# Development of Ground Special Vehicle PHM with Case-Based Reason Model
## 4 Application Validation and Analysis
### Results and Analysis
#### 4.2.1 Analysis on Result of Normal/Abnormal State Determination  
In this case, Table 7 shows state classification accuracy for three CBR methods. It can be seen from the results that the state classification accuracy of the SA-CBR method is the highest, indicating that the SA-CBR method has the highest weight optimization ability. For data sets A1, A4, and A5, the state classification accuracy of CW-CBR is higher than that of EW-CBR, and it has certain weight optimization capabilities. For data sets A2 and A3, the state classification accuracy of CW-CBR are lower than that of EW-CBR. Therefore, the weighting method based on data correlation is not effective for state classification problems. Especially in the condition that the correlation of the data is not significant. Also, the result indicates that although for the same data set, the state classification accuracy of SA-CBR is as low as 74%, it is still higher than the accuracy obtained by the other two methods. Thus, SA-CBR is the first choice. Based on the engineering practice of the application, when the state classification accuracy is less than 70%, the result can only be used as a reference for operators. As we know, a single method is not always good for all circumstances, so we need to consider combining other optimization algorithms to develop more accurate models in the next step.  
Furthermore, ROC diagrams of three weight allocation methods are given, which are especially suitable for the description and analysis of two-class problems [37,38], as shown in Figure 7a-e, respectively. In the above figures, the horizontal coordinate denotes the false positive rate (\(FPR\)), and the vertical coordinate denotes the true positive rate (\(TPR\)). \(FPR\) and \(TPR\) are calculations as follows.  
\[TPR=TP/(TP+FN) \tag{21}\]  
\[FPR=FP/(TN+FP) \tag{22}\]  
where \(TP\) denotes the number of target cases whose result attribute is \"positive\" and are classified as \"positive\". \(TN\) denotes the number of target cases whose result attribute is \"negative\" and are classified as \"negative\". \(FP\) denotes the number of target cases whose result attribute is \"negative\" and are classified as \"positive\". \(FN\) denotes the number of target cases whose result attribute is \"positive\" and are classified as \"negative\".  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subset** & **EW-CBR** & **CW-CBR** & **SA-CBR** \\ \hline A1 & 73.16 & 76.56 & 77.40 \\ A2 & 69.65 & 68.16 & 74.57 \\ A3 & 97.96 & 92.78 & 98.91 \\ A4 & 70.89 & 72.90 & 75.20 \\ A5 & 93.32 & 94.18 & 94.89 \\ \hline \hline \end{tabular}
\end{table}
Table 7: State classification accuracy rate (%).  
Each point in the ROC graph represents a classifier, and the classifier reflects the pros and cons of classification performance. The closer the point is to the upper left corner (\(0,1\)), the better the performance of the corresponding classifier, and the (\(0,1\)) point represents the perfect classifier. It is easy to see from the ROC graphs of five data sets that SA-CBR has the best classification performance.  
Figure 7: Comparison of the ROC diagram.