## 3 Results and Discussion
### Failure Prediction Scoring Model  
Machine learning and AI are capable of handling significantly larger volumes of information than humans. They excel at identifying and representing hidden relationships within data, making them indispensable tools in modern big data analysis. In this study, a diverse set of data spanning 17 years was also analyzed. The visualization of this data suggested the trends described in the previous sections (trends 1 through 3), which inspired an attempt to construct a predictive model supported by ChatGPT. We can obtain specific weights for each failure factor by providing ChatGPT with the qualitative failure factors (1) through (3) above as prompts. Prior to applying the prompts to unknown cases, we tested them using known failure scenarios to confirm that the AI-generated outputs aligned with expected outcomes. Through this process, we determined prompts that consistently produced realistic and interpretable results. The components of the score are shown in Table 3, and the evaluation criteria are presented in Table 4. Actual operational data were used for scoring and validation to assess the applicability of the AI-suggested weight. The key point for validation is that in predictive maintenance, scoring must not underestimate RBC deterioration. Even if it overestimates, it can be used for predictive maintenance to be on the safe side. With this in mind, we verified whether the threshold detects all failure events in advance using AI-suggested weights. This is possible because thresholds can be determined for each risk level to predict failures once the weights are defined. Figure 6 presents the validation results of the scoring model. The figure consists of two parts: the upper and lower graphs. The upper graph shows the calculated scores based on inspection log records using the criteria defined in Table 3. These scores are labeled as the AI-suggested score and are plotted on the vertical axis. The blue, green, and red shaded areas correspond to the risk levels defined in Table 4. Specifically, the blue area indicates a score of 0-3, representing a low risk of failure in the rotating disk device. The green area represents a medium risk with a score of 4-6, and the red area indicates a high risk with a score of 7 or more. The lower graph reproduces the state transition chart of the rotating disk device as shown in Figure 5. It is included to verify whether the calculated scores in the upper graph effectively capture the occurrence of failures. As shown in Figure 6a, the AI-suggested scores for rotating disk device No. 1 were almost 5 points. The score of 6 points from 18 April 2008 to 26 November 2009 corresponded to periods when abnormal noise was present. In the first failure period (25 February 2010 to 12 November 2010), the second failure (16 January 2012), the third failure period (18 September 2012 to 6 February 2014), and the fourth failure period (10 July 2017 to 17 January \begin{table}
\begin{tabular}{l l l} \hline
**Score Range** & **Risk Level** & **Recommended Action** \\ \hline
0-3 points & Low & No issues; normal operation \\
4-7 points & Middle & Perform inspection as planned \\
7 points or more & High & Prompt inspection and consideration of backup unit startup \\ \hline \end{tabular}
\end{table} 
Table 4: Risk classification criteria based on AI-suggested scoring of daily inspection results. This table presents the interpretation of total scores derived from the scoring model applied to daily inspection records. Based on the aggregated score, the risk of RBC failure is categorized into three levels—low, middle, and high—with corresponding recommendations for inspection and operational response.