsistency coefficient is less than 0.1, the calculation can be continued. This paper takes the cooling system W101 as an example, and the results of the consistency check of the judgment matrix are shown in Table 6. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & \(\overset{\wedge}{\gamma}\) & \(\overset{\wedge}{\theta}\) \\ \hline \(M\) & 1.312 & 2458.100 \\ \(J\) & 1.295 & 2606.440 \\ \(S\) & 0.641 & 7832.664 \\ \(V\) & 0.771 & 7935.696 \\ \(K\) & 0.931 & 5960.776 \\ \(G\) & 0.524 & 27503.400 \\ \(W\) & 0.910 & 4145.720 \\ \(NC\) & 0.542 & 7912.399 \\ \(L\) & 0.791 & 8161.512 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model parameter of failure rate of each component of machining center. Figure 7: ANP model of failure mode of machining center. It can be seen from Table 6 that the inconsistency coefficients are all less than 0.1; thus, the calculation can be continued, and the super limit matrix can be directly obtained by the SD software, as shown in Figure 8.  
It can be seen from Figure 8 that the non-zero value of each row of the super limit matrix is the same, indicating that the matrix has become stable, and the non-zero value is the failure mode influence degree, as shown in Figure 9. In the same way, the failure mode influence value of the remaining components of the machining center can be obtained.