# Fault Analysis of Shearer-Cutting Units Driven by Integrated Importance Measure
## 4 Effectiveness Discussion of IIM Ranking
### Relative Value Distribution Analysis by Radial Bar Charts  
The radial bar chart can clearly show the ranking results of importance measures, which can effectively identify the bottom events of high importance. To analyze the ranking and distribution of various IMs, the calculation results of 20 bottom events were normalized, and the ranking of different importance measures is analyzed in the radial bar chart. If the importance of the bottom event is greater, the bottom event is outside the radial bar chart. The radial bar charts of IIM, BIM, CIM, and FVIM are shown in Figures 6-9, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bottom Event** & **IIM** & **BIM** & **CIM** & **FVIM** \\ \hline C1 & 3.99911 \(\times\) 10\({}^{-7}\) & 0.368783 & 0.008214 & 0.022272 \\ C2 & 1.34516 \(\times\) 10\({}^{-7}\) & 0.364217 & 0.001040 & 0.002855 \\ C3 & 2.18133 \(\times\) 10\({}^{-7}\) & 0.363952 & 0.000623 & 0.001713 \\ C4 & 2.18133 \(\times\) 10\({}^{-7}\) & 0.36382 & 0.000416 & 0.001142 \\ C5 & 2.90844 \(\times\) 10\({}^{-7}\) & 0.367969 & 0.006934 & 0.018845 \\ C6 & 7.83712 \(\times\) 10\({}^{-9}\) & 0.006784 & 0.000398 & 0.001093 \\ C7 & 1.59783 \(\times\) 10\({}^{-8}\) & 0.013568 & 0.000398 & 0.001093 \\ C8 & 2.10862 \(\times\) 10\({}^{-7}\) & 0.419421 & 0.087777 & 0.209282 \\ C9 & 1.09067 \(\times\) 10\({}^{-7}\) & 0.374530 & 0.017244 & 0.046042 \\ C10 & 2.54489 \(\times\) 10\({}^{-7}\) & 0.386214 & 0.035601 & 0.092179 \\ C11 & 6.36222 \(\times\) 10\({}^{-8}\) & 0.363829 & 0.000430 & 0.001182 \\ C12 & 7.99822 \(\times\) 10\({}^{-7}\) & 0.383456 & 0.031268 & 0.081544 \\ C13 & 5.45333 \(\times\) 10\({}^{-9}\) & 0.363829 & 0.000430 & 0.001182 \\ C14 & 4.36267 \(\times\) 10\({}^{-8}\) & 0.392528 & 0.045522 & 0.115973 \\ C15 & 4.36266 \(\times\) 10\({}^{-8}\) & 0.383789 & 0.031792 & 0.822837 \\ C16 & 2.90845 \(\times\) 10\({}^{-8}\) & 0.382371 & 0.029563 & 0.077316 \\ C17 & 1.27244 \(\times\) 10\({}^{-6}\) & 0.392528 & 0.045522 & 0.115973 \\ C18 & 9.81600 \(\times\) 10\({}^{-7}\) & 0.423029 & 0.093447 & 0.2208997 \\ C19 & 1.28335 \(\times\) 10\({}^{-6}\) & 0.477737 & 0.179404 & 0.375530 \\ C20 & 1.81778 \(\times\) 10\({}^{-7}\) & 0.367430 & 0.006087 & 0.016567 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of four IMs for 20 bottom events. Figure 8: Radial bar chart of CIM. Figure 6: Radial bar chart of IIM. Figure 7: Radial bar chart of BIM. From these radial bar charts, the IM rankings can be clearly displayed, making it convenient to determine the key fault causes of shearer-cutting units. Figure 6 shows that these four bottom events with the greatest IIM (the top 20% of IIM ranking) are C19 (bearing wear), C17 (nonconformity of oil varieties or improper oil quantity), C18 (cooling spray function failure), and C12 (bolt fracture). Figures 7 and 8 show that the ranking of BIM is the same as that of CIM. The four bottom events with the highest BIM or CIM are C19 (bearing wear), C18 (cooling spray function failure), C8 (short circuit between coils, water in connection cavity, dampened circuit), and C17 (nonconformity of oil varieties or improper oil quantity). Figure 9 shows that the four bottom events with the highest importance of FVIM are fatigue damage, C15 (fatigue damage, gearbox dry friction or particle contamination), C19 (bearing wear), C18 (cooling spray function failure), and C8 (short circuit between coils, water in connection cavity, dampened circuit). Therefore, we can easily find the rankings of different IMs by radial bar charts. From the perspective of the relative importance distribution of four importance measures, in Figure 7, except for C6 (oil seal leakage) and C7 (oil spill blockage), the BIM ranking of other bottom events is very close, and the ranking results of BIM cannot be intuitively distinguished. Comparing Figures 7 and 8, the ranking results of BIM and CIM are the same, but the bottom events of CIM can be distinguished clearly because the gap between maxima and minima is narrow.