## 3 Results and Discussion
### PdM Assessment of Case Study
#### 3.1.7 Step 7: Prediction Techniques and Priority Index  
The last step of the PdM assessment matrix is to determine the most suitable prediction technique using the priority index. First, prognosis is all about predicting the future behavior of the component under study by estimating its RUL [75]. To determine the best out of several possible prediction techniques, we propose using the priority index shown in Table 5. As seen, the table scores each specific prediction technique in two categories: \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Ability to Diagnose the Maintainable Item(s)**} \\ \hline \multicolumn{3}{c}{**Informativeness of Failure Mechanism(s) and Cause(s) Diagnosed**} \\ \hline Low (1) & Low (1) & Medium (2.5) & High (5) \\ \hline Medium (2) & 2.2 & 5 & 10 \\ \hline High (3) & 3.2 & 7.5 & (Full diagnosis of all maintainable items and failure mechanisms) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Diagnosis priority index. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Detectability**} \\ \hline
**Earliness** & **Low (1)** & **Medium (2.5)** & **High (5)** \\ \hline Low (1) & Low (1) & 2.5 & 5 \\ \hline Medium (2) & 2.2 & 2.2 & 10 \\ \hline High (3) & 3.2 & 7.5 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection priority index. predictive horizon_ and _precision of deterioration prognosis and RUL prediction_. More specifically, these refer to the time horizon within which the specific prognosis technique is able to predict the RUL (week, month, or months ahead), and the associated precision and accuracy of this prognosis and RUL estimate (variation in months, month, or week), respectively. The specific prognosis level is estimated by multiplying the quantitative rankings of the predictive horizon by the precision of deterioration prognosis and RUL prediction. The PdM index at the component level can be estimated as the product of the coverage and levels of detection, diagnosis, and prognosis. Furthermore, underlining the most suitable condition parameters and analyses enables detection, diagnosis, and prognosis. The final PdM assessment matrix covering the entire seven-step process is presented in Figure 1. In this case, the assigned values of the sensor coverage and the levels of detection, diagnosis, and prognosis are based on the literature review (Section 2.1) and the empirical case study data. The final PdM assessment matrix for the case study clearly shows the connection between the coverage indexes of different failure symptoms associated with failure mechanisms and failure modes. It also shows the opportunities and limitations associated with the trending of one monitoring parameter at a time to detect, diagnose, and predict the equipment's state. Finally, the PdM index level demonstrates that the trending of the vibration signal has the ability to enable the detection, diagnosis, and prediction of the failure modes studied: rotor looseness and bearing vibration. The PdM assessment matrix process above is generalized in the following six steps:  
1. Fill the first matrix that includes failure mechanism and failure symptoms. Then, determine the potential symptoms for each failure mechanism. In this case, \(I\) is assigned if the symptom is valid for the failure mechanism, and \(0\) is given if the symptom is not valid. 2. Estimate the coverage index for each failure symptom (sensor) by dividing the sum of failure mechanisms detected by the specific symptom under study by the total number of failure mechanisms associated with the specific failure mode. For example, the symptom of vibration can detect \(11\) out of \(13\) failure mechanisms associated with looseness (failure mode) of rotor (component), which offers a coverage index of \(92\%\) (\(12/13=0.92\)). 3. Estimate the effectiveness of the detection technique, which covers detectability (how accurate) and detection earliness (how early).